Housing, credit, and real activity cycles: Characteristics and comovement

Journal of Housing Economics 20 (2011) 210–231

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Journal of Housing Economics

journal homepage: www.elsevier .com/locate / jhec

Housing, credit, and real activity cycles: Characteristics and comovement q

Deniz Igan a, Alain Kabundi b,⇑, Francisco Nadal De Simone c, Marcelo Pinheiro d,Natalia Tamirisa a

a Research Department, International Monetary Fund, 700 19th Street, NW, Washington, DC 20431, USAb Department of Economics, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africac Central Bank of Luxemburg, 42 Rue de Luxembourg, L-3392 Roedgen, Luxemburgd George Mason University, Department of Finance, Fairfax, Virginia 22030, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 9 June 2010Available online 23 July 2011

JEL classification:E32E44F40

Keywords:Macro-financial linkagesHouse pricesCreditBusiness cycle

1051-1377/$ - see front matter � 2011 Elsevier Incdoi:10.1016/j.jhe.2011.07.002

q The material discussed herein may not reflectInternational Monetary Fund or of Central Bank of L⇑ Corresponding author. Fax: +27 11 559 3039.

E-mail addresses: [email protected] (D. Igan)(A. Kabundi), [email protected] ([email protected] (M. Pinheiro), [email protected]

This paper describes the characteristics and comovement of cycles in house prices, residen-tial investment, credit, interest rates, and real activity in advanced economies during thepast 25 years. Stylized facts and regularities are uncovered using a dynamic generalizedfactor model and spectral techniques. House price cycles are found to lead credit and realactivity over the long term, while in the short to medium term the relationship variesacross countries. Interest rates tend to lag other cycles at all time horizons. Although globalfactors are important, the US business cycle, housing cycle and interest rate cycle generallylead the respective cycles in other countries over all time horizons, while the US creditcycle leads mainly over the long term.

� 2011 Elsevier Inc. All rights reserved.

1. Introduction

What do we know about housing cycles, a major sourceof economic activity, and its relationship with credit andoverall economic developments? This question gainedimportance in the aftermath of the global financial crisis.The house price downturn and the resulting defaults in asmall segment of the mortgage market in the United Statestriggered a complex chain reaction that caused the globaleconomy and financial system to stumble, taking manyby surprise and challenging the conventional economicparadigms. As efforts to derive lessons from the crisis

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the opinions of theuxembourg.

, [email protected] De Simone),

g (N. Tamirisa).

and reshape the way we think about modern economiesproceed, it is important to take stock of what the data tellus about the three cycles that form a paramount macro-financial link in the economy – housing, credit and realactivity. However, the three strands of the literature per-taining to the behavior of real estate prices, financial mar-kets, and the real economy have long been rather isolatedfrom each other. In addition, a review of the vast literatureon international business cycles shows, there is lessconsensus regarding the sources of shocks, channels oftransmission, and the degree of synchronization amongcountries’ growth rates of economic activity than there isregarding countries’ levels of economic activity. In Gian-none et al.’s view (2008), this is largely due to poor dataquality, short samples, and lack of robustness regardingdata filtering and statistical methods.

The purpose of this paper is, therefore, twofold: first, toexamine the co-movements in housing aggregates (bylooking at house prices and residential investment), credit

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2 The increased liquidity of housing wealth owing to such regulatoryfactors as the availability of home equity loans or reversed mortgages haveambiguous effects on the relation between house prices and real activity:they can make consumption less dependent on current income, therebystabilizing real activity cycles, but more dependent on asset prices,amplifying business cycle effects (Feldstein, 2007; Leamer, 2007).

3 Changes in interest rates can affect the housing market through variouschannels, including through the direct effect on the user cost of capital,expectations of future house price movements and housing supply, as wellas through indirect wealth effects from house prices, balance sheet andcredit channel effects on consumer spending and housing demand. Thestrength of these transmission channels is likely to depend on institutionaland regulatory factors pertaining to the housing market.

4 Most central banks consider developments in monetary and creditaggregates and asset prices when making interest rate decisions, and theEuropean Central Bank and the Bank of Japan include credit variables as

D. Igan et al. / Journal of Housing Economics 20 (2011) 210–231 211

markets (by looking at interest rates and bank credit to theprivate sector), and business cycles within countries andinternationally (by looking at real GDP). Second, inspiredby Giannone et al.’s observation, this study puts extremecare in the collection of high-quality data and in the choiceof the statistical procedures used to filter the data. Dispar-ities of opinion regarding growth rates business cycles rel-ative to levels business cycles are a sober reminder of thepoor statistical properties of first differencing and thus,in dealing with nonstationary data, this research followsan alternative statistical approach.

This paper compiles and discusses stylized facts on thecharacteristics and comovements of cycles in house prices,residential investment, bank credit to the private sector,interest rates, and real activity in advanced economies dur-ing the past 25 years. The focus is on two issues:

(1) How closely has the cyclical behavior of houseprices, residential investment, bank credit and realeconomic activity been synchronized over differenttime horizons within countries? Are these cyclicalpatterns consistent with the regularities predictedby modern financial accelerator theories? How dothey relate to interest rate cycles?

(2) How closely has the cyclical behavior of houseprices, residential investment, bank credit and realeconomic activity been synchronized across coun-tries? Is there evidence of some countries’ cyclesleading other countries’ cycles?

Both questions have been at the heart of discussions,triggered by the recent crisis, on how shocks are transmit-ted across the financial systems and real economies withinand across borders and how financial, monetary and super-visory frameworks need to be modified to minimize theoccurrence of crises and prevent their rapid propagation.A proper understanding of the domestic and internationalregularities of the three cycles in question is critical in thiscontext.

On the first question, the business cycle literaturepoints to a high degree of comovement in house prices,residential investment, bank credit and real activity inthe domestic economy (see, for example, Stock andWatson, 1999). Bank credit and house prices typically raiseduring economic upswings, as firms and consumers de-mand more credit to expand investment and consumption;and during downturns these trends reverse. The financialaccelerator theory suggests that financial cycles are likelyto have a larger amplitude than real activity cycles and thatthe financial accelerator effects tend to amplify real eco-nomic cycles owing to the procyclicality of bank lending.Such procyclicality arises because changes in asset pricesaffect the external finance premium (Bernanke and Gertler,1989), the value of collateral (Kiyotaki and Moore, 1997)1

1 Note that the link between credit and house prices works in bothdirections: the ease in credit constraints increases demand for housing andpushes house prices up, while rising house prices and collateral valuesimprove the perceived creditworthiness of borrowers and enable them toborrow more.

and bank leverage (Adrian and Shin, 2008; Berger and Bouw-man, 2009).2

One would also expect a high degree of synchronizationbetween the three cycles in question and the interest ratecycles, with interest rates being contemporaneous withother cycles or lagging them (to the extent that houseprices reflect agents’ interest rate expectations). Output,and to a lesser extent house prices and credit, tend to re-spond to interest rate shocks (Bernanke and Gertler,1995; Mishkin, 2007; Assenmacher-Wesche and Gerlach,2010).3 However, central banks in advanced economies havenot traditionally targeted house prices and credit, as mone-tary policy is considered to be an ineffective tool for thatobjective (Bernanke et al., 1999).4

Evidence on international comovement in the three cy-cles in question is dominated by the vast literature oninternational business cycles. Helbling and Bayoumi(2003) find little comovement between G-7 cycles in the1973–2001 period, and while they also find correlationsto be unstable, recessions tend to be relatively more corre-lated. However, Bordo and Helbling (2003), using a longersample, 1880–2001, find that the degree of comovementamong industrialized countries has increased over time.Artis (2003) concludes that there is no comovement amongEuropean economies that suggests a European cycle, incontrast to Lumsdaine and Prasad (2003), who find a Euro-pean cycle together with evidence of a world cycle for agroup of 17 OECD countries between 1963 and 1994. Sim-ilarly, Nadal De Simone (2002) finds that while there is agreat deal of comovement among Germany, France, Italy,the rest of the Euro area and the US levels of real GDP inthe period 1975–2001, there is also a European component– more relevant in downturns than in upswings – as wellas national idiosyncrasies.

Stressing the question of the source of the observedcomovement,5 Canova et al. (2007) find no evidence of aEuropean cycle or of its emergence in the late 1990s – be-sides an increase in synchronicity in the late 1990s – butof a significant world component. Imbs (2004), and Kose

separate ‘‘pillars’’ of monetary policy. Some authors argued, instead, for aproactive role of central banks in ‘‘leaning against the wind’’ of changes incredit growth and asset price increases, particularly forcefully raisinginterest rates to prevent bubbles from reaching unsustainable proportions(Borio and Lowe, 2002; Borio, 2006).

5 In what follows, no attempt is made as distinguishing between‘‘comovement’’ and ‘‘synchronization’’ of economic activity acrosscountries.

7 The short to medium term is defined here as 6–16 quarters, in line with

212 D. Igan et al. / Journal of Housing Economics 20 (2011) 210–231

et al. (2003, 2008), find an important world component inEuropean business cycles.6 Yet, the world component ex-plains a larger variation of output during the period1986:3 to 2003:4 than in the Bretton Woods period, but lessthan in the period 1972:3–1986:2, except for France andItaly. In contrast, Del Negro and Otrok (2008) find no changeof cross-country correlations in the EU during 1970–2005,but strong links between the EU and the US cycles.

The role of the United States as the source of the worldcycle is also controversial. However, a careful reading ofthe recent empirical literature suggests that whether hav-ing originated in the United States or globally, the shock ef-fects are felt in the United States first, and then in Europe;it is the slower propagation mechanism that has a strictlyEuropean imprint. Giannone et al. (2005) and Giannoneand Reichlin (2006), using a sample of 18 countries withdata from 1970 to 2005, find that EU economic fluctuationsare world-originated or US-originated, and that Europeanheterogeneities are explained by national components thatare quite persistent, but of much less importance. Studyingthe transmission of business cycles from the United Statesto Germany, Eickmeier (2007) and Kabundi and Nadal DeSimone (2007, 2010) suggest that the world cycle is theUS cycle. In contrast, Crucini et al. (2008) report finding aworld (G-7) cycle, which is not coming from a US shockin the period 1960–2005.

Few studies move beyond the topic of business cycles’comovement and examine multi-dimensional relation-ships among macroeconomic and financial variables inthe international context. Using a factor-augmented vectorautoregression (FAVAR) model, Otrok and Terrones (2007)find a high degree of comovement in house prices in ad-vanced economies and attribute it to the effects of the USmonetary policy shocks. In a fixed-effects panel VAR,Goodhart and Hofmann (2008) identify a multi-directionalrelationship among house prices, monetary aggregates andreal activity indicators. In contrast to these two model-based studies on international dynamics of macroeco-nomic and financial indicators, this paper focuses ondocumenting the stylized facts pertaining to the three cy-cles in the domestic and international context. In doing so,the importance of data transformation techniques that donot alter the data-generating process is stressed, mindfulof Giannone et al.’s (2010) point about the sensitivity offindings to alternative data filtering and statistical methods.

The methodology used in this paper reflects recent ad-vances in the econometrics of macroeconomic and finan-cial cycles. Data stationarity is tested using the mostrobust unit-roots tests available and, when necessary, non-stationary series are made stationary using Corbae andOuliaris (2006) ideal band-pass filter. Next, a generalizeddynamic factor model (Giannone et al., 2002; Forni et al.,2009; Eickmeier, 2007) is applied to extract the commoncomponents of time series. Data comprise a broad rangeof economic and financial indicators for 18 advanced econ-omies for the period from 1981:Q1 to 2006:Q4. The data-base covers real activity indicators, credit aggregates,

6 There is some conflicting evidence of lower synchronization, possiblyreflecting increased economic specialization (Kose and Yi, 2006; Kose et al.,2003).

house prices, residential investment, short- and long-terminterest rates, and household wealth. The degree of comov-ement between cycles is measured using dynamic correla-tions (Croux et al., 2001) and coherence and phase-anglestatistics. Leads and lags between cycles are identified onthe basis of statistical testing.

The findings of the paper can be summarized as follows:

(1) Over the short to medium term, the lead–lag rela-tionships between housing, credit and real activitycycles tend to vary across countries, possibly owingto institutional differences that affect the financialaccelerator mechanism.7 However, there seems tobe a tendency for residential investment to lead houseprices, and for house prices and credit to lead or tomove in tandem with interest rates. Over the longterm, house prices clearly lead credit and real activityin all countries. Long-term movements in houseprices may be driven by slow changing fundamentals(for example, demographics and zoning regulations),which in turn drive demand for credit and real activ-ity. Also over the long term, real output and houseprices, and, to a lesser extent credit, tend to lead inter-est rates.

(2) Country cycles in housing, credit and real activityare largely driven by common factors, and the roleof such factors appears to have increased over time,possibly owing to growing financial integration. TheUS cycles in real activity, house prices, residentialinvestment, and interest rates tend to lead othercountries’ respective cycles over all time horizons,while the US credit cycle leads only in the longrun. This finding points to significant spillovers fromthe United States to the rest of the world, underlin-ing the need to take into account not only domesticbut also global trends in house prices and creditwhen analyzing the economic outlook.

The paper contributes to the literature in several ways.It is one of the few studies that carefully documents thecharacteristics and comovement of the three cycles simul-taneously using a sizeable set of macroeconomic andfinancial variables for a large number of countries over along period of time.8 The distinguishing feature from theexisting studies of cycles in the international context is thatthe paper enhances knowledge about the characteristics ofhousing and credit cycles, which have been much less stud-ied than business cycles. The paper is also among the first touse sophisticated statistical techniques to analyze these cy-cles, paying particular attention to the implications of thenonstationary data treatment on the results. The compila-tion of stylized facts in this paper can be seen as a necessaryinitial step toward a more systematic, model-based analysisof macro-financial linkages in the global economy.

the National Bureau of Economic Research (NBER)’s definition of the‘‘minor’’ cycle. The long term is defined here as 16–32 quarters, or theNBER’s ‘‘major’’ cycle.

8 Claessens et al. (2009) is close in its coverage but they focus on housingboom-busts and credit crunches around business cycle troughs.

10 Similar models have recently been used by Giannone et al. (2002), Forniet al. (2009), Eickmeier (2007), and Kabundi and Nadal De Simone (2007).

11 In general factors are I(0), given that they are extracted from I(0) series


The rest of the paper is organized as follows. Section 2discusses methodology. Section 3 reviews data and datatransformations. Section 4 presents results. Section 5concludes.

2. Methodology

The descriptive analysis comprises three aspects: (i)characteristics of the cyclical movements, (ii) their break-down into common and idiosyncratic components, and(iii) depiction of the comovement among cycles within acountry and among cycles across countries. The methodol-ogy used to analyze each aspect is described in detail inseparate sub-sections.

2.1. Characteristics of cycles

There is a large amount of research focusing simply onthe definition and measurement of economic cycles, andseveral approaches have been used in the literature. Here,we use the classical definition of the business cycle basedon the turning points in the (log-)level of aggregate eco-nomic activity (Burns and Mitchell, 1946) to date the cyclesin the variables of interest. The algorithm originally sug-gested by Bry and Boschan (1971), further developed byHarding and Pagan (2002), and which operationalizes theoriginal approach in Burns and Mitchell (1946), locatesturning points in cycles. The algorithm searches for maximaand minima over a given period of time and defines a peak(trough) at time t as occurring when the series yt > (<) yt+2,ensuring that peaks and troughs alternate. It also imposes arestriction that a cycle phase, the contraction (from peak totrough) or the expansion (from trough to peak), lasts atleast two quarters and a complete cycle (from one peak tothe next) lasts five quarters at a minimum.

In addition to dating cycles, the two main characteris-tics of cyclical phases, namely, duration and amplitude,are identified (Harding and Pagan, 2002). The duration ofa contraction (expansion) phase is the number of quartersbetween a peak (trough) to the next trough (peak). Theamplitude of a contraction (expansion) phase is the changein the series of interest from a peak (trough) to the nexttrough (peak).

The algorithm is consistent with the methodology usedby the NBER and the CEPR to date business cycles in theUnited States and the Euro area, respectively. We applythe same methodology to date housing and credit cycles.

2.2. Common versus idiosyncratic components

To disentangle the common from the idiosyncratic com-ponents of the variables of interest, a large-dimensionalapproximate generalized dynamic factor model (GDFM)is used. This approach is preferred to wavelet analysis asit is more robust in the context of a data base that containsa number of time series observations which is relativelysmall compared to the number of series.9 The model isclosely related to the traditional factor models of Sargent

9 See Forni et al. (2000) for a justification of this method in that sort ofenvironment.

and Sims (1977) and Geweke (1977), except that it allowsfor the possibility of serial correlation and weakly cross-sectional correlation of idiosyncratic components, as inChamberlain (1983) and Chamberlain and Rothschild(1983).10

The approximate dynamic factor model analysis focuseson identifying a common component using a large numberof series. A vector of time series Yt ¼ ðy1t ; y2t ; . . . ; yNtÞ

0 canbe represented as the sum of two latent components, acommon component Xt ¼ ðx1t ; x2t ; . . . ; xNtÞ0, which is dri-ven by a small number of shocks that are common to theentire panel, and an idiosyncratic component Et ¼ ðe1t ;

e2t ; . . . ; eNtÞ0, which is specific to a particular series andorthogonal to the common component. Hence,

Yt ¼ Xt þ Et

Yt ¼ CFt þ Et:ð1Þ

where Ft ¼ ðf1t ; f 2t ; . . . ; f rtÞ0 is a vector of r common factors

and C ¼ ðc01; c02; . . . ; c0NÞ0 is an N � r matrix of factor load-

ings, with r� N. The common component Xt , which is alinear combination of common factors, is driven by a lim-ited number of common shocks, which are the same forall variables. Nevertheless, the effects of the commonshocks differ from one variable to another and from onecountry to another due to different factor loadings. In thisframework and in contrast to standard common compo-nent analysis, the idiosyncratic component is driven by idi-osyncratic shocks, which are specific to each variable andcountry. The dynamic factor model used here differs fromthe static factor model in that it treats lagged or dynamicfactors Ft as additional static factors. Thus, common factorsinclude both lagged and contemporaneous factors.

Stock and Watson (1998) demonstrate that the idiosyn-cratic component, which is weakly correlated by construc-tion, vanishes through application of the law of largenumbers (as T, N ?1); and therefore, the common com-ponent can be easily estimated in a consistent manner byusing standard principal component analysis. The first reigenvalues and eigenvectors are calculated from the vari-ance–covariance matrix, covðYtÞ:

Xt ¼ VV 0Yt ; ð2Þ

and since the factor loadings C = V, Eq. (1) becomes

Ft ¼ V 0Yt : ð3Þ

From (1), the idiosyncratic component is

Et ¼ Yt � Xt : ð4Þ

From all the more or less formal criteria to determinethe number of static factors r, Bai and Ng (2002) informa-tion criteria are selected for use in this study. As in Forniet al. (2009), Ft is approximated by an autoregressive rep-resentation of order 111:

(or series that have been made stationary). Hence, if one wants to havefactors that are dynamic, they have to be estimated from an AR process;AR(1) processes have been found to be appropriate in most empiricalstudies.

13 The short- and long-term interest rates are left in nominal terms on thegrounds that money illusion may be an important factor in determining thenature of the cyclical movements and linkages among cycles. Brunnermeierand Julliard (2008), for instance, show that nominal house prices are


Ft ¼ BFt�1 þ ut; ð5Þ

where B is an r � r matrix and ut an r � t vector of residu-als. Eq. (5) is the reduced form model of the common com-ponent in Eq. (1).

2.3. Measures of comovement

Measures of dynamic correlation, coherence, and phaseangle are used to evaluate the structure of the comove-ments among the series of interest, including leads andlags. The dynamic correlation between two stochastic pro-cesses is the correlation coefficient between the real part oftheir spectral decomposition (see Croux et al., 2001; Fuller,1976, for technical details). The dynamic correlation variesbetween �1 and +1. Formally,

qy1y2¼ Cy1y2

ðkÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiSy1ðkÞSy2

ðkÞp ;

where Cy1y2 ðkÞ is the cospectrum between y1 and y2 pro-cesses at frequency k and Sy1

and Sy2are the spectral den-

sity functions of the processes at frequency k defined over�p and p.

Coherence is intrinsically related to the dynamic corre-lation and is given by

Ky1y2ðkÞ ¼ jSy1y2

ðkÞj2

Sy1ðkÞSy2

ðkÞ :

The coherence is symmetric and a real number between0 and 1. It does not measure correlation at different fre-quencies. It disregards the phase angle shifts between thevariables, and can thus be interpreted as the R2 from theregression of y1 on y2.

The phase angle between processes y1 and y2 helpsidentify the lead–lag relationship and is given by

uy1y2ðkÞ ¼ tan�1ðqy1y2

jCy1y2Þ;

where qy1y2is the quadrature spectrum. Only when

Ky1y2ðkÞ–0, the phase angle converges in distribution to a

normal random variable. When the coherence is signifi-cant, it is possible to construct confidence intervals forthe lead and lag relations between the two processes.

3. Data and data transformations

3.1. Data

The database comprises a set of quarterly macroeco-nomic and financial series for 18 advanced countries forthe period from 1981:Q1 to 2006:Q4.12 The list of countriesand the period covered have been largely determined bythe objective of having a complete database of uniform,good quality. The data cover indicators of real activity,including consumption, investment (broken down into res-idential and non-residential), international trade in goodsand services; confidence indicators, international portfolio

12 The countries included in the analysis are Australia, Austria, Belgium,Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, theNetherlands, New Zealand, Norway, Spain, Switzerland, the UnitedKingdom, and the United States.

and direct investment flows, consumer prices, as well asfinancial variables, such as bank credit to the private sec-tor, house prices, stock prices, monetary aggregates, andshort- and long-term interest rates.13 Also included areselected balance-sheet data, such as household wealth.Besides national variables, the data set includes selectedglobal variables, such as crude oil prices, commodity priceindex for industrial inputs, world demand, and world re-serves. Most of the data series are from the Organizationfor Economic Cooperation and Development (OECD) data-base, and a few come from national and other internationalsources. For a complete list of the data used in the analysis,as well as the data sources, see Appendix.

3.2. Unit root tests

For the purposes of the GDFM analysis, data need to becovariance stationary. After removing the seasonal compo-nent,14 the degree of integration of data series should bedetermined. As is well known in the unit root testing litera-ture, unit root tests have low power against the alternativeof a deterministic trend, and results are sensitive to thespecification of the unit root tests. The two tests with thehighest power available in the literature of unit root testingare used in this study: the ERS (Elliott et al., 1996) unit roottest and the KPSS test (Kwiatkowski et al., 1992) unit roottest. The ERS test is a generalized least squares unit root test,which is more powerful than standard Dickey–Fuller tests.The KPSS test provides a robust cross-check on the ERS test,as it uses a different null hypothesis, stationarity, instead ofnonstationarity, as in the ERS test. The unit root tests con-ducted include a constant and a deterministic trend.15 Thenumber of lags is chosen using the Schwarz informationcriterion and ensuring that no serial correlation is left inthe residuals. The results of unit root tests are presentedon Table 1.

A striking finding is that real house price series for anumber of countries (including France, Ireland, the Nether-lands, New Zealand, Norway, and the United States) arefound to be I(2). Credit series are also I(2) for a numberof countries, including, notably, Japan and Spain. The de-gree of integration of the series has implications for mod-eling, forecasting and for policy analysis. For example, if ahouse price series is I(2), first-differencing it and using ittogether with other first-differenced series for which thetrue data generating process (DGP) is I(1) will render spu-rious results. In contrast, second-differencing a series con-sidered to be I(2), but for which the true DGP is I(1) willresult in over-differencing and will weaken the analysis.From a policy perspective, an I(2) house price series

typically boosted when inflation declines.14 X-12 was used for removing the seasonal component of series. Data are

in logs except interest rates.15 As most time series trend, a trend was included in the null hypothesis.

However, in case of doubt, the order-of-integration analysis was also doneexcluding the trend and or the constant from the null hypothesis.

Table 1Unit root tests.

Source: Authors’ estimates.Notes: The table reports the results of two unit root tests: ERS (Elliott et al., 1996) and KPSS (Kwiatkowski et al., 1992). All tests were done including aconstant and a trend. The number of lags was chosen using the Schwarz criterion and ensuring that no serial correlation is left in the residuals. Highlightingin the table identifies the cases where the two tests rendered conflicting results. In such cases, unit root tests were also done excluding a constant and trend,and graphical evidence was examined particularly closely. Output is the real GDP as calculated by the IMF’s International Financial Statistics, house prices areexpressed in real terms by deflating the nominal house price indices by CPI, credit is bank credit to the private sector deflated by CPI.a The charts do suggest that stationarity is only achieved at the specified differenced series.b The ERS test barely passes the confidence level.c Schwarz criterion suggests taking 3 lags and there is no SC either with 1 or 2 lags. Three lags suggest I(1).d The KPSS test barely passes the confidence level. In addition, observation of the series suggests that it contains a unit root.

(Y axis: spectrum; X axis: periodicities in quarters)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Infinite64,032,021,316,012,810,7 9,1 8,0 7,1 6,4 5,8 5,3 4,9 4,6 4,3 4,0 3,8 3,6 3,4 3,2 3,0 2,9 2,8 2,7 2,6 2,5 2,4 2,3 2,2 2,1 2,1

0

0.00005

0.0001

0.00015

0.0002

0.00025

GDP Ideal Band Pass, Corbae-Ouliaris Filter GDP First Differenced (RHS)

Fig. 1. Filtered versus differenced series.



implies that shocks to house price changes have a lastingeffect. A shock such as a natural catastrophe or a hardeningof housing supply constraints introduced by tightening ofzoning regulations, for example, given housing demand,may have a lasting effect on the rate of house pricechanges. If this feature is ignored in a model, a persistentgrowth rate of house prices might be interpreted as a mis-alignment of house prices by a model that considers thatthe above shocks only have lasting effects on the level ofhouse prices and not on the growth rate of house pricesas well.16 In other words, wrong diagnostics about the de-gree of integration of a time series may bias the allocationof variance between trend (equilibrium) and cycle, confusepersistent shocks in rates of growth with misalignment,and thus lead to wrong policy prescriptions.

19 As stated above, Giannone et al. (2010) note that while there is a broad

3.3. The ideal band-pass filter

The theory of filtering has a long tradition in time seriesanalysis.17 A key and well-established result is that the dif-ference operator is a high-pass filter (with properties similarto those of the derivative) that has a gain function ‘‘thatdeviates substantially from the squared gain function of anideal high-pass filter.’’18 The popularity of first-differencingI(1) series in applied econometrics is, therefore, somewhatsurprising. The wide application of the Hodrick–Prescott fil-ter to I(1) series is similarly surprising given that the filteraffects the DGP of the series if an inappropriate smoothingconstant is used. Empirical work often uses 1600 and 100as values for the smoothing constant for quarterly andannual time series, respectively, i.e., the values used byHodrick and Prescott to respect US series DGP (Harvey andTrimbur, 2008). The suitability of those values to series otherthat the ones studied by Hodrick and Prescott should betested rather than assumed. The use of wrong values forthe smoothing constant in empirical work distorts the allo-cation of variance between trend and cycles affecting there-by cycle analysis.

This paper uses the Corbae–Ouliaris ideal band-pass fil-ter (Corbae and Ouliaris, 2006) to isolate the cyclical com-ponent in the data. A band-pass filter is nothing but asequential combination of a high- and a low-pass filter thatremoves or attenuates all but a band of frequencies fromthe input series. The filter passes through the componentsof the time series levels with periodic fluctuations between6 and 32 quarters – in line with the original specification ofBurns and Mitchell (1946) – while removing componentsat other, higher or lower, frequencies.

Assume that Xt is an I(1) process with DXt ¼ v t suchthat v t has a Wold representation. The spectral density ofv t is fvvðkÞ > 0, for all k.

16 In New Zealand, for example, during the mid-1990s, the Reserve Bankof New Zealand was concerned about the possible ‘‘misalignment’’ of realhouse prices and viewed the persistent growth of house prices as anunsustainable development. If house prices are I(2), however, thosedevelopments need not be unsustainable.

17 See for instance, the seminal work by Koopmans (1974), chapters 4 and6, Harvey (1989), Harvey and Jaeger (1993), Hodrick and Prescott (1997),Harvey and Trimbur (2008).

18 Koopmans (1974, p. 170).

The discrete Fourier transform of Xt for kt–0 is

wXðksÞ ¼1

1� eikswvðksÞ �

eiks

1� eiks

ðXn � X0Þn1=2 ;

where ks ¼ 2psn , s = 0, 1, . . . , n � 1, are the fundamental fre-

quencies. The second term makes it clear that the Fouriertransform is not asymptotically independent across funda-mental frequencies because the second term is a determin-istic trend in the frequency domain with a randomcoefficient of ðXn�X0Þ

n1=2 . Unless that term is removed, it willproduce leakages into all frequencies kt–0, even in the lim-it as n!1. Sacrificing a single observation, instead of esti-mating the random coefficient à la Hannan (1970), Corbaeand Ouliaris (2006) show that by imposing that ðXn � X1Þ ¼ðXn � X0Þ will produce an estimate that has no finitesampling error, has superior end-point properties, andhas much lower mean-squared error than popular time-domain filters such as Hodrick–Prescott or Baxter–King.In addition, in contrast to Baxter–King, it is consistent.

The filtering approach followed has an importantadvantage for the objective of our analysis, as in contrastto alternative ways of obtaining covariance-stationary data(such as differencing for I(1) or I(2) series or detrending forI(0) series with a deterministic trend), it does not removethe portion of the variance that is relevant for the businesscycle analysis (see Harvey and Jaeger, 1993, for an illustra-tion of how first differencing affects a series’ data genera-tion process).19 As an illustration of this point, Fig. 1displays the spectra of the US real GDP – which contains aunit root according to both ERS and KPSS tests – after filter-ing the series using Corbae–Ouilaris and after filtering theseries by first differencing it. Notice the sizeable loss of var-iance in the frequency of interest – the business cycle fre-quency – that results from filtering via first differencing,even though, according to the unit root tests, the series isnonstationary and first differencing it is what is often donein this case to render it stationary.

Therefore, while carefully analyzing the statistical prop-erties of the time series, analysis of the characteristics ofthe business cycles concentrates on the series madestationary using the Corbae–Ouliaris filter. How data aretreated prior to the analysis of cyclical behavior and com-ovement in the series – whether series are filtered by firstdifferencing or filtered by Corbae–Ouliaris – has bearing onthe conclusions of such analysis. For example, and to illus-trate the impact of the type of filtering in isolating thebusiness cycle frequency mass of time series, the share ofthe common variance in the total variance of a serieswould indicate how important common explanatory forcesare in its behavior. Notice that filtering by first- and

consensus on the synchronization of recessions and expansions on the basisof data on the level of economic activity, there is not at all agreement on the‘‘facts’’ on growth cycles, i.e., filtered data capturing some longer movingaverage of growth rates. They argue that one important source of surprisingdifferences on descriptive statistics is ‘‘lack of robustness with respect todata filtering and statistical methods.’’ The conjecture here is thatdifferences in results across studies in growth rates and not in levels arepartly due to the distortions introduced to series data generating processesby filters often used in empirical work. Further analyzing this issue is,however, beyond the scope of this paper. Comin and Gertler (2006) alsodiscuss the role of filtering in the literature on economic cycles.

Table 2Variance shares.

Differenced series Filtered series

Average 0.07 0.31Maximum 0.18 0.70Minimum 0.01 0.01Standard deviation 0.04 0.17Coefficient of variation 0.62 0.55Variance share exceeding 20% 0.03 0.68

Source: Authors’ estimates.Notes: The table reports the variance shares of common components fortwo alternative ways of data treatment – first differencing and filteringusing the ideal band pass filter. The series included in this illustrativeexercise are output, house prices, credit, short- and long-term interestrates. The last row shows the number of series, in percent of the totalnumber of series, for which the variance share explained by the commoncomponents of the included series exceeds 20%.

20 This is further elaborated below.21 Residential investment series are not shown in Fig. 2 to make it easier

to read. These series move closely with house prices with an averagecorrelation coefficient of 0.50 across countries. Thecommon components ofthese two series characterizing housing cycles move even more closely; see


second-differencing results in only 3% of all the series dis-playing a common variance share larger than 20%. Whenthe same series are filtered instead using Corbae–Ouliaris,68% of them have a variance share larger than 20%. Table 2summarizes the variance share that is attributable to com-mon components for the series of interest.

Clearly, data transformations have not only statistical,but also policy relevance. For instance, a wrong data trans-formation process may introduce a downward bias in thedegree of comovement between series and the degree ofestimated economic integration in structural modelsembedding those series. As a corollary, wrong data trans-formation may introduce an upward bias in the efficacyand efficiency of uncoordinated macroeconomic policies.To illustrate, when Dutch house prices are filtered via sec-ond differencing (recall that the series is found to be I(2)),the common variance share is 8 percent; when they are fil-tered using Corbae–Ouliaris, the common variance sharebecomes 16%. From a policy viewpoint, the correct treat-ment of the series is important because what may beviewed as a disequilibrium in the housing market – forexample, an overvaluation – may simply be a statisticalartifact. In addition, and importantly for current discus-sions about ‘‘leaning against the wind’’, since statisticalproperties of house price series differ across countriesand, in some countries, as implied by the degree of integra-tion of the series, shocks to house price inflation are persis-tent, erroneous data treatment could complicate thestabilization of house price inflation if it were to becomethe central bank’s objective.

In sum, the ideal band-pass Corbae–Ouliaris filter, byminimizing distortions to the data generating processes,and in particular by retaining more mass under the spectraat the traditional business cycle and longer periodicities,should allow cycles intrinsic characteristics to be describedin a more robust fashion.

Appendix Figure for an illustration of this point using US and UK data.22 For example, the possibility of mortgage equity withdrawal and

refinancing is likely to fasten and strengthen the transmission of houseprice shocks to household consumption and bear on the monetarytransmission mechanism, with changes in interest rates having a strongereffect on households’ cash flow, consumption and output. Differences in theprice elasticity of housing supply may also be contributing to differences inthe amplitudes of cycles. If the supply elasticity is low, house prices wouldtend to respond strongly to changes in interest rates, with knock-on effectson wealth and consumption.

4. Results

The discussion of the empirical results focuses on thedegree of commonality in the cyclical behavior of housing,credit and real economic activity within and across coun-tries and over time. When presenting the results, the char-acteristics of the three cycles and comovement among

them in the domestic context are first discussed. Next,the international comovement of cycles, the importanceof common factors in driving them, as well as the role ofthe United States in leading housing, credit and businesscycles in other advanced economies are studied. Whilethe results are descriptive and stylized, to the extent possi-ble, they are related to the pertinent strands of literatureand their implications as surveyed in Section 1.

4.1. Domestic cycles

4.1.1. CharacteristicsDuration and amplitude of the three cycles, by country,

are presented in Table 3. These characteristics are com-puted using the Bry–Boschan methodology a.k.a. ‘‘theBBQ algorithm’’ (Q standing for the fact that the algorithmis applied to quarterly time series) described in Section 2.1.A typical business cycle consists of a contraction phase of 6quarters and an expansion phase of 8 quarters. A similarpattern emerges for the typical housing cycle, while thetypical credit cycle has a longer upturn phase.

We find a high degree of comovement in credit, real houseprices, and real activity within countries.20 Fig. 2 displays thecyclical portion – the sum of common and idiosyncratic com-ponents – of these series.21 The peaks and troughs of the threecycles often coincide, consistent with the predictions of theliterature on business cycles and financial accelerators re-viewed in Section 1, and particularly the view that busi-ness cycles are closely related to housing cycles (forexample, Leamer, 2007), which in this study evinces clearlyas the duration of real house price cycles is not signifi-cantly different from the duration of real activity cycles.

However, there are also notable differences in the char-acteristics of cycles across countries. For instance, Irishhouse prices appear to be relatively more volatile whileSwiss housing markets seem to have less fluctuation. Sim-ilarly, in terms of duration, Canada, New Zealand, andSwitzerland emerge as countries with asymmetric housingcycles with the expansion phase lasting twice as long asthe contraction phase. Such cyclical differences may relateto differences in the structure of countries’ financial sys-tems and housing markets such as the share of mortgagedebt, owner-occupation rates, and the pervasiveness ofvariable rate mortgages (Table 4).22 A systematic testingof these hypotheses is beyond the scope of the present styl-ized facts analysis, and would be an interesting topic for fu-ture research.

Table 3Cycle characteristics.

Peak-to-trough Trough-to-peak Peak-to-trough Trough-to-peak Peak-to-trough Trough-to-peak Peak-to-trough Trough-to-peak

Duration Amplitude Duration Amplitude Duration Amplitude Duration Amplitude Duration Amplitude Duration Amplitude Duration Amplitude Duration AmplitudeOutput House prices Residential investment Credit

Australia 5 �0.72 7 0.72 6 �0.67 5 0.65 7 �1.08 5 0.86 7 �0.94 5 0.88Austria 6 �0.80 7 0.84 5 �0.76 8 0.74 5 �1.03 8 0.98 6 �0.66 8 0.87Belgium 6 �0.95 10 1.02 6 �1.10 9 1.40 5 �0.68 12 1.04 6 �0.86 6 0.80Canada 6 �0.83 9 0.72 5 �0.67 10 0.66 5 �0.82 7 0.74 7 �1.16 8 1.28Denmark 7 �1.14 6 1.18 6 �1.13 7 1.06 6 �0.83 5 0.64 5 �0.61 7 0.58Finland 7 �1.50 8 1.32 9 �1.24 6 1.28 6 �0.96 6 1.03 6 �1.07 7 0.97France 5 �0.93 10 1.06 6 �0.83 7 0.93 6 �0.80 8 1.06 8 �1.21 8 1.20Germany 5 �0.84 7 0.96 6 �1.04 7 1.11 5 �0.66 5 0.54 7 �0.62 13 0.68Ireland 7 �1.11 6 1.15 8 �1.55 7 1.45 5 �0.72 6 0.77 5 �0.85 10 1.11Italy 5 �0.66 7 0.59 8 �1.37 9 1.47 7 �1.05 8 1.07 7 �1.19 9 1.30Japan 5 �0.82 9 0.85 6 �1.06 7 0.61 4 �0.56 7 0.48 5 �1.41 9 1.28Netherlands 7 �0.93 9 0.88 6 �0.89 5 0.76 7 �1.03 8 1.07 7 �1.09 8 1.15New Zealand 6 �0.57 6 0.57 5 �1.02 10 1.20 5 �0.64 7 0.59 6 �0.90 6 0.83Norway 7 �0.73 11 0.39 6 �0.86 6 0.83 5 �0.75 8 0.70 7 �0.57 14 0.93Spain 6 �1.07 11 1.18 7 �1.10 8 0.94 4 �0.79 5 0.84 8 �1.17 8 1.04Switzerland 5 �1.03 6 1.05 4 �0.55 8 0.55 6 �0.63 7 1.10 3 �0.58 37 1.35United Kingdom 5 �0.62 9 0.85 6 �1.04 8 0.82 6 �1.11 6 1.03 6 �1.33 9 1.15United States 5 �1.03 7 0.64 7 �0.62 6 0.93 4 �0.83 14 0.93 4 �0.63 6 0.44Mean 6 �0.91 8 0.89 6 �0.97 7 0.97 5 �0.75 7 0.82 6 �0.94 10 0.99Median 6 �0.88 8 0.87 6 �1.03 7 0.93 5 �0.79 7 0.86 6 �0.92 8 1.00Standard deviation 1 0.21 2 0.24 1 0.25 1 0.28 1 0.30 2 0.28 1 0.26 7 0.25

Source: Authors’ estimates.Notes: The table reports the average duration and amplitude of the cyclical component (the sum of the common and idiosyncratic components) for output (real GDP), house prices, residential investment, andcredit, by country. Duration is defined as the number of quarters from peak (trough) to trough (peak). Amplitude, expressed in percent, is the contraction (expansion) from peak (trough) to trough (peak).

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Australia

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

GDP Credit House prices

Austria

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Belgium

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Canada

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Denmark

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Finland

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

France

-0.1

0.0

0.1

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Germany

-0.1

0.0

0.1

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Ireland

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Italy

-0.1

0.0

0.1

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Fig. 2. Total cyclical movements of real GDP, credit and house prices.


4.1.2. CorrelationsThe signs of correlations of the cyclical components of

real house prices, residential investment, credit, and out-put are generally positive, consistent with the financialaccelerator theories and the procyclicality of credit andhouse prices (Table 5).23 However, the magnitude ofcorrelations (and in a few cases even their sign) vary across

23 Granger causality tests show that of the 216 possible pairs of seriesexplored, 97% of them show bi-directional Granger causality. This is hardlysurprising given the predominant share of the common component incycles. The non-parametric procedure followed in the paper is better suitedto study the behavior of pairs of series over different portions of thespectrum as the causality hypothesis embeds the comovement hypothesis;and it has been shown that the strength of comovement of series variesacross frequencies. In contrast, parametric time series analysis, by defini-tion, averages over frequencies.

countries, possibly due to differences in the financial andreal estate regulatory frameworks and the importance offinancial accelerator mechanisms. For example, house pricesand output generally have a stronger positive comovementthan credit and output in the United Kingdom, Canada,Finland, and Spain than in other countries. The favorablehousing credit conditions in some countries during the sam-ple period show up more clearly in the data: countries thatexperienced large house prices increases such as the UnitedKingdom, Canada, and Ireland also display large and signifi-cant correlations between credit and real house prices. Atthe same time, they display large correlations between out-put and real house prices. These features of the data are use-ful to model the interrelations among the three cycles andare consistent with the financial accelerator theory. In con-trast, output and house prices, and credit and house prices

-0.1

0.0

0.1

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Japan


-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Netherlands

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

New Zealand

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Norway

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Spain

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

Switzerland

-0.2

-0.1

0.0

0.1

0.2

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

United Kingdom

-0.1

0.0

0.1

1981Q1 1986Q1 1991Q1 1996Q1 2001Q1 2006Q1

United States

Fig. 2 (continued)

Table 4Characteristics of mortgage markets.

Mortgage equitywithdrawal

Refinancing Mortgage interesttax relief

Share of fixed ratemortgagesa

Home ownershipratioa

Residential mortgagedebt outstandingb

Australia Yes Partially No 16 70 51Austria No No Partially 75 56 20Belgium No No Partially 75 68 30Canada Partially No No 71 65 43Denmark Yes Yes Yes 70 52 88Finland Yes No Partially 7 64 39France No No No 68 55 26Germany No No No 84 44 52Ireland Partially Yes Partially 15 77 52Italy No No Partially 22 74 15Japan Partially Partially Partially 78 60 37Netherlands Yes No Yes 64 53 89New Zealand Yes Yes No 67 77 55Norway Yes No Partially 10 78 50Spain Partially No Partially 7 82 46Switzerland No Yes Partially 72 35 102United Kingdom Yes Partially No 28 69 75United States Yes Yes Partially 65 67 58

Source: Global Property Guide, European Mortgage Federation, and national sources.Note: The table reports various indicators of the depth and regulatory structure of mortgage markets.

a In percent.b In percent of GDP.


in the United States are not significantly correlated. Yet, notethat residential investment and output are, in line withLeamer (2007).

4.1.3. Leads and lagsOver the short and medium term, the lead–lag relation-

ship between the cyclical component of housing, credit

Table 5Correlation coefficients for total cyclical components.

Source: Authors’ estimates.Notes: The table reports the correlation coefficients for the total cyclical component of output (real GDP), house prices, residential investment, and creditseries, over the entire sample period. For each pair listed in the column title, entries in the table show the correlation between the series over the wholesample period. Correlation coefficients larger than 0.40 are significant and are highlighted.

24 Interest rates lag real output in 66% of the countries in the sample andare contemporaneous in the remainder of the cases. This result can beconsistent with different models of monetary policy. For example, interestrates may lag output because changes in them are fully anticipated byforward-looking economic agents. Alternatively, interest rates may lagoutput because monetary policymakers are not forward-looking enough or,while less likely, because they do not factor growth in their policy reactionfunction.


and output within countries is mixed (Table 6). Outputleads house prices in 17% of countries and is contempora-neous with them in 39% of countries; in 44% of countriesreal house prices lead output instead. Output also leadscredit in 44% of countries and is contemporaneous in 17%of countries, while lagging in 39% of countries. Theseresults suggest that if real house price and credit bubblesexist (as may be suspected in countries where real houseprices or credit lead output), the data do not support thegenerality of the phenomenon. Yet, output lags residentialinvestment in 72% of countries, indicating the importanceof a robust housing market in driving real activity.

House prices lead credit in about 50% of countries, arecontemporaneous in 22% of countries, and lag credit in28% of countries. Like the cross-country differences in cor-relations, the lack of a common pattern in the lead–lagrelationships may reflect differences in institutional set-tings that in turn determine differences in the channelsof transmission – whether the dominant channel relatesto increases in house prices, which improve creditworthi-ness of borrowers and allow them to borrow more, or in-stead, to greater availability of credit, owing toimprovements in bank balance sheets, or both. Residentialinvestment, by contrast, leads credit in 78% of countries, iscontemporaneous in 6 percent of countries, and lag creditin 17% of countries. Taken together with the findings onprices, this may indicate that a demand/supply shock tohousing reflected in changes in residential investmenttranslates into house price changes (residential investmentleads house prices in 67% of countries), which then maytrigger a credit surge. A major point is that differencesacross countries indicate the need to account for idiosyn-crasies when modeling housing markets.

Short-term interest rates lag residential investment in apredominant share of the cases at all periodicities (89% of

countries) and while less frequent, still in the majority ofcountries, interest rates also lag house prices. Interest rateslag credit in 72% of countries, and in most other countriesare contemporaneous.24 The finding that short-term inter-est rates never lead house prices and rarely lead residentialinvestment and credit is potentially important. One possibleinterpretation is that during the period covered by the study,monetary policy has not been used to actively influencehouse prices and credit, even in countries and regions thatinclude monetary and credit aggregates, in addition toprices, as ‘‘pillars’’ into their monetary policy frameworks(for example, the Euro area and Japan). However, it cannotbe precluded that the finding reflects the ineffectiveness ofmonetary policy in influencing house prices and credit. Inany case, this stylized fact should be accounted for whenmodeling the monetary transmission mechanism.

Two specific results are noteworthy. First, house pricesare found to lead credit, real activity, and interest rates inmany countries that have experienced significant run-upin house prices before the current crisis, for example, theNetherlands, Spain, the United Kingdom, and the UnitedStates. Second, the lead–lag relationships are found to varyconsiderably across countries in the Eurozone. These re-sults may suggest that relying on monetary policy aloneto discourage rapid increases in house prices and creditmay not be effective. A macroprudential approach may

Table 6Leads and Lags between cycles within countries.

Credit-output Credit-house prices Credit-residential investment Output-house prices Output-residential investment

6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters

Australia lag lag contemp. lag lag contemp. contemp. lag lag contemp.Austria lead lag lead lead lag lag contemp. lead lag lagBelgium lag contemp. lag lag lag lag lag lag lag contemp.Canada lag lag lag lag lag lag lag contemp. lag lagDenmark lead lead lead lag contemp. lag lag lag lag lagFinland lag lag lag lag lag lag lag lag lead lagFrance contemp. lag lag lag lag lag lag lag lag contemp.Germany lead lead contemp. contemp. lead lead contemp. lag lead lagIreland lead lead lead lead lag lag lead lead lag lagItaly lead lag lead lag lag lead contemp. lead lead leadJapan lag lead contemp. lead lag lag lead lead lag contemp.Netherlands contemp. lead lag lag lag contemp. lag lag lag contemp.New Zealand lag lag contemp. lag lag lag lead lead contemp. contemp.Norway lead lead lead lead lead contemp. lag lead lead leadSpain lag lag lag lag lag contemp. contemp. lag lag leadSwitzerland contemp. lead lag lead lead lag contemp. lag lag lagUnited Kingdom lead lag lag lag lag lag contemp. contemp. lag lagUnited States lead contemp. lag lag lag lag lag lag lag lagLags 39 50 50 67 78 67 44 56 72 50Contemporaneous 17 11 22 6 6 22 39 11 6 33Leads 44 39 28 28 17 11 17 33 22 17

House prices-residential investment Interest rates-credit Interest rates-output Interest rates-house price Interest rates-residential investment

6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters 6–16 quarters 16–32 quarters

lag contemp. lag lead lag lag contemp. lag lag laglag lag lag lead lag lag contemp. lead lag laglag lag contemp. lag lag lag lag lag lag laglag lag lag lead lag lag lag lag lag laglag lag lag lead lag lag lag lag lag laglead lead contemp. contemp. contemp. lag lag lag lag laglead lead contemp. contemp. lag lag lag lag lag laglag contemp. lag lag contemp. contemp. contemp. lead lead laglag contemp. lag lag contemp. lag contemp. lead lag laglead contemp. lag lead contemp. lag contemp. contemp. contemp. contemp.lag lag contemp. lag lag lag contemp. contemp. lag laglead lead lag lag lag lag lag lag lag laglag lag lag contemp. lead lag lag contemp. lag leadlead lead lead lead contemp. lag contemp. lag lag laglead lead lag lead lag lag lag lag lag laglag lag lag lag lag lag contemp. lag lag laglag lag lag lag lag lag lag lag lag laglag lag lag lag lag lag lag lag lag lag67 50 72 44 67 94 56 67 89 890 22 22 17 28 6 44 17 6 633 28 6 39 6 0 0 17 6 6

Source: Authors’ estimates.Notes: The table reports the lead–lag relationship between pairs of series that are the focus of the analysis. Interest rates are nominal short-term rates. For each pair listed in the column title, entries in the tableindicate whether the first variable leads or lags the second variable, or whether the relationship is contemporaneous, on average, over the frequency band. The numbers in the bottom indicate the percentage ofcountries with a given type of relation. Leads and lags are calculated using the approach suggested by Croux et al. (2001).

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be needed to support monetary policy objectives. Whilethese results are only stylized facts and without furtherempirical analysis cannot be used to derive firm policy

Credi

House Pr

-0.05

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

Common component

UNITED STATES

-0

-0

-0

0

0

0

0

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

-0

-0

-0

0

0

0

0

0

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

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1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

Common component

UNITED STATES

-0

-0

-0

-0

-0

0

0

0

0

0

0

-0.06

-0.04

-0.02

0.00

0.02

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1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

-0

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-0

-0

0

0

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0

0

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-0.02

-0.01

0.00

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1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

Common component

UNITED STATES

-0

-0

-0

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-0

-0

0

0

0

0

0

-0.10

-0.05

0.00

0.05

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0.20

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

-0.

-0.

-0.

-0.

0.

0.

0.

0.

0.

Real GDP

Fig. 3. Total cyclical movement versus comm

implications, they should be accounted for in current dis-cussions about the objectives of monetary policy in gen-eral, and ‘‘leaning against the wind’’ in particular.

t

ices

Total cyclical movement

.03

.02

.01

.00

.01

.02

.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

GERMANY

.03

.02

.01

.00

.01

.02

.03

.04

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

JAPAN


.10

.08

.06

.04

.02

.00

.02

.04

.06

.08

.10

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

GERMANY

.05

.04

.03

.02

.01

.00

.01

.02

.03

.04

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

JAPAN


.03

.03

.02

.02

.01

.01

.00

.01

.01

.02

.02

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

GERMANY

08

06

04

02

00

02

04

06

08

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

JAPAN

on components in selected countries.


A strong and important result is that in the long run,real house prices and residential investment lead output,interest rates, and credit unequivocally. More specifically,real house prices tend to lead output, which then leadscredit. The fact that house prices and credit are not coinci-dent over the long run may suggest that long-term trendsin house prices are mostly driven by fundamentals, e.g.,demographics (which raises the demand for housing anddrives construction and residential investment and de-mand for credit) or zoning requirements. Over the longterm, the stylized facts do not seem supportive of modelsin which monetary policy is effective in influencing realhouse price dynamics, as interest rates clearly lag realactivity, credit, and house prices.

4.2. International comovement of cycles

4.2.1. Common componentsTo further understand the cyclical patterns studied,

their common components are identified using a DGFM.The share of the common components in total cyclicalmovements is, in general, quite large. The large share ofthe common components driving the three cycles (espe-cially output and credit) in most countries suggests thateither cycles tend to be driven by common shocks (forexample, oil price changes), or that shocks to one countryare quickly transmitted to other countries (Fig. 3). In theglobal economy not only international trade, but alsofinancial markets, are important channels for the interna-tional transmission of shocks (see, for example, Koseet al., 2006, 2008; Belke and Orth, 2008). A large share ofthe common components in the real house price cycle,for instance, may reflect common monetary policy shocks,owing to coordinated monetary policy (consistent withOtrok and Terrones’ (2007) findings), or to the tendency

Table 7Evolution of cyclical movements driven by common components.

Output House prices

1980s 1990s 2000s 1980s 1990s 2

Australia 0.82 0.84 0.64 0.63 0.60Austria 0.30 0.72 0.91 0.59 0.90Belgium 0.90 0.89 0.97 0.90 0.78Canada 0.85 0.92 0.91 0.89 0.62Denmark 0.08 0.63 0.66 0.68 0.59Finland 0.79 0.81 0.95 0.65 0.82France 0.43 0.82 0.95 0.20 0.62Germany 0.67 0.66 0.81 0.33 0.65 -Ireland 0.76 0.85 0.93 0.57 0.76Italy 0.89 0.79 0.91 0.26 0.79Japan 0.72 0.71 0.93 0.64 0.65Netherlands 0.72 0.84 0.96 0.21 0.85New Zealand 0.16 0.90 0.90 0.80 0.84Norway 0.56 0.18 0.50 0.62 0.87Spain 0.60 0.88 0.95 0.70 0.85Switzerland 0.84 0.73 0.95 0.81 0.86United Kingdom 0.90 0.92 0.70 0.84 0.74United States 0.78 0.56 0.90 0.85 0.62Mean 0.65 0.77 0.87 0.60 0.71Median 0.74 0.83 0.92 0.65 0.77

Source: Authors’ estimates.Notes: The table reports the correlation coefficients for the total cyclical compoFig. 2) and the common components (shown in Fig. 4).

of financial globalization to reduce interest rate differen-tials. In contrast, cross-country differences in the cyclicalbehavior, may result from different economic structures– for example, the degree of reliance on commodity ex-ports, openness, diversification, and regulatory frame-works for the financial sector and mortgage markets.Those countries’ specificities may be reflected in the idio-syncratic components of variables and the different factorloadings of the common components. Identifying the eco-nomic fundamentals driving those common componentsis, however, beyond the scope of this paper.

Importantly, the role of the common components in thedomestic cycles has evolved over time (Table 7). For exam-ple, there is evidence of increased commonality in creditand business cycles in the United States in the 2000s. Over-all, in 70% of country-cycle pairs, the common componenthas accounted for a larger share of the cyclical movementin the 2000s than it had in the 1980s, without apparent re-gional patterns. This is consistent with the findings inBordo and Helbling (2003) regarding the increase in outputcomovement among industrialized countries, and it mayreflect increased financial innovation and integration,which have relaxed liquidity constraints for householdsand firms. The increased commonality is observed also inthe mean or the median of the total sample. However,the common component of real house prices, and, to a les-ser extent, residential investment, declined during the2000s in more than half of the countries in the sample.

Two striking country-specific results deserve mention-ing. First, there is the negative comovement of the com-mon component of house prices, on the one hand, andthe common components of output and credit, on the otherhand, in Germany over the whole sample period – consis-tent with the findings of the negative correlations in therespective cycles reported in Table 5 (Fig. 4). This is likely

Residential investment Credit

000s 1980s 1990s 2000s 1980s 1990s 2000s

0.35 0.45 0.74 0.62 0.90 0.85 0.870.54 0.84 0.09 0.81 0.49 0.91 0.890.92 0.88 0.84 0.90 0.81 0.91 0.890.74 0.77 0.35 0.46 0.89 0.87 0.920.87 0.70 0.71 0.69 0.55 0.60 0.900.82 0.65 0.85 0.94 0.68 0.79 0.910.69 0.86 0.90 0.94 0.40 0.25 0.820.02 0.79 0.18 0.49 0.38 0.57 0.530.90 0.73 0.81 0.70 0.91 0.84 0.940.92 0.68 0.56 0.66 0.67 0.35 0.880.21 0.76 0.61 0.82 0.31 0.67 0.690.11 0.79 0.31 0.86 0.63 0.87 0.960.53 -0.16 0.64 0.84 0.78 0.67 0.840.53 0.83 0.89 0.31 0.39 0.34 0.670.83 0.75 0.75 0.86 0.21 0.80 0.830.59 0.62 0.58 0.57 0.40 0.27 0.590.78 0.66 0.54 0.51 0.86 0.71 0.910.42 0.19 0.67 0.73 0.80 0.51 0.870.58 0.63 0.59 0.68 0.60 0.66 0.840.64 0.74 0.65 0.72 0.60 0.69 0.88

nents (including the common and idiosyncratic component, as shown in

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1


UNITED STATES

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

GERMANY

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

JAPAN

Fig. 4. Common components for selected countries.


associated with German unification, but the framework inthis paper precludes further analysis. Another interestingcountry-specific finding is that while the share of the com-mon component of house prices was relatively stable inSpain during the 1990s and 2000s, it increased in Italyand France, underscoring differences in the sensitivity ofhouse prices to external shocks in Europe.

4.2.2. Leads and lagsFrom an international perspective, a key question is

whether some countries play a leading role in the trans-mission of business cycles, housing and credit cyclesaround the world. The stylized results of this study canshed light on that question. In fact, consistent with Gian-none et al. (2005), Giannone and Reichlin (2006), Giannoneet al. (2010) and others, and in contrast to Crucini et al.(2008), US cycles tend to lead the corresponding cycles inother countries over the long term (Table 8).25 The UShousing cycle (both prices and quantities – residentialinvestment) leads other countries’ housing cycles over theentire time horizon.26 The US business cycle also leadsbusiness cycles in most other countries.27 One of the

25 The analysis in this paper makes it possible to meaningfully talk aboutlong run and short run. This methodology is not always consistent with theone followed in the referred papers, however. See footnote 22 above.

26 Exceptions are Germany, Japan and Norway, for reasons that areprobably easy to identify: Germany’s unification, Japan’s financial crisis and‘‘the lost decade’’, and Norway’s heavy dependence on oil.

27 Exceptions are Australia, Ireland, New Zealand, Norway, andSwitzerland.

mechanisms of transmission for such a strong leadingrelationship may be monetary policy as it seems thatchanges in the US policy rates tend to lead interest ratechanges in other countries.28 This hypothesis gets supportfrom another result: in contrast to other cycles, the US creditcycle leads other countries’ credit cycles only over the med-ium- to long-term horizon; over the short term, the creditcycle is contemporaneous in the majority of cases. Clearly,differences in the role of the US cycles across types of vari-ables (real activity, residential investment, house prices,and credit) over different time horizons underscore chal-lenges in assessing spillovers from the United States to therest of the world. Those differences may explain current dis-parities in the literature results regarding the relative role ofUS and global shocks in international output comovements.They suggest that modeling the dynamics of shocks trans-mission may require accounting for nonlinearities that re-flect differences in short-run and long-run direct effectsand long-run feedback effects among countries. From a pol-icy making viewpoint, these stylized features of the datahave significant implications for discussions on policycoordination.

28 One possible interpretation of these stylized facts could be thatchanges in the US monetary policy stance affect, other things equal,credit supply and real output in the US first, and then via trade,financial and confidence channels (as in Kabundi and Nadal De Simone,2010), they affect other countries’ interest rates, credit, and economicactivity.

Table 8Lead-lag relations between the United States and other countries.

Output House prices Residential Investment Credit Short-term interestrates

8–16quarters

16–32quarters

8–16quarters

16–32quarters

8–16quarters

16–32quarters

8–16quarters

16–32quarters

8–16quarters

16–32quarters

Australia lag lag lead contemp. lead lead contemp. lead lead contemp.Austria lead lead lead contemp. lead lead contemp. lead lead leadBelgium lead lead lead lead lead lead contemp. lead lead leadCanada lag contemp. lead lead lead lead contemp. lead lead leadDenmark lead lead lead contemp. lead lead contemp. contemp. lead leadFinland lead lead lead lead lead lead lead lead lead leadFrance lead lead lead lead lead lead lead lead lead leadGermany lead lead lag lag lag lead contemp. lag lead leadIreland lead lead lead contemp. lead lead lead lead lead leadItaly lead lead lead lead contemp. lead contemp. lead lead leadJapan lead lead lag lead lead lead lead lead lead leadNetherlands lead lead lead lag lead lead lead lead lead leadNew Zealand lag contemp. lead lead lead lag contemp. lead lead contemp.Norway contemp. lead lag lag lead lead contemp. lag lead leadSpain lead lead lead lead lead lead lead lead lead leadSwitzerland lead lead lead lead lead lead contemp. lead lead leadUnited Kingdom contemp. contemp. lead lead lead lead contemp. lead lead leadLags 18 6 18 18 6 6 0 12 0 0Contemporaneous 12 18 0 24 6 0 65 6 0 12Leads 71 76 82 59 88 94 35 82 100 88

Source: Authors’ estimates.Notes: For each pair of countries and the variable, entries in the table indicate whether the cyclical component in the United States leads or lags the cyclicalcomponent in the respective country listed in the first column, or whether the relationship is contemporaneous, on average, over the frequency band. Thenumbers in the bottom indicate the percentage of countries with a given type of relation. Leads and lags are calculated using the approach suggested byCroux et al. (2001).


5. Conclusion

This paper presents stylized facts about three cyclesthat are paramount to the economic growth and financialstability of modern economies: housing, credit, and realactivity. The paper highlights the cross-country diversityof cyclical comovements in the variables characterizingthese cycles, and differences across time horizons, pointingto the challenges of modeling these comovements with theaim of drawing policy prescriptions of general relevance.

The findings also help explaining the lack of consensusin the empirical literature on many issues concerningmacro-financial linkages, for example, the relative impor-tance of financial accelerator mechanisms in differentcountries and the effectiveness of domestic macroeco-nomic policies in influencing credit and house price sus-tained increases. The answers to these questions dependon the domestically-driven dynamics of the three cyclesas well as their sensitivity to global factors, which, as thepaper illustrates, tend to shift over time and vary acrosscountries.29 The paper also shows that empirical resultsare sensitive to the data transformations used to make seriesstationary, which is another factor explaining the disparatefindings in the literature.

A related implication of the stylized facts discussed inthis paper is that uniform policy prescriptions concerning,

29 The sample used in this study consists of advanced countries; evengreater differences can be expected in a broader sample including emergingand developing economies.

for example, the appropriateness of taking into account as-set prices in monetary policymaking, could be problematic.Although some general patterns can be identified, the de-gree of comovement between housing, credit, and outputvaries considerably across countries and even within indi-vidual countries over time. Statistical properties of houseprice series also differ across countries. In some countries,shocks to house price inflation are persistent – i.e., not onlyshocks to house prices are persistent – as implied by thedegree of integration of the series. Important for policy,this characteristic would complicate the stabilization ofhouse price inflation, if it were to become part of the cen-tral bank’s objective.

Finally, the study underscores the high degree of com-monality in national cycles in all three variables whilehighlighting the leading role of economic activity in theUnited States. This finding may be a reflection of theimportant role of the United States in the global economyand financial system, as well as increased trade and finan-cial integration among advanced economies. Indepen-dently of the cause of the observed stylized facts, theseresults matter not only for modeling the internationaltransmission of shocks, but importantly, for policy coordi-nation discussions.

Acknowledgments

The authors thank Tam Bayoumi, Stijn Claessens, JamesHamilton, Ayhan Kose, Gianni De Nicolò, participants inthe WEAI 8th Pacific Rim Conference, INFINITI Conference,

Table A1List of variables.

Variable Source Definition

Commodity industrial inputsprice index

WEO Index including market price series for agricultural raw materials and metals (fuel and preciousmetals are excluded). Weights used are available at http://www.imfstatistics.org/imf/

Crude oil spot price WEO Equally-weighted average of crude oil spot prices in UK. Brent (light), Dubai (medium), and WestTexas Intermediate (prior to 1983, Alaska North Slope (heavy) was used instead of West TexasIntermediate)

Stock price index Haveranalytics

Index of publicly-traded assets. Exact definition varies across countries, see http://www.haver.com/databaseprofiles.html#financial for details

World demand WEO Index of real total domestic demand in all countries where the weights are the trade exports toadvanced economies

World reserves IFS Total international reservesCapacity utilization OECD Survey-based capacity utilization rate estimate from the Business Tendency Survey (see http://

stats.oecd.org/mei/default.asp?rev=2 for further details)Balance of payments IFS Overall balance of transactions between residents and non-residents involving exchange of goods,

services and income, financial claims on and liabilities to the rest of the world, and transfersCapital stock of the business

sectorOECD Value of all fixed assets still in use, at the actual or estimated purchasers’ prices for new assets of the

same type, irrespective of the age of the assetsHousing stock OECD Number of dwellings available for residential occupancyCredit to the private sector IFS Bank credit to the domestic private sectorCompensation of employees OECD Total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work

done by the latter during the accounting period. Includes both wages and salaries and the value ofthe social contributions payable by employers (payable to to public Social Security schemes or toprivately-funded social insurance schemes, or imputed social contributions by employers providingunfunded social benefits)

Compensation rate ofgovernment employees

OECD Compensation of public sector employees relative to total economy

Compensation rate of thebusiness sector

OECD Compensation of private business sector employees relative to total economy

Consumer price index IFS Series of summary measures of the period-to-period proportional change in the prices of a fixed setof consumer goods and services of constant quantity and characteristics. Exact definition variesacross countries, see http://www.haver.com/databaseprofiles.html#financial for details

Current account OECD All the transactions that involve economic values (specifically, goods and services, income, andcurrent transfers) and occur between resident and nonresidents entities

Current disbursements ofhouseholds

OECD Direct taxes on households plus total transfers paid by households plus private final consumptionexpenditure

Current receipts of households OECD Compensation of employees plus self-employment and property income received by households pluscurrent transfers received by households

Current transfers received byhouseholds

OECD Social security benefits and other current transfers paid by government plus non-social-securitytransfers received by households

Dependent employment OECD Number of persons who are in false self-employment, i.e., relationships which in fact carry certainresponsibilities for the employer (including deduction of taxes and social security contributions atsource), but are declared as a purchase of services from a self-employed person

Dependent employment of thebusiness sector

OECD Number of persons that are dependent-employed in the private business sector

Foreign direct investment IFS International investment that reflects the objective of non-resident entities to obtain a lastinginterest in a resident enterprise. Includes equity capital, reinvested earnings, other capital, andfinancial derivatives associated with various intercompany transactions between affiliatedenterprises. Excluded are flows of direct investment capital into the reporting economy forexceptional financing, such as debt-for-equity swaps

Direct investment abroad IFS International investment made by resident entities in the economy (direct investors) with theobjective of establishing a lasting interest in an enterprise resident in an economy other than that ofthe investor (direct investment enterprise). ‘‘Lasting interest’’ implies the existence of a long-termrelationship between the direct investor and the enterprise and a significant degree of influence bythe direct investor on the management of the direct investment enterprise. Direct investmentinvolves both the initial transaction between the two entities and all subsequent capital transactionsbetween them and among affiliated enterprises, both incorporated and unincorporated

Employed OECD Number of persons who have a paid employment position or are self-employedEmployment of the business

sectorOECD Number of persons employed in the private business sector

Exchange rate IFS USD per local currency, market rate, end of periodExport unit values IFS Laspeyres index for exported goods and services with weights derived from the data for transactionsExports of goods and services IFS Exported goods and services measured on a free-on-board basis, that is, by the value of the goods at

the border of the exporting country. Goods item covers general merchandise, goods for processing,repairs on goods, goods procured in ports by carriers, and nonmonetary gold

Factor income from abroad OECD Compensation received by resident employees and operating surplus of resident producers fromnonresidents

Factor income paid abroad OECD Compensation paid to nonresident employees and operating surplus to nonresident producers fromresidents

Financial account IFS Net sum of direct investment, portfolio investment, financial derivatives, and other investment

(continued on next page)


http://www.imfstatistics.org/imf/

http://www.haver.com/databaseprofiles.html#financial


http://www.stats.oecd.org/mei/default.asp?rev=2

http://www.stats.oecd.org/mei/default.asp?rev=2


Table A1 (continued)


Fixed investment in construction OECD Capital formation in land and structuresFixed investment in machinery

and equipmentOECD Capital formation in machinery an equipment used in production

Fixed investment in non-residential construction

OECD Capital formation in land and structures for uses other than residential

Fixed investment of governmententerprises

OECD Capital formation in the public sector

Government consumption offixed capital

OECD Reduction in the value of the fixed assets used in production by the public sector during theaccounting period resulting from physical deterioration, normal obsolescence or normal accidentaldamage

Government currentdisbursements

OECD Government final consumption expenditure plus property income, social security benefits, othercurrent transfers, and subsidies paid by the government

Government current receipts OECD Total direct taxes plus social security contributions by households plus other current transfers andproperty income received by the government plus indirect taxes

Government employment OECD Number of persons employed by the public sectorGovernment fixed capital

formationOECD Fixed capital formation in the public sector

Government savings (net) OECD Government current receipts minus government current disbursementsGross domestic product deflator IFS Volume of gross domestic product (GDP) calculated by recalculating the values of the various

components of GDP at the constant prices of the previous year or of some fixed base year, frequentlyreferred to as ‘‘GDP at constant prices’’, divided by GDP at current prices

Gross domestic product IFS Aggregate measure of production equal to the sum of the gross values added of all residentinstitutional units engaged in production, expressed at constant prices referring to a base period

Gross total fixed capitalformation

OECD Total value of producers’ acquisitions, less disposals, of fixed assets during the accounting period pluscertain additions to the value of nonproduced assets (such as subsoil assets or major improvementsin the quantity, quality or productivity of land) realized by the productive activity of institutionalunits

Household disposable income OECD Balance of primary incomes of the household sector plus all current transfers, except social transfersin kind, receivable by households minus all current transfers, except social transfers in kind, payableby households, that is, total income less current transfers paid. Such transfers comprise employers’social insurance contributions; employees’ social insurance contributions; taxes on income; regulartaxes on wealth; regular interhousehold cash transfers; and regular cash transfers to charities

Household savings OECD Disposable income less final consumption expenditure after taking account of an adjustment forpension funds

Import unit values IFS Laspeyres index for imported goods and services with weights derived from the data for transactionsImports of goods and services IFS Imported goods and services measured on a free-on-board basis, that is, by the value of the goods at

the border of the exporting country. The value of imported goods calculated on this basis excludesthe cost of freight and insurance incurred beyond the border of the exporting economy. Goods itemcovers general merchandise, goods for processing, repairs on goods, goods procured in ports bycarriers, and nonmonetary gold

Increase in stocks OECD Increase in stocks of finished goods and in work in progressIndustrial production OECD Output of industrial establishments, covering mining and quarrying, manufacturing, and electricity,

gas and water supply, but excluding constructionLabor force OECD Number of all persons who fulfil the requirements for inclusion among the employed or the

unemployed during a specified brief reference periodLabor force participation rate OECD Labor force divided by the total working-age population (individuals 16–64)Labor productivity of the

business sectorOECD Output per unit of labor input in the private business sector

Labor productivity of the totaleconomy

OECD Output per unit of labor input (unit labour costs on the other hand refer to labor cost per unit ofoutput)

Long-term interest rate oncorporate bonds

OECD Interest rate on corporate bonds with maturity longer than 1 year

Long-term interest rate ongovernment bonds

OECD Interest rate on government bonds with maturity longer than 10 years

Money supply, broad definition:M2 or M3

IFS Available money including marketable securities

Money supply, narrowdefinition: base money, M1 orM2

IFS Currency including coins and notes and personal money in current and deposit accounts

Other investment assets IFS All financial assets in the balance of payments not covered in direct investment, portfolio investment,financial derivatives, or reserve assets. Major categories are transactions in currency and deposits,loans, and trade credits

Other investment liabilities IFS All financial liabilities in the balance of payments not covered in direct investment, portfolioinvestment, financial derivatives, or reserve assets. Major categories are transactions in currency anddeposits, loans, and trade credits

Portfolio investment assets IFS Transactions, creating assets for residents, with nonresidents in financial securities of any maturity(such as corporate securities, bonds, notes, and money market instruments) other than thoseincluded in direct investment, exceptional financing, and reserve assets


Table A1 (continued)


Portfolio investment liabilities IFS Transactions, creating liabilities for residents, with nonresidents in financial securities of anymaturity (such as corporate securities, bonds, notes, and money market instruments) other thanthose included in direct investment, exceptional financing, and reserve assets

Private final consumptionexpenditure

OECD Total value of all expenditures on individual and collective consumption goods and services incurredby resident households and resident non-profit institutions serving households; defined in terms ofactual final consumption as the value of all the individual goods and services acquired by residenthouseholds

Private non-residential fixedcapital formation

OECD Fixed capital formation by the private sector in non-residential structures, e.g., commercial andindustrial buildings

Private residential fixed capitalformation

OECD Fixed capital formation by the private sector in residential structures, e.g., single-family homes andapartments

Private total fixed capitalformation

OECD Fixed capital formation in the private sector

Property income received byhouseholds

OECD Income receivable by the owner of a financial asset or a tangible nonproduced asset in return forproviding funds to or putting the tangible non-produced asset at the disposal of, another institutionalunit; consists of interest, the distributed income of corporations (i.e., dividends and withdrawalsfrom income of quasi-corporations), reinvested earnings on direct foreign investment, propertyincome attributed to insurance policy holders, and rent

Real effective exchange rate,ULC-based

IFS Index representing a nominal effective exchange rate index (an index of a currency’s period- averageexchange rate to a weighted geometric average of exchange rates) adjusted for relative movementsin national cost indicators of the home country and selected countries. The cost indicator is therelative unit labor cost for the advanced economies based on a basket of 26 countries and Euro areaas a group. These 26 advanced economies include Austria, Belgium, Finland, France, Germany,Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Australia, Canada, Denmark, HongKong SAR, Israel, Japan, Korea, New Zealand, Norway, Singapore, Sweden, Switzerland, UnitedKingdom, and United States. The main source for the unit labor cost data is from the OECD AnalyticalDatabase (quarterly unit labor cost in manufacturing). However, for Australia, Hong Kong SAR,Singapore, and Israel, the unit labor cost data are provided by IMF staff (annual data interpolated intohigher frequencies). The source for the United States’ quarterly unit labor cost data is the Bureau ofLabor Statistics

Self-employed OECD Number of persons who are the sole owners, or joint owners, of the unincorporated enterprises inwhich they work, excluding those unincorporated enterprises that are classified as quasi-corporations, where the remuneration is directly dependent upon the profits (or the potential forprofits) derived from the goods or services produced (where own consumption is considered to bepart of profits). The incumbents make the operational decisions affecting the enterprise, or delegatessuch decisions while retaining responsibility for the welfare of the enterprise. In this context,enterprise includes one-person operations

Self-employment incomereceived by households

OECD All the payments, in cash, in kind or in services, which are received over a given reference period byindividuals for themselves or in respect of their family members by virtue of their involvement incurrent or former self-employment

Short-term interest rate IFS Interest rate on a loan or other obligation with a maturity of less than one year. In most cases, themoney market rate is used; interbank market rate or Treasury bill rate substitutes when that is notavailable

Unemployed OECD Number of persons above a specified age who during the reference period were without work,currently available for work, and seeking work

Unemployment rate OECD Unemployment divided by labor forceUnit capital costs OECD Unit cost for the use of a capital asset for one period – that is, the price for employing or obtaining

one unit of capital services (a.k.a. ‘‘rental price’’ of a capital good or the ‘‘user cost of capital’’)Unit labor cost of the

manufacturing sectorOECD Average cost of labor per unit of output, calculated as the ratio of total labor costs to real output, in

the private manufacturing sector. In broad terms, unit labor costs show how much output aneconomy receives relative to wages, also the equivalent of the ratio between labor compensation perlabor input (per hour or per employee) worked and labor productivity

Unit labor cost of the totaleconomy

OECD Average cost of labor per unit of output, calculated as the ratio of total labor costs to real output

Unit labor costs in the businesssector

OECD Average cost of labor per unit of output, calculated as the ratio of total labor costs to real output, inthe private business sector

Velocity of money OECD Ratio of monetary aggregate (Ml or M2) to nominal GDPWage rate of the business sector OECD Basic remuneration per time unit or unit of output in the private business sector. In a number of

countries wage rates are determined by law or regulation through collective bargaining agreements,etc. The statistics compiled on these award rates (which are generally minimum or standard rates)are clearly distinguished from statistics referring to wage rates actually paid to individual workers

Wage rate of the manufacturingsector

OECD Basic remuneration per time unit or unit of output in the private manufacturing sector

Wage rate of the governmentsector

OECD Basic remuneration per time unit or unit of output in the public sector


-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

-0.02

-0.01

-0.01

0.00

0.01

0.01

0.02

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

House prices Residential investment

UNITED STATES

-0.1

0.0

0.0

0.0

0.0

0.0

0.1

-0.1

-0.1

0.0

0.0

0.0

0.0

0.0

0.1

1981Q1 1985Q1 1989Q1 1993Q1 1997Q1 2001Q1 2005Q1

UNITED KINGDOM

Fig. A1. Common components in housing cycles, 1981–2006.


and the IMF Research Department Brown Bag seminar, twoanonymous referees, and the editor for helpful comments.We are also grateful to David Velazquez-Romero, JairRodriguez, Angela Espiritu, and Gavin Asdorian for excel-lent research assistance. Alain Kabundi gratefully acknowl-edges the financial support from Economic ResearchSouthern Africa (ERSA), South Africa.

Appendix A. Data coverage and sources

The data used in the analysis covers eighteen advancedcountries over the period from 1981 to 2006 at quarterlyfrequency. Appendix Table A1 shows the variables usedalong with the data sources and gives a brief descriptionof each variable. All series are seasonally adjusted andare expressed in real terms except for interest rates(Fig. A1).

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Documents

Housing, credit, and real activity cycles: Characteristics and comovement