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1
Investment-Specific Shocks and Real Business Cycles in Emerging
Economies: Evidence from Brazil
Eurilton Araújo Insper Institute Rua Quatá, 300
04546-042 São Paulo-SP [email protected]
Abstract
This paper investigates the role of the RBC (Real Business Cycle) model with investment-specific technology shocks in explaining business cycle fluctuations in Brazil. I consider the role of transitory and permanent components of neutral and investment-specific technology shocks. I fit the model to the data using Bayesian techniques to show that the permanent components of neutral and investment-specific shocks are the major sources of fluctuations. In fact, investment-specific shocks can account for remarkable percentages of fluctuations in GDP growth, investment growth and trade balance to GDP ratio. Furthermore, I present simulation evidence showing that the RBC model cannot account for some important features of the data. Keywords: investment-specific shocks, business cycles, Brazil JEL Classification: C11, E32, F41
1. Introduction
In this paper, I investigate the role of investment-specific technology shocks for
business cycle fluctuations in Brazil. The main goal is to gauge the importance of
investment-specific technology shocks as a source of macroeconomic fluctuations in the
context of the standard Real Business Cycle (RBC) tradition in which fluctuations are
explained by means of technology shocks in an environment without frictions. The artificial
economy has two types of technology shocks: neutral shocks and investment-specific
shocks. Neutral shocks affect the production of all goods homogeneously, and investment-
2
specific shocks affect only investment goods. I consider the role of transitory and
permanent components of each shock in accounting for Brazilian business cycles.
In a recent paper, Aguiar and Gopinath (2007) suggest that emerging market
business cycles result from permanent and transitory shocks to productivity. They find that
the permanent component of a neutral technology shock is very important to account for
macroeconomic fluctuations in Mexico. They interpret the shocks to trend as dramatic
changes in institutions and policy in emerging economies. They also analyze the case of a
small open developed economy, namely Canada. In contrast to the results for Mexico,
transitory shocks are more important for Canada than trend shocks.
Garcia-Cicco, Pancrazi, and Uribe (2007) point out that the identification of
permanent shifts in productivity is difficult using short samples. They use more than one
century of Argentinean data to conclude that the model described in Aguiar and Gopinath
(2007) is not capable of accounting for the dynamics of the trade balance. In addition, they
find a smaller role for the permanent component of the neutral technology shock. This
evidence suggests that business cycles in emerging markets cannot be explained solely by
technology shocks as in the RBC tradition. However, none of these papers addresses the
role of shocks to the marginal efficiency of investment in driving emerging market business
cycles in a simple RBC model without additional frictions. Therefore, it may still be
possible to describe business cycles as driven by technology shocks, if the model allows for
different types of technology shocks.
The findings in Greenwood, Hercowitz, and Huffman (1988); Greenwood,
Hercowitz, and Krusell (1997 and 2000); and Fisher (2006) stress the importance of
investment-specific technology shocks as a source of growth and macroeconomic
fluctuations in the US. Greenwood, Hercowitz, and Krusell (1997) focus on the importance
3
of this kind of shock for economic growth by performing a growth accounting exercise.
This study finds that approximately 60% of growth in aggregate output per men-hour is due
to investment-specific technological change. In Greenwood, Hercowitz, and Krusell (2000),
the same authors investigate the role of investment-specific technological change in
business cycle in a calibrated Neoclassical Model. The results indicate that this form of
technological change is the source of 30% output fluctuations. This is an impressive
number given that investment in equipment, the major source of this kind of technological
change, amounts to approximately 7% of US GNP. Fisher (2006) builds a RBC model, with
the two types of technology shocks, in order to generate economic restrictions capable of
identifying the two types of technology shocks in a Structural Vector Auto-regression
system (SVAR). Again, the results show the importance of technology shocks for
economic fluctuations. Moreover, investment-specific technology shocks account for the
majority of the effects of technology shocks on economic fluctuations.
More elaborated Dynamic Stochastic General Equilibrium (DSGE) Models, such as
Pakko (2002), Guerrieri, Henderson and Kim (2005), and Ireland and Schuh (2008), rely on
investment-specific technology shocks to understand the historical evolution of total factor
productivity in the US. Guerrieri, Henderson, and Kim (2005) document the increase in
productivity growth in information and communication technology (ICT) both in Europe
and in the US. To study the effects of productivity growth in the ICT sector in the US and
Europe, they build a two-country general equilibrium model in which productivity in the
ICT sector is associated with investment-specific technological change. This is in line with
Greenwood, Hercowitz, and Krusell (1997 and 2000) who emphasizes investments in new
equipments rather than in structures as the source of investment-specific technical change.
Justiano, Primiceri, and Tambalotti (2008) show that investment shocks are extremely
4
important for US business cycles in a DSGE model with nominal rigidities. Letendre and
Luo (2007) study the effects of transitory investment-specific shocks in a small open
economy calibrated to Canadian data. They find that this type of shock enhances the ability
of the model to replicate the dynamic behavior of trade-related variables, especially the
auto-correlation of the trade balance and the correlation between the output and the trade
balance.
Recent studies that build RBC models to understand Brazilian business cycles, such
as Ellery Jr., Gomes, and Sachsida (2002); and Bugarin et al. (2007), have only considered
the role of neutral technology shocks. Nonetheless, they show that the Neoclassical Model
can explain growth and business cycle fluctuations in Brazil. Historically, investment-
specific shocks may have played a potentially important role since the Brazilian
industrialization process that starts in the 1930’s and increases significantly after de World
War II, , as discussed in Baer(2008), was based on promoting growth in durable goods and
machinery through special government programs. The emerging automobile industry in the
1950s is the standard example of the Brazilian industrialization strategy based on the
substitution of imports and investment in new equipments and machinery.
In recent years, openness to trade and the effects of deregulations and privatizations
in the 1990s, as documented by Schimitz and Teixeira (2008), have increased productivity
in Brazil. In fact, it is plausible that part of that increase is due to new equipments and
increasing marginal efficiency of investment. More recently, with an increase in the rate of
adoption of ICT technology, as documented in Basant et al. (2007), a study using firm-level
data for Brazil and India, emerging market countries, and especially Brazil, tend to see a
potentially greater role of investment-specific technology change as an alternative source of
business cycles.
5
INSERT FIGURE 1
INSERT FIGURE 2
Figures 1 and 2 motivate more concretely the potential importance of investment-
specific technology shocks. Figure 1 shows an increasing pattern in the component of
investments related to equipments and machinery from 1901 to 2006. Investment in new
equipments and machines start growing around 1945, with increasing efforts to bring about
industrialization in Brazil. In a period known as the Brazilian “miracle”, the 1960’s and
1970’s, the growth was extremely fast. During the “lost decade” of the 1980’s, we see a
clear decrease; and since the mid 1990’s, a recovery has been taken place. Figure 2 shows
the time series of the relative price of investment in equipment and machinery in terms of
non-durable consumption goods from 1970 to 2007. Unfortunately, data from 1901 to 1969
is not available. The decline in this relative price, shown in Figure 2, is in line with the
empirical evidence reported in Greenwood, Hercowitz, and Krusell (2000) for the US.
Letendre and Luo (2007) describe the same pattern for Canada. In spite of the bad
economic performance of the 1980’s, the negative co-movement between the relative price
and investment in equipments, at least in the 1970’s and in the 1990’s, suggests that there
have been significant technological advances embodied in new capital goods.
I study a small open economy version of the model studied in Fisher (2006) with
variable capital utilization in the tradition of Greenwood, Hercowitz, and Huffman (1988). I
consider, as Aguiar and Gopinath (2007), shocks to levels and growth rates. In line with
Garcia-Cicco, Pancrazi, and Uribe (2007), I use annual time series from 1947 to 2007 to
improve the identification of growth rate shocks. However, instead of deriving restrictions
to identify a SVAR, I directly estimate the structural parameters of the RBC model, which
6
includes the autoregressive coefficients and variances of all types of shocks, employing
Bayesian methods.
The structural full-information macroeconometric approach has recently become a
viable alternative to calibration and limited-information methods. Ireland and Schuh
(2008) perform structural estimation of a two-sector RBC model via maximum likelihood.
Dejong, Ingram, and Whitman (2000) perform Bayesian structural estimation of a closed-
economy RBC model featuring transitory neutral and investment-specific technology
shocks for the US. Justiano, Primiceri, and Tambalotti (2008) also perform structural
Bayesian estimation of a medium-scale new Keynesian model featuring investment shocks
and a myriad of alternative sources of fluctuations.
In addition to the structural econometric exercise, I simulate the model under the
estimated parameter values in order to gauge the importance of investment-specific
technology shocks vis-à-vis neutral technology shocks as a source of macroeconomic
fluctuations.
The findings indicate that the permanent components of neutral and investment-
specific shocks are important sources of business cycle fluctuations in Brazil. Investment-
specific shocks are the most important source of fluctuations for the trade balance to output
ratio, investment growth and output growth. Neutral shocks are the most important source
of fluctuations for consumption growth. These findings are in line with Aguiar and
Gopinath (2007). As shown in Garcia-Cicco, Pancrazi, and Uribe (2007) for Argentina, the
model cannot match stylized business cycle second moment statistics, especially the ones
related to the trade balance to output ratio. The evidence that frictionless real business cycle
models cannot account for business cycle fluctuations in emerging markets long annual
data sets is also present in the Brazilian case.
7
The paper contains four additional sections. Section 2 describes the small open
economy version of the RBC model presented in Fisher (2006). Section 3 discusses the
Bayesian techniques and the data used for estimation. Section 4 presents the findings.
Finally, Section 5 offers some concluding remarks.
2. The Model
In this section, I describe a small open economy RBC model with variable capital
utilization and two shocks, one to the general technology and another to investment, as
specified in Fisher (2006). However, different from Fisher (2006), and in accordance with
Ireland and Schuh (2008), as well as Aguiar and Gopinath (2007), I consider distinct shock
specifications for the logs of the levels and the growth rates of both shocks. In the short run,
both components of each shock affect aggregate variables. The long run implications of
both shocks for macroeconomic variables depend on growth rate components. In sum, I
consider stationary shocks to the level and growth of neutral and investment-specific
technology shocks to account for the short run and the long run impacts of both shocks on
macroeconomic dynamics.
To describe the economic environment, it is sufficient to present the social planner’s
problem, since the Welfare theorems hold. The social planner chooses consumption (tC ),
investment ( tX ), hours worked ( tH ), household private debt position (tD ), the rate of
capacity utilization ( tU ), and next period’s capital stock ( 1+tK ) to
maximize ])[log(0
0 ttt
t HCE −∑∞
=
β , the inter-temporal utility function of a representative
household, subject to the following constraints:
8
t
t
t
ttttttt V
K
K
KXCYDRD
2
111 2
)1(
−+++−+= +
−− µφ (1)
ttttt XVKUK +−=+ ))(1(1 δ (2)
ηδδ
ηt
t
UU 0)( = (3)
αα −= 1)( ttttt HKUAY (4)
]1)[exp(1
* −−+=−
dZ
DRR
t
tt ψ (5)
Equation (1) describes the dynamics of debt position. Equation (2) governs capital
accumulation in this economy. According to (3), the rate of depreciationδ is a function of
the rate of capacity utilization. The production function is specified according to (4).
Finally, according to (5), the small open RBC is closed with an external debt-elastic interest
rate to induce independence of the deterministic steady state to initial debt positions. This
specification is in line with Aguiar and Gopinath (2007) and Garcia-Cicco, Pancrazi, and
Uribe (2007). The variable tD denotes the aggregate external debt per capita, taken as
exogenous by the household. The equality tt DD = holds in equilibrium. Since the model is
non-stationary because of the presence of two unit roots in the permanent component of
each shock and standard numerical solutions do not apply to models with stochastic trends,
the scale variable αα
α −−
−−− = 1
11
1
11 )()( gt
gtt VAZ is needed to obtain a stationary solution. The
parameters ,β ,α andδ are the inter-temporal discount factor, the Cobb-Douglas technology
parameter and the depreciation rate, respectively. The parameters µ and d measure the
9
long-run average growth of t
t
K
K 1+ and1−t
t
Z
D, respectively. The parameter ψ measures the
sensitivity of the interest rate to debt position.
The variables tA and tV are the neutral and the investment-specific technology
shocks, respectively. Each shock contains two separate auto-regressive components: a
stationary level disturbance (transitory component) and a stationary growth rate disturbance
(permanent component). The notation uses capital letters and the superscript g for growth
rate shocks; and small letters and the superscript l for level shocks. The following auto-
regressive processes characterize the logarithm oftA and tV :
)ln()ln()ln( gt
ltt AaA += (6)
lat
lt
la
lt eaa += − )ln()ln( 1ρ (7)
gatg
t
gtg
agg
agt
gt e
A
Aa
A
A++−=
−
−
−
)ln()ln()1()ln(2
1
1
ρρ (8)
)ln()ln()ln( gt
ltt VvV += (9)
lvt
lt
lv
lt evv += − )ln()ln( 1ρ (10)
gvtg
t
gtg
vgg
vgt
gt e
V
Vv
V
V++−=
−
−
−
)ln()ln()1()ln(2
1
1
ρρ (11)
The shocks ,late ,g
ate ,lvte and g
vte are normally distributed with variances
,)( 2laσ ,)( 2g
aσ ,)( 2lvσ and 2)( g
vσ . The auto-regressive coefficients ,laρ ,g
aρ ,lvρ and g
vρ
measure the persistence of each component of the two technology shocks and their absolute
values are less than one to guarantee that the processes are stationary. The average growth
rates of tA and tV are ga and gv , respectively.
10
The optimality conditions are described in the appendix. To find a stationary
equilibrium, I define the following normalized
variables: ,1−
=t
tt Z
Cc ,
1−
=t
tt Z
Dd ,
1−
=t
tt Z
Ii ,
1−
=t
tt Z
Yy ,tt Hh = ,tt Uu = and
gtt
tt
VZ
Kk
11 −−
= . The
transformed variables are stationary and standard numerical solutions can be applied to a
system of stochastic difference equations written in terms of these variables. The rate of
capacity utilization and hours worked are assumed to be stationary, therefore there is no
need of any transformation for these variables.
3. Empirical Methodology and Data
In this section, I briefly discuss the Bayesian approach to the estimation of DSGE
models. Then, I present the priors and the data used in the estimation.
3.1. Bayesian Methods
The use of Bayesian Methods to estimate DSGE models has increased over recent
years. An and Schorfheide(2007) survey the application of Bayesian techniques in
Structural Macroeconometrics. Under reasonable priors, Bayesian methods offer
advantages over alternative system estimators such as maximum likelihood, which is based
on the strong assumption that the DSGE model is the data-generating process. Thus, the
Bayesian approach can deal with potential misspecification problems in a better way than
maximum likelihood; and this is the main reason why it is a popular method among
empirical macroeconomists.
The vector ),,,,,,,( 01 ψµφηδαβ d=Θ contains the parameters describing the
structure of the artificial economy and the vector
11
),,,,,,,,,(2ggg
vlv
ga
la
gv
lv
ga
la vaσσσσρρρρ=Θ characterizes the technology shocks. We have
in total 18 parameters.
The equations that characterize the equilibrium conditions is presented in the
appendix, and can be written as the following system of rational expectations difference
equations tttt JeHwwGE +=+ )( 1 , in which the symbol tE denotes the expectation operator.
The matrices G, H and J are functions of the vector ).,( 21 ΘΘ=Θ The
vector ),,,,,,,( 1 ttttttttt rxcyuhkdw += contains the choice variables and te is a vector
containing transitory and permanent components of neutral and investment-specific shocks.
The solution of the system takes the form of a state space representation, in which
some variables are observed and others are not. Given a sample of observable variables,
denoted by obsTY , an econometrician computes the likelihood function )|( Θobs
TYp by means
of the Kalman Filter algorithm. Then, for any specification of the prior distribution )(Θp ,
the posterior distribution for the parameters of the model, according to Bayes Theorem,
is)(
)|()()|(
obsT
obsTobs
T Yp
YppYp
ΘΘ=Θ . The analytical computation of the posterior distribution is
almost impossible for complex models. In fact, an expression for )( obsTYp is the result of
integrating )|()( ΘΘ obsTYpp with respect to .Θ However, it is possible to obtain draws form
the unknown posterior through Bayesian simulation techniques such as the Metropolis-
Hastings algorithm, which is discussed in detail by An and Schorfheide(2007).
An advantage of the Bayesian method is the possibility of incorporating additional
information about the range of plausible values for the parameters through the specification
of the prior distribution. In fact, in DSGE models, past knowledge accumulated among
12
macroeconomists or restrictions coming from the equations describing the steady state
usually suggest reasonable ranges for the structural parameters.
Bayesian simulation techniques allow the econometrician to obtain draws from the
posterior distribution of the parameter vector. Therefore, it is possible to characterize
numerically the posterior distribution of any object that is a function of the parameters such
as variance decompositions, moments and impulse responses to shocks. These objects are
useful tools in assessing the economic significance of a model. In addition, the fit of a
particular model to the data can be measured by the marginal data density )( obsTYp .
Moreover, model comparison, in the Bayesian framework, is done by means of posterior
odds ratio which is the ratio of marginal data densities for different models. In sum,
Bayesian methods allow the assessment of the economic and statistical significance of a
model in a flexible way.
3.2. The Priors
Table 1 summarizes the prior distributions for the parameters of the model. The
priors are independent across parameters. They reflect beliefs about reasonable values for
the parameters. These beliefs are based on existing prior research, especially from
calibrated open-economy DSGE models. In addition, prior specification takes in
consideration economic restrictions on some parameters. The degree of certainty about the
values of a particular parameter can be evaluated by the tightness of the prior. In fact, an
almost non-informative prior should be specified if there is little information available on
the value of a particular parameter.
13
I use Normal, Beta, Gamma and Inverted-Gamma distributions for the parameters.
These distributions are centered in reasonable values, based on previous literature, cited in
the introduction, on RBC models applied to the Brazilian economy.
Some parameters are calibrated to standard values in the literature. This is the case if
the parameter in question is not crucial for the purpose of the paper, which is to explain
Business Cycle in Emerging Economies as driven only by technology shock and to gauge
the importance of investment-specific shocks in this context. I set 001.0=ψ and d is set in
such a way that the steady state of the trade balance to output ratio takes the mean value
observed for that variable in as Ellery Jr., Gomes, and Sachsida (2002); and Bugarin et al.
(2007) the data (1.32%). Remaining calibrated parameters are 4.0,92.0 == αβ . These
values are close to the ones used in Ellery Jr., Gomes, and Sachsida (2002) and Bugarin et
al. (2007). Steady state conditions impose restrictions on the parameters *0 ,,, Rdδµ . They
are functions of the average growth rates of tA and tV ( ga and gv ). Therefore, ga and gv are
estimated and the values of *0 ,,, Rdδµ can be recovered afterwards.
INSERT TABLE 1
3.3. The Data Set
To improve the identification of the permanent shocks I use annual time series from
1947 to 2007, following the suggestion in Garcia-Cicco, Pancrazi, and Uribe (2007). The
data are taken from IPEADATA, a Brazilian economic database. I use 4 time series in the
estimation: real per capita consumption, real per capita investment, real per capita GDP and
the trade balance to GDP ratio. The first differences of the series, in logarithmic scale, are
used in the estimation.
14
Though, there are time series information starting in 1901 for GDP per capita, the
trade balance and some components of investments, I decide to work with a data set
beginning in 1947. This choice is based on the following reasons.
First, I can use more accurate information in the estimation since data on total
investment and consumption are not available prior to 1947. Second, without consumption
growth data, I cannot assess one important stylized fact in emerging economies business
cycles, that is consumption is more volatile than output. Third, historically, prior to 1947,
Brazil was predominantly an agrarian economy with a small stock of capital. Therefore, the
prototype RBC model is a priori not a good model to address macroeconomic fluctuations
prior to 1947, since it does not account explicitly for the role of land as an important
productive factor and emphasizes technology shocks as the driver of fluctuations, which
was not the case for Brazil in its pre-industrial stage. Fluctuations in this era were mainly
due to international commodity prices fluctuations, such as the price of coffee, rubber and
sugar.
4. Results
This section presents the estimates of the parameters of the model and the variance
decomposition, indicating the relative importance of each shock to each endogenous
variable. In addition, I present simulation results concerning second moment statistics.
4.1. Parameter Estimates
15
Table 2 summarizes the posterior distributions for the parameters of the model.
There is not enough information in the data to shift the prior concerning the average growth
rates of tA and tV . Therefore, the posterior is essentially the same prior Normal density.
Concerning the auto-regressive coefficients and standard deviations for the structural
shocks, the data seem to be informative about these parameters. Investment-specific shocks
are persistent and more volatile than the neutral technology shocks. The permanent
component of investment-specific shocks is more volatile than the transitory component.
Both types of neutral technology shocks are equally volatile. The permanent component of
the neutral shock is not persistent and the transitory component is very persistent.
The mean value of the parameter η suggests a low depreciation rate in steady state.
It is also worth noticing the relative low value forφ , implying a relatively small cost of
adjustment in investment.
INSERT TABLE 2
4.2. Variance Decomposition
Table 3 summarizes the variance decomposition for each variable used in the
estimation. The variables are the growth rate of real per capita consumption (cg ), the
growth rate of real per capita investment (xg ), the growth rate of real per capita GDP (yg )
and the trade balance to output ratio (tby ).
The permanent components of both types of shocks are the main source of
consumption growth fluctuations. The permanent neutral shock can account for
approximately 64% of the consumption growth fluctuations.
16
The contribution of each shock to the investment growth fluctuations is more evenly
spread. Al shocks are important in explaining these fluctuations but investment-specific
shocks can account for more than 60%.
Output growth is driven by transitory neutral and permanent investment-specific
shocks. In fact, the permanent investment-specific shock contributes to more than a half of
the observed variance in output growth.
Finally, the trade balance to GDP ratio is almost entirely driven by the permanent
investment-specific shock with a role for the transitory neutral shock.
In short, the permanent components of neutral and investment-specific shocks are
both important sources of fluctuations in a simple RBC model fitted to Brazilian data.
Furthermore, investment-specific shocks can account for a substantial fraction of
fluctuations in GDP growth, investment growth and trade balance to GDP ratio.
INSERT TABLE 3
4.3. Second Moments
The Bayesian estimation approach uses information in the data to update parameter
values in the model conditional on the cross-equation restriction implied by the equilibrium
conditions. Since the model might not be in line with the data along some dimensions,
sometimes it his hard to match the behavior of individual variables and moments.
Table 4 reports second moments implied by the RBC model. In the model
consumption growth is less volatile than output growth. This counterfactual feature of the
model indicates that consumption smoothing is still important despite the presence of
17
permanent shocks. Investment growth is very volatile due to the very volatile permanent
component of investment-specific shock.
The model is able to generate the positive correlation of consumption and
investment growth with output; though the consumption growth correlation is very low
compared with the data. This is the case since these variables are fundamentally driven by
different sources of fluctuations as shown in table 3.
Consumption and investment growth are weakly auto-correlated. The implied
consumption and investment growth auto-correlations based on the model are compatible
with no auto-correlation. In contrast, the model can replicate accurately the output growth
auto-correlation.
In sum, though the model does not match quantitatively the stylized business cycle
statistics for consumption, investment and output growth, it is able to replicate their
qualitative features and, in some cases, can reproduce the magnitudes of some of the
statistics.
The performance of the model deteriorates concerning the dynamics of the trade
balance. In the model, the trade balance to output ratio is extremely volatile and is not
significantly negatively correlated with consumption, investment and output growth.
Moreover, the degree of auto-correlation is excessive, suggesting the possibility of a unit
root behavior for this variable. These features stand in contrast to the evidence from the
data, pointing to a less volatile and auto-correlated trade balance to output ratio which is
negatively correlated with consumption, investment and output growth.
The behavior of the trade balance to output ratio, in the model fitted to Brazilian
data, have the same characteristics documented in Garcia-Cicco, Pancrazi, and Uribe
(2007) for Argentina. In this last paper, the authors show that the odd behavior of this
18
variable is a feature of the small open economy structure based only on technology shocks
which tend to induce near unit root behavior in consumption that is translated in a near
random walk trade balance to GDP ratio.
INSERT TABLE 4
5. Conclusion
In this paper, I investigate the role of investment-specific technology shocks for
business cycle fluctuations in Brazil. The main goal is to gauge the importance of
investment-specific technology shocks as a source of macroeconomic fluctuations in the
context of the standard Real Business Cycle (RBC) tradition in which fluctuations are
explained by means of technology shocks in an environment without frictions. I estimate
the structural parameters of the RBC model, which includes the autoregressive coefficients
and variances of all types of shocks, by means of Bayesian methods.
There are two main findings. First, variance decompositions show that the
permanent component of both types of technology shock are important sources of business
cycle fluctuations in the context of a standard small open economy RBC model fitted to
Brazilian data. Second, the model cannot replicate second moment statistics, especially
moments involving the trade balance to output ratio. The first result is in line with Aguiar
and Gopinath (2007) and the second result corroborates the findings reported in Garcia-
Cicco, Pancrazi, and Uribe (2007) for Argentina.
In short, empirical evidence from annual Brazilian data shows that the permanent
components of neutral and investment-specific shocks are both important sources of
fluctuations in a simple RBC model. Simulation results concerning the computation of
19
second moments show that a simple RBC model cannot account for stylized facts of
Brazilian business cycles based on a long annual data set.
References
Aguiar, Mark and Gopinath, Gita (2007) “Emerging Market Business Cycles: The Cycle is
the Trend” , Journal of Political Economy, Vol. 115, No 1 (February), pp. 69-102.
An, Sungbae and Schorfheide, Frank (2007) “Bayesian Analysis of DSGE Models”,
Econometric Reviews, Vol. 26, No. 2-4 (March) pp. 113-172
Baer, Werner (2008). The Brazilian Economy: Growth and Development, 6th Edition.
Boulder, Colorado, Lynne Rienner Publishers.
Basant, Rakesh et al. (2007) “ICT adoption and productivity in developing countries: new
firm level evidence from Brazil and India”, Ibmec São Paulo, Working Paper No. 23-2007.
Bugarin, Mirta S. et al. (2007) “The Brazilian Depression in the 1980s and 1990s” in
Kehoe, Timothy and Prescott, Edward (editors) Great Depressions of the Twentieth
Century, Minneapolis, Minnesota, Federal Reserve Bank of Minneapolis, pp. 287-304.
DeJong, David N.; Ingram, Beth F.; and Whiteman, Charles H. (2000) "Keynesian
Impulses versus Solow Residuals: Identifying Sources of Business Cycle Fluctuations",
Journal of Applied Econometrics, Vol. 15, No. 3 (May/June), pp. 275-287.
20
Ellery Jr., Roberto; Gomes, Vitor; and Sachsida, Adolfo (2002) “Business Cycle
Fluctuations in Brazil”, Brazilian Review of Economics, Vol. 56, No. 2 (April/June), pp.
269-308.
Fisher, Jonas D. M. (2006) “The Dynamic Effects of Neutral and Specific Technology
Shocks”, Journal of Political Economy, Vol. 114, No. 3 (June), pp. 413-451.
Garcia-Cicco, Javier; Pancrazi, Roberto; and Uribe, Martin (2007) “Real Business Cycles
in Emerging Countries?”, Department of Economics, Duke University, Working Paper.
Guerrieri, Luca; Henderson, Dale; and Kim, Jinill (2005) “Investment-Specific and
Multifactor Productivity in Multi-Sector Open Economies: Data and Analysis”.
International Finance Discussion Paper 828 (February), Washington: Federal Reserve
Board.
Greenwood, Jeremy; Hercowitz, Zvi; and Huffman, Gregory W. (1988) “Investment,
Capacity Utilization, and the Real Business Cycle” American Economic Review, Vol. 78,
No. 3 (June), pp. 402-417.
Greenwood, Jeremy; Hercowitz, Zvi; and Krusell, Per (1997) “Long-Run Implications of
Investment-Specific Technological Change” American Economic Review, Vol. 87, No. 3
(June), pp. 342-362.
21
Greenwood, Jeremy; Hercowitz, Zvi; and Krusell, Per (2000) “The Role of Investment-
Specific Technological Change in the Business Cycle” European Economic Review, Vol.
44, No. 1 (January), pp. 91-115.
Justiniano, Alejandro; Primiceri, Giorgio; and Tambalotti, Andrea (2008) “Investment
Shocks and Business Cycles”, Department of Economics, Northwestern University,
Working Paper
Letendre, Marc-André and Luo, Daqing (2007) “Investment-Specific Shocks and External
Balances in a Small Open Economy Model”, Canadian Journal of Economics, Vol. 40, No.
2 (May), pp. 650-678
Pakko, Michael R. (2002) “What happens when the technology growth trend changes?”,
Review of Economic Dynamics, Vol. 5, No. 2 (April) , pp 376-407
Schmitz Jr., James A. and Teixeira, Arilton (2008) “Privatization’s Impact on Private
Productivity: The Case of Brazilian Iron Ore” Review of Economic Dynamics, Vol. 11, No.
4 (October), pp. 745-760
Schuch, Scott and Ireland, Peter N. (2008) “Productivity and US Macroeconomic
Performance: Interpreting the Past and Predicting the Future with a two-Sector Real
Business Cycle Model” Review of Economic Dynamics, Vol. 11, No. 3 (July), pp. 473-492
22
TABLES
Table 1: Priors
Parameters Density Mean Std. Dev
)1(100 −ga Normal 1.5 1
)1(100 −gv Normal 1.5 1
η Gamma 6.5 3
gaρ Beta 0.25 0.2
laρ Beta 0.25 0.2
gvρ Beta 0.25 0.2
lvρ Beta 0.25 0.2
φ Gamma 2.5 3
gaσ100 Inverted Gamma 2 5
laσ100 Inverted Gamma 2 5
gvσ100 Inverted Gamma 2 5
lvσ100 Inverted Gamma 2 5
23
Table 2: Posteriors
Parameters Mean Std. Dev 90% HPDI
)1(100 −ga 1.5166 1 [-0.1271, 3.1222]
)1(100 −gv 1.5133 1 [-0.0719, 3.1016]
η 11.3288 1.8413 [6.3389, 16.0522]
gaρ 0.1346 0.0534 [0.0250, 0.2311]
laρ 0.9172 0.0115 [0.8620, 0.9761]
gvρ 0.6601 0.0808 [0.4971, 0.8234]
lvρ 0.5669 0.0905 [0.3549, 0.7740]
φ 2.6163 0.3005 [1.8392, 3.3578]
gaσ100 1.6476 0.3068 [0.5255, 2.6726]
laσ100 1.7288 0.4283 [0.4866, 3.2689]
gvσ100 2.5874 0.4291 [0.5108, 3.6388]
lvσ100 2.0342 0.3849 [0.4303, 5.1311]
24
Table 3: Variance Decomposition (in %)
Variables Shocks
gae lae gve l
ve
cg 64.0
[55.40, 71.30]
16.10
[4.50, 28.30]
19.70
[9.80, 30.60]
0.20
[0, 0.4]
xg 17.50
[12.70, 21.30]
20.70
[13.10, 26.90]
30.70
[9.30, 41.40]
31.20
[19.20, 44.80]
yg 10.80
[5.20, 17.00]
33.70
[25.80, 41.70]
52.60
[41.80, 66.80]
1.70
[0.40, 4.00]
tby 9.30
[3.40, 14.60]
16.10
[3.40, 35.40]
74.70
[48.70, 91.90]
0.80
[0.10, 1.80]
Results reported are posterior median and 90% HPDI in brackets
25
Table 4: Second Moments
cg xg yg tby
Standard Deviation (in %)
Data 4.76 10.38 3.59 2.25
Model 3.81
[3.55, 4.07]
18.52
[15.84, 20.64]
5.79
[4.18, 7.11]
41.59
[28.42, 56.39]
Correlation with yg
Data 0.76 0.52 1 -0.27
Model 0.115
[-0.036, 0.29]
0.436
[0.293, 0.530]
1 0.051
[0.014, 0.098]
Correlation with tby
Data -0.40 -0.18 -0.27 1
Model -0.003
[-0.079, 0.062]
0.271
[0.25, 0.294]
0.051
[0.014, 0.098]
1
Serial Correlation
Data 0.24 0.19 0.45 0.75
Model 0.017
[0.009, 0.03]
0.05
[-0.09, 0.15]
0.49
[0.41 0.58]
0.988
[0.981, 0.993]
Results reported are posterior median and 90% HPDI in brackets
26
FIGURES
-7
-6
-5
-4
-3
-2
-1
0
10 20 30 40 50 60 70 80 90 00
Figure 1: Equip and machin (in log)
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1970 1975 1980 1985 1990 1995 2000 2005
Figure 2: Relative Price of Investment