The Credit Channel in Monetary Policy Transmission at the

Zero Lower Bound. A FAVAR Approach

Master Project

Alexandru Barbu, Zymantas Budrys, Thomas Walsh

Barcelona Graduate School of Economics

Master in Economics

June 7, 2014

Abstract

This paper aims to provide a methodology for identifying the credit channel in US

monetary policy transmission, consistent with periods at the zero lower bound. We

follow Ciccarelli, Maddaloni and Peydro (2011) in identifying credit shocks through

quarterly responses in the Federal Reserve’s Senior Loan Officer Survey, but augment

their identification strategy in two key ways. First, we use the credit variables inside

a Factor Augmented Vector Autoregression, to summarize the information contained

in a set of 110 US macroeconomic and financial series. Second, we adopt the shadow

rate developed by Wu & Xia (2013) as an alternative to the effective federal funds

rate at the zero lower bound. We present our results through impulse response func-

tions and carefully designed counterfactuals. We find that monetary policy shocks

have considerably larger effects through the credit supply side than the credit demand

side. Building counterfactual analyses, we find the macroeconomic effects arising from

the supply side of the credit channel to be sizable. When focusing on the recent un-

conventional policies, our counterfactuals show only very modest movements in credit

variables, suggesting that the positive effects of unconventional monetary policy during

the crisis may not have acted strongly through the credit channels.

1

1. Introduction

The recent global financial crisis has heightened the interest of both academia and

policy circles in the empirical relevance of the credit channel for the effective trans-

mission of monetary policy to the real economy. In turbulent times, however, such

an analysis faces additional identification challenges. The breadth and heterogeneity

of the Federal Reserves unconventional measures have undeniably expanded the set of

macroeconomic variables relevant to monetary policy assessment. 1

A second consideration is the relative lack of variation in the conventional mone-

tary policy measure, the effective Federal Funds rate, since it had reached the ZLB in

January 2009. Consequently, economists have sought a single measure that can par-

simoniously capture the stance of the monetary policy at the zero lower bound while

possibly quantifying the impact of unconventional monetary policies on the macroe-

conomy.2

These arguments guide our choice of methodology and emphasise the relative impor-

tance of the identification of credit channel in monetary policy transmission mechanism.

Accordingly, this paper seeks to provide a framework for identifying the credit

channel in US monetary policy transmission, consistent with periods at the zero lower

bound. Following Ciccarelli et al.(2013), we trust the bankers and identify credit shocks

through quarterly responses in the Federal Reserves Senior Loan Officer Survey. We

augment their identification strategy in two important ways. First, we employ the

credit variables inside a Factor Augmented Vector Autoregressive model, to summa-

rize the information contained in a set of 110 US macroeconomic and financial series.

Second, we adopt the shadow rate developed by Wu& Xia(2013) as an alternative to

the effective federal funds rate for the period after January 2009. The shadow interest

rate provides a measure claimed to summarize the stance of US monetary policy at the

zero lower bound.

Our paper makes contributions to three different strands of literature. To our

knowledge, this is the first application of the shadow rate to the identification of the

credit channel in monetary policy transmission at the zero lower bound. Second, the

literature examining the implications of the credit channel in a Factor Augmented VAR

models is rather thin. Jimborean et al.(2013) provide such an analysis for the French

1For a more comprehensive review of unconventional policies, see, for example, Thornton (2012).2Bullard (2013) provides a brief summary of the ongoing research on this topic.

2

economy, but identification of credit shocks is based on bank-level balance sheet ratios.

Third, this is the first methodology able to disentangle and quantify the effect of broad

lending channel and credit demand channel, as defined by responses from Senior Loan

Officer Survey, on such a large set of macroeconomic series, as facilitated by our FAVAR.

The paper is organized as follows: Section 2 reviews the credit channel literature

and discusses its main identification challenges. Section 3 presents our methodology

and proposes a proper identification of the credit shock, of the monetary shock at the

zero lower bound and of the wider macroeconomic model. Section 4 summarizes our

data. Section 5 presents the main results and interpretation, section 6 presents our

evaluation and section 7 concludes.

2. Literature review

A common puzzle in business cycle analysis is the observation of large and persis-

tent business cycle fluctuations stemming from relatively small and temporary real

or monetary shocks (King and Rebello, 1999). The traditional view in the monetary

policy transmission literature is that monetary authorities leverage their control over

short term interest bearing securities to affect the cost of capital and subsequently real

spending on durable and investment goods (Bernanke and Gilchrist, 1995) Following

the literature, we interpret this transmission mechanism as the interest rate channel.

Bernanke and Gertler(1995) claim the interest rate channel fails to explain empir-

ical evidence in 3 important aspects: timing (some real variables, such as business

fixed investments, react long after the interest rate has reverted to trend), magnitude

(large output fluctuations come at odds with the relatively small cost-of-capital effects

predicted in empirical studies) and composition (large impulse responses in long lived

assets to shocks in short term rates.

Their findings point to the existence of a credit enhancement channel - a mecha-

nism that amplifies and propagates monetary policy shocks to the real economy. At its

root lies the concept of external finance premia: a wedge between the cost of internal

finance (liquid assets, retained earnings) and external sources of finance (debt, equity).3

3Conventionally, the external finance premium is rationalized through the presence of frictions such ascredit market imperfections, asymmetric information, principal agent problems, costly monitoring or costlystate verification.

3

Another theoretical result is the financial accelerator hypothesis: the presence of en-

dogenous dynamics in the external finance premia across the business cycle (Bernanke,

Gertler and Gilchrist (1996)). For identical financing needs, the external finance pre-

mia is inversely correlated with the firm’s net worth (liquid assets) and collateral value

on illiquid assets. A small negative shock to firms’ net worth damages firms’ credit-

worthiness, lowers access to capital, dampens investment expenditures, lowering future

net worth, which in turn has negative consequences on present net worth, and so on

and so forth.

Testing for the empirical relevance of credit channel in business cycle dynamics

exposes us to severe identification challenges. These problems stem from the fact

that fluctuations in credit demand and supply are by and large unobserved variables.

According to Bernanke & Gertler (1995), credit aggregates are largely unable to dis-

entangle the effects stemming from the credit channel from those generally associated

with the interest rate channel. As shown in Bernanke, Gertler and Gilchrist (1996),

following a monetary policy tightening, both the interest rate channel, through the

policy rate, and credit channel, through the external finance premia, predict similar

dynamics in lending volumes.

Aggregate credit measures fail to account for the amount of existing credit lines,

whose demand tends to be countercyclical (Ciccarelli et. al., 2013). Ultimately, statis-

tics of aggregate credit prices fail to control for such strategic behaviors as flight to

quality. This reported tendency of banks to optimally rebalance their portfolios to-

wards their most creditworthy borrowers during phases of financial fragility artificially

reduces the sensitivity of credit price aggregates to changes in monetary policy. Al-

ternatively, micro data takes into account actual credit granted, instead of total loan

demand, being forced to make restrictive assumptions over the latter.

There is an growing literature of the relevance of bank lending shocks in driving

fluctuations in macroeconomic variables. Amiti and Weinstein (2013) find that bank

lending shocks can account for around 40% of the variation in investment expenditure

in Japan. Chodorow-Reich(2014) finds that the contraction of credit can explain up

to half of the employment decline from a sample of SMEs following the collapse of

Lehman Bros. Kashyap, Stein and Wilcox (1993) provide more evidence of a loan

supply channel of monetary policy to the real economy.Following monetary tightening,

the mix of external finance changes such that firms rely more on other external sources

such as commercial paper, and less on bank loans. They find bank loan supply directly

affects firms’ investments, suggesting that firms cannot perfectly substitute bank lend-

4

ing. Kashyap and Stein (2000) have investigated the impact of monetary policy on

lending behaviour of banks using data on one million loans. Moreover, Kashyap, La-

mont and Stein (1994) show that monetary policy has significant impacts on firms

inventories through liquidity constraints.

3. Methodology

Given the aforementioned empirical challenges, we adopt the approach of Ciccarelli

et al (2013) in identifying the credit channel through responses in the quarterly US

Senior Loan Officer Survey. A breakdown in broad lending channel and credit demand

channel is done following definitions from Bernanke et. al. (1995).

Senior Loan Officer Survey

Regional Feds request quarterly information on the lending standards that banks apply

to customers and on the loan demand they receive from firms and households. The

survey applies to a representative sample of 60-70 insured, domestically chartered com-

mercial banks.4 Due to data availability, we consider only commercial and industrial

(C&I) loans. Our series starts in 1991Q4. Respondents are asked to assess the change

in lending standards they apply to business loans and credit demand they receive from

business customers. Responses are weighted on a scale, from eased considerably to

tightened considerably. Only credit changes in the last 3 months are considered. Re-

garding the identification of credit shocks, we follow Bernanke & Gertler (1995) and

denote an innovation to responses related to demand for loans as a shock to credit de-

mand and an innovation to total lending standards as a shock to credit supply (broad

credit channel). While the SLOS responses are qualitative, results are reported as

net percentages.5 Once again, we trust the bankers in the sense that we take their

responses to be true and accurate. A detailed description of the SLOS questions is

provided in the annex.6

4The number of Senior Loan Officer Survey respondents varies slightly across the series5For any given credit variable, net percentages are constructed as the difference between the number of

banks reporting that standards have eased somewhat or considerably and the number of banks reportingthat standards have tightened somewhat or considerably, divided by the number of banks in the sample.

6For a review of the relative performance of Senior Loan Officer Survey in identifying the credit channelin the US monetary policy transmission, see Lown&Morgan (2006)

5

The shadow rate and the zero lower bound

A common practice in the monetary policy transmission literature is to identify the

monetary policy shock as an unexpected standardized change in the overnight Federal

Funds rate.7 However, since December 2008, the Federal Funds rate has (effectively)

been at the zero lower bound. The ensuing lack of variation implies the Federal Funds

rate can convey little information about the changes in US monetary policy during

the ZLB period. Moreover, the structural break in the variation of Federal Funds

rate would cause significant identification challenges for a prolonged period, long af-

ter the policy would have exited the zero-lower bound. Moreover, as Williams (2014)

emphasizes, the frequency and duration of zero-lower bound events might be severely

understated.8

Consequently, the literature has sought to find a monetary policy measure consis-

tent with both normal and zero-lower bound periods. In a seminal paper, Black (1995)

defines the nominal interest rate as an option with a strike price at the ZLB and the

short term shadow rate as the value of its underlying asset. The nominal interest rate

will equal the shadow rate for any positive values rt ≥ 0, and zero otherwise. Wu &

Xia (2013) use this insight to model the shadow rate through a Gaussian Affine Term

Structure Model (GATSM). GATSM uses information from selected yields at different

maturities to construct the remainder of the yield curve.9. While GATSM are very close

approximators of the actual yield curves in normal times, they fail in zero-lower bound

periods, as they allow for the possibility of negative nominal rates, which is implausible.

To simulate the yield curve at the ZLB, Wu & Xia (2013) introduce a non-linearity

in their linear factor model. The short term nominal rate becomes a non-linear function

of the factors. Factors are extracted from the observed yields using principal compo-

nent analysis and regressed on the yields. Then the model parameters are estimated,

and the estimates are used to create a counterfactual shadow rate that is affine in the

factors. The shadow rate is the nominal rate that would prevail were there no physical

currency. (if the ZLB would not exist).

Wu & Xia (2013) further provide an approximation which allows for closed form

solutions in multiple factors models. Hence, it returns a model that is empirically

7A comprehensive review of monetary policy shock choices is provided by Christiano, Eichenbaum andEvans (1999).

8Williams (2014) argues that modelling the probability of ZLB occurring is chiefly based on historicaldata from a short enough period to indicate ZLB events would practically be non-existent

9Hamilton and Wu (2010) provide derivation and intuition behind GATSM

6

tractable, simulates the observed ZLB yield curve with an high level of precision and

returns a shadow rate that is robust to different specifications.10

Note, though, that the entire theoretical construct would be a risk in the event of

a very persistent zero-lower bound period.11 Since in the Wu & Xia (2013) model, the

shadow rate is a function of forward rates, and this forward rates summarize the ex-

pectations about the future short term rate, a long enough ZLB period could stabilize

investors expectations of future short rates to zero for long enough for the shape of the

yield curve to be impaired. However, Swanson and Williams (2013) provide evidence

that the sensitivity of longer term yields to news during the current ZLB period is not

significantly altered.

Evaluating the Shadow Rate as a Measure of Monetary Policy

Wu & Xia (2013) verify whether the shadow rate is a reliable representation of the US

monetary policy stance at the ZLB. To test for a structural break in the dynamics of

the monetary policy rate across pre and post crisis periods, they run a likelihood ratio

test. The restricted model requires that the autoregressive coefficients of the reduced

from model are not significantly different before and after the crisis. They cannot reject

the null hypothesis of no structural break for the shadow rate, but do reject the null

hypothesis for the effective federal funds rate. Given the shadow rate, by construction,

closely follows the effective federal funds rate in normal times (see fig. 1, annex), but

decouples and continues to exhibit reasonable variation at the zero lower bound, we

interpret Wu & Xia shadow rate as an alternative monetary policy measure consistent

with the ZLB.

Following the literature, we set the beginning of the ZLB period to Q1 2009. We

construct a continuous series of the monetary policy rate by appending the effective

Federal Funds rate before the ZLB period with the shadow rate estimates during the

ZLB period. We subsequently employ the shadow rate, as an alternative measure of

monetary policy rate at the ZLB, and the credit variables, as identified from the re-

sponses in the Senior Loan Officer Survey, in a Factor Augmented VAR model, as

10For a more extensive discussion on the effectiveness of the Wu & Xia (2013) shadow rate in summarizingmonetary policy stance at the zero lower bound, see Bullard (2012) and Hamilton (2013).

11In Black(1995) model, the prospect of non-positive longer term yields is excluded. This holds intheoretical cases with continuous time. In practice, though, with non-zero step intervals, longer rates canbe negative, since there is some probability, given the current level and volatility of the shadow rate process,the rate will remain negative for the length of the horizon.

7

detailed below.

A Factor Augmented Vector Autoregressive Model

Following Sims (1980) critique of incredible identifying restrictions in dynamic simulta-

neous equations models, structural vector-autoregressions have become a powerful tool

in monetary policy transmission analysis. As Bernanke, Boivin&Eliasz (2004) explain,

the VAR approach requires only a plausible identification of the monetary policy shock

and not necessarily of the remainder of the macroeconomic model. To the extent to

which the monetary authority sets policy based on variables that are excluded from

the model, the resulting impulse responses are likely to be biased. If Sims(1992) jus-

tification of the price puzzle as a response of the monetary authority to inflationary

pressures not captured in the VAR model was right, the reasoning can be generalized

for other omitted variables.

We follow Bernanke, Boivin & Eliasz (2005) in specifying a Factor Augmented

Vector Autoregressive model of the US economy. One benefit of a FAVAR is it can

summarize the information contained in a large set of observed macroeconomic vari-

ables Xt in a relatively compact vector of latent factors Ft. Moreover, it ameliorates

degrees-of-freedom problems, mimics the large information set monetary authorities

might actually use in setting policy and obtains impulse responses for a large set of

macroeconomic variables of interest.

Following Bernanke et. al.(2005), we specify the following factor augmented vector-

autoregressive model:[Ft

st

]=

[µF

µs

]+ Ψ1

[Ft−1

st−1

]+ ...+ Ψp

[Ft−p

st−p

]+

[eFt

est

](1)

where the latent factors Ft and the shadow rate st load on the macroeconomic series

Xt according to : [Xt

Yt

]= L

[Ft

Yt

]+ εt (2)

where Xt is a N × 1 vector of observed macroeconomic series, Ft is a K ′times1 vector

of latent factors which summarize the dynamics in Xt, with K << N , st is a vector of

observed shock variables, which includes the monetary policy rate but might include

8

also other macroeconomic or credit variables, and mt is a measurement error. Since

the factors Ft are unobserved variables, we cannot use OLS to estimate the dynamic

equation in (2). Following Bernanke et.al.(2004), we adopt a two step principal com-

ponent analysis.

The argument behind principal component analysis is that observable variables

tend to be correlated, therefore have certain degree of redundancy in estimation. It

should be, therefore, possible to extract a smaller sample of orthogonal factors which

capture most of the variance in the observed series. The factors would then be linear

combinations of the weighted series.

The principal component analysis is performed as follows: From the measurement

equation, we estimate the matrix of coefficients and extract eigenvalues and eigen-

vectors. We order eigenvalues from largest to smallest and extract the factors corre-

sponding to the largest eigenvalues. We then use these factors in the dynamic equation.

Since conventional factor selection and lag selection criteria are shown to be less

reliable for FAVAR specification, we are guided by the literature. Following Bernanke

et. al. (2005) and Wu et al. (2013), we set the number of factors to 3. However, results

are robust for different factor specifications. Following Boivin & Giannoni (2009), we

set our optimal number of lags for quarterly series to 1.

In the resulting reduced VAR model, we impose the following variable ordering: We

set the monetary policy variable last. Following Bernanke et.al(2004), we differentiate

between slow moving and fast moving variables. Slow moving variables are assumed

not to react contemporaneously to monetary policy shocks. We extract factors from

the slow moving variables and place them before the policy rate.F1,t

F2,t

F3,t

st

=

µF1

µF2

µF3

µs

+ Ψ1

F1,t−1

F2,t−1

F2,t−1

st−1

+

eF1,t

eF2,t

eF3,t

est

(3)

At first, we run our baseline specification, with the vector of contemporaneous vari-

ables Yt containing the factors and the monetary policy rate. Subsequently, we add,

along the monetary policy rate, our two credit variables. In ordering credit supply and

credit demand, we follow Ciccarreli et. al.(2010) argument that credit supply adjusts

quicker to monetary policy shocks. Accordingly, we order the credit supply variable

last, but before the monetary policy variable.

9

Results are presented through impulse response functions, historical decompositions

and counterfactual analyses. To obtain the impulse response functions, we convert our

VAR(1) process into a VMA(∞) and compute the response from a monetary policy

shock today ust to the s step ahead forecast of our macroeconomic variable of interest,

through the factor loadings.

Jmp,is =

∂Xit+s

∂ump= by.i

∂Y it+s

∂ump+ bst.i

∂st+s

∂ump(4)

For historical decomposition, we make use of the Wald Theorem (Hamilton, 1994) and

write our VMA process as a sum of p initial conditions and the series of shocks for all

subsequent periods, where p is the number of lags.

X1t = J011u1t + J012u2t + J013u3t + ...+ φ011X01 + φ012X02 + φ013X03... (5)

for all periods 1..t and initial conditions 1..p for all lags p.

Finally, we run ex-post counterfactual analyses. First, to quantify the relative

impact of the credit demand channel and credit supply channel on the macroeconomic

variables, subsequent to a monetary policy shock, we construct the following reduced

form coefficient matrix:Fi,t

Cdt

Cst

st

=

φ1,i φ1,k+1 φ1,k+2 φ1,k+3

... ... ... X

... ... ... X

... ... ... ...

Fi,t−1

Cdt−1

Cst−1

st−1

+

eFi,t

eCdt

eCst

est

(6)

where i = 1..K. By alternatively setting the coefficients in front of the credit vari-

ables to zero, we effectively shut down the credit channel, while allowing shocks from

the macroeconomic variables to feed in the dynamics of credit variables. Second, to

quantify the impact of unconventional monetary policies on credit supply and credit

demand during the ZLB period, we modify the historical decomposition for the credit

variable of interest by replacing the estimated shocks with some counterfactual shocks

that would have been necessary to push the shadow interest rate back to the ZLB.

4. Data

Our data is restricted to quarterly frequency, given the availability of the US Se-

nior Loan Officer Survey. Following Boivin&Giannoni (2009), we construct our fac-

10

tors from a balanced panel of 110 quarterly US macroeconomic series, for the period

1991Q4 − 2013Q4. The series are freely available at the Federal Reserve Bank of St.

Louis Economic Database (FRED). All macroeconomic series are transformed to en-

sure stationarity. Both our choice of series and transformation codes follow literature

and are shown in the appendix. Our credit data is compiled from Senior Loan Offi-

cer Opinion Survey on Bank Lending Practices, available at the US Federal Reserve

Board of Governors website. The SLOS series span from 1991Q1 to 2013Q4 and are

downloaded in net percentages. Wu & Xia(2013) Shadow Rate is available online on

the Federal Reserve Bank of Atlantas website, and updated in real time.

5. Results

We report our results as follows. First, we estimate our model for the pre-crisis period,

defined as 1991Q4 - 2007Q4 and for the baseline specification, excluding credit vari-

ables. This is to compare our results with Bernanke et. al (2004) Wu & Xia(2013) and

thus confirm that our model is well specified and can adequately model the dynamics

of this period.

The pre-crisis sample includes only a mild downturn, 2000-2001, from which the

US economy recovered fairly quickly. The crisis starting in 2007 was an event of much

larger magnitude and was not isolated to the US. Given that the FAVAR is purely

statistical and fits parameters based on our sample data, adding the crisis period will

likely have a very strong influence on how comovements are estimated. This may or

may not be detrimental to estimation.

5.1 Pre-crisis period

Figure 1 plots impulse response functions for a selection of 8 US macroeconomic se-

ries including the monetary policy rate, as the baseline specification (3 factors, 1 lag),

excluding credit variables. The macro series include Industrial Production, CPI in-

flation, GDP deflator, Unemployment, Housing Starts, Inventories and Private Fixed

Investment. Impulse responses are plotted for a 25 basis points tightening of monetary

policy rate. All responses are standardized. Bootstrapped 90% confidence intervals are

displayed.

We note that both Fixed Investment and Private Inventories fall, a move consistent

11

with the interest rate channel: as firms face higher interest rates, fewer prospective

projects will achieve positive NPV, thus at the margin, we would expect to see firms

investment expenditures fall. Given that investment is a forward looking decision over

a long horizon, investment decisions are likely to be sensitive to changes in interest

rates - firms would simply postpone their investment plans until rates become favor-

able again. Indeed, while inventories return to trend in around 15 quarters, investments

take longer, implying a larger persistence to monetary shocks.

Moreover, falling output results in lower inflation and higher unemployment - spare

capacity in the economy puts downwards pressure on prices, as reflected in downward

movements in CPI inflation and the GDP deflator. Again, our results appear to be

consistent with standard macroeconomic theory and are similar to those found by

Bernanke et. al. (2004) for a similar specification.

5.2 Crisis period included

In Figure 2 we present impulse responses functions of selected macroeconomic series

for the US economy. Our sample now runs from 1991Q4 until 2013Q4. Responses of

Unemployment and Industrial Production still point downwards, but are now deeper

and more persistent. Responses of Inventories and Fixed Investment are of approxi-

mately the same magnitude as in the pre-crisis period. However, both display greater

persistence.

With the inclusion of the crisis period, CPI behaviour is strikingly different, and

shows a positive response to a monetary tightening, (however error bands are large, and

this the same response found by Wu & Xia over the period with similar estimation).

Very large expansionary unconventional monetary policy operations (which lower the

shadow rate) were occurring at around the same time as large downward shocks to

inflation - this may be a reason why a then hypothesised tightening of monetary policy

(increasing shadow rate) would increase inflation.

The VAR will absorb this comovement, even though there is no direct causal link

between low rates and low inflation. Low inflation arises in a weak economy, and mon-

etary policy is set in a manner that is endogenous to the state of the economy (central

bankers probably care about output and not just inflation) and so when GDP falls,

central banks will loosen policy at the same time as falling inflation (which usually

happens in recessions), even though theory tells us, ceteris paribus, that looser mone-

12

tary policy should raise inflation not lower it.

The GDP Deflator, in contrast to CPI inflation, continues displaying a more ex-

pected response - inflation falls when monetary policy tightens - except it is far more

persistent and does not display reversion back towards zero within the 24 quarter hori-

zon.

Inclusion of the crisis period appears to have increased the persistence with which

shocks affect the macro-economy, especially in Unemployment and GDP Deflator. As

previously mentioned, we believe this comes from the fact that the recession period was

a downturn of much greater size than that of the pre-crisis sample, and so, with the

inclusion of the crisis, we would naturally see impulse responses of greater amplitude

and persistence as the VAR system learns from these extra events.

5.3 Adding credit variables

In the subsequent set of results, we augment our baseline specification by adding the

two survey variables to the reduced form vector Yt. Despite an increase in the number

of estimated coefficients, our standard errors do not vary significantly.

As mentioned in the methodology, we impose the following Cholesky ordering of

credit variables: by assuming banks react faster to monetary policy shocks and adjust

lending standards before firms adjust loan demand, we order credit demand before

credit supply.12 We run a robustness check with reverse ordering. This does not alter

the results.

Figure 4 plots the impulse response functions for Credit Demand and Credit Supply

to a 25 basis points monetary policy tightening. A negative move in credit demand

denotes a decrease in demand for commercial and investment loans. A positive move

in credit supply denotes a tightening in the lending standards banks apply to business

clients.

As one would expect, demand for credit, as reported by the Senior Loan Officer

Survey, contracts following a monetary policy tightening. The tighter monetary policy

raises short term rates, making borrowing more expensive (interest rate channel), but

also discount future expected cash flows from firms investment, depress net worth and

increase external finance premia (credit channel).

12Ciccarreli et. al. (2010) provides a further justification for this variable ordering

13

Regarding Credit Supply, we would expect to see banks’ tightening fall following an

increase in the short term rate. Any short term financing banks do to reconcile their

balance sheet positions would now be more punitive. At the margin, they will reduce

loan supply in an attempt to avoid incurring such costs or reduce the likelihood. The

upward move in lending standards comes to support our expectations.

Figures 4 and 6 plot the impulse response functions for credit demand and credit

supply variables to a 25 basis points tightening in monetary policy rate. Dynamics are

consistent with macroeconomic theory and similar to those found by Ciccarreli et. al.

(2013). While the general shape of dynamics is similar in in both periods, persistence

and amplitude increase significantly during the crisis period (the former from 12 to 18

quarters). The fact that both credit demand and supply respond more strongly and

variables take longer to return to zero following a monetary shock may be indicative

of the challenges firms and banks faced in repairing their balance sheets in the wake of

the crisis - raising equity and reducing leverage , possibly exhibiting increased aversion

of firms to take on new debt (Net Demand) and a lower willingness of banks to make

new loans (greater persistence in Net Tightening).

Counterfactual analysis

As stated in the methodology, we create counterfactuals in an attempt to quantify the

effect of credit demand channel and broad lending channel on the macroeconomy and

to estimate the effects of unconventional monetary policy via the Credit Channel. We

approach the construction of counterfactual situations in two ways:

1. In the reduced form VAR, alternately setting the coefficients in front of the credit

variables to zero and creating new impulse responses. We do this to block the

direct link between monetary policy shocks and the credit variables.

2. Using historical decomposition - it is possible to rewrite a variable as sum of past

shocks and its initial values. By carefully constructing artificial monetary policy

shocks, and adding them to the original series, we can create a new counterfactual

series, whereby the new shocks set the shadow rate to a desired level (in our case

0.25pp)

In a slightly different manner to our approach, Ciccarelli et al. (2013) design coun-

terfactual experiments by creating a series of shocks which sets the impulse response

14

of a credit variable to a monetary policy shock to zero at all lengths of the horizon.

In comparison to our methodology, we believe their approach leads to more plausible

inference, as it also accounts for indirect affects of the monetary policy shock through

the other macroeconomic variables.

The construction of any such ex-post counterfactual experiments is subject to in-

terpretation. Therefore, we devote an entire section, Evaluation, to setting out the

main critiques of such methods, and why the conclusions drawn from these methods

should be taken with caution and interpreted accordingly.

5.4 Shutting down the credit demand channel

Figures 8 and 11 present impulse response functions obtained by alternatively shut

down the reaction of credit demand and credit supply variables in our reduced VAR

model. We treat this as a first approximation to view the effect of monetary policy

acting on the real economy via the Credit Channel.

Figure 8 plots the original IRF (blue) with both demand and supply sides of the

credit channel operating as usual. The second IRF (purple) is when the coefficient

corresponding to the monetary policy shock, in front the credit demand variable, is set

to zero. Note again, that this does not imply the entire IRF itself will be zero - this

only slightly reduces the magnitude of the response. This is due to indirect effects from

monetary policy affecting the factor variables, and the dynamics of the factor variables

subsequently feeding in the credit variables.

The differences in the impulse responses of the macro variables obtained from shut-

ting down the credit demand channel are fairly small. When plotting the 90% con-

fidence bands, the counterfactuals do not appear to be statistically significant. We

would interpret this as the demand side of the Credit Channel as being small, and thus

being a limited source of amplification of monetary policy shocks. In turn, this might

imply that changes in firms’ demand for loans due to monetary policy shocks do not

have significant macroeconomic effects.

5.5 Shutting down the credit supply channel

An important result from our counterfactual exercise is that the supply side of the

Credit Channel appears to be much stronger than the demand side, both in terms of

its relative power over the other side of the channel, and in terms of its amplification

15

of monetary shocks on the macro-economy. We observe this in two ways. When we

shut down the supply side, we remove the majority of the response of the SLOS supply

variable to a monetary policy shock. Furthermore, a very large part of the magnitude

and persistence of the shock is also removed.

In Figure 12, we see that there are substantial changes in the impulse response func-

tions of the selected macroeconomic variables when the broad channel is shut down,

as compared to when the channel active. Output (as captured here by Industrial

Production), Unemployment and private business spending (Inventories and Fixed In-

vestment) show greatly reduced amplitudes when we remove the broad lending channel.

This indicates that credit supply is significant in amplifying monetary policy shocks to

the real economy.

5.6 Estimating the Effects of Unconventional Monetary Policy

In this part we aim to identify the impact of the Federal Reserve’s unconventional mon-

etary policies and particularly to highlight the role these measures have had on the

Credit Channel. The second method, as used by Wu & Xia(2013), is used to construct

counterfactual scenarios. We create specific shocks such that, when entered into the

system, the counterfactual shadow rate is pushed back from its negative value (captur-

ing the effects of unconventional monetary policy operations) and is set to equal the

zero lower bound (defined as 25 basis points in our simulation).

This is our attempt at showing to what extent unconventional monetary policies

have worked through the credit channel. Wu & Xia (2013), for example, find evidence

that Industrial Production and Employment would be lower today if there had been

no unconventional monetary policies.

The counterfactual series do not deviate significantly from the original, suggesting

that unconventional monetary policy has has limited effects via the credit channel.

This is consistent with the narrative that during the crisis period, and in the years af-

terwards due to the depressed economic environment - firms are not looking to borrow,

regardless of how attractive rates are, and banks are unwilling to lend and take on new

risk.

This exercise thus builds on the earlier counterfactuals developed by Wu & Xia

(2013), whose analysis suggests that economic activity (output and employment) is

16

higher as a consequence of such unconventional policies. They do not specify through

which channels these effects are realised. This question remains open to further re-

search.

6. Evaluation

Throughout our analysis, we have been relying on several assumptions of varying plau-

sibility. In this section, we will set out the limitations to our analysis and potential

weaknesses in our conclusions.

1. The Need for Cholesky Ordering: Identification of the structural shocks in re-

duced VAR models require restrictions on the contemporaneous interactions of

variables, otherwise the system becomes fully endogenous and cannot be solved.

There is no proper way, a priori, to select an objectively good variable ordering,

and this is something that has to carefully reasoned. Fortunately, in a FAVAR,

since we only shock the monetary policy instrument, we only need to make order-

ing decisions regarding monetary policy, which we order last by the assumption

that policy makers observe the state of the economy before setting policy. This

is the same assumption Soares (2011) makes when estimating a FAVAR for the

Eurozone. When we add SLOS survey variables to the FAVAR, we must make

assumptions about the ordering of credit demand and credit supply. We make

the choice to put supply after demand, and reason that banks will react faster to

monetary policy shocks than firms will do in adjusting their credit demand.

2. FAVAR(p) lag and factor Selection: With a larger data set we would have been

able to select more lags or add more factors in our FAVAR, if desired. Higher

lag order allows a more flexible fit and so offers the possibility to more closely

fit the data. However with more parameters to estimate comes the risk of higher

imprecision due to lower degrees of freedom. We are forced by our sample to

simply take the extreme end of this trade off and select one lag. One lag might

not be sufficiently flexible to capture the full dynamics of the series we attempt

to model.

3. Qualitative Results: Our SLOS variables are qualitative in nature, that is, we can

only know if credit demand and supply are increasing or decreasing, but cannot

know the magnitude of such changes. As such, our analysis can offer limited

quantitative predictions.

4. Due to data availability, our analysis is not able to disentangle the broad credit

channel into the bank lending and balance sheet channels, and leaves this topic

17

for future research.

5. Data: Due to some of the survey data series being discontinued or disaggregated,

our analysis is limited to commercial and industrial loans. Our analysis may

change once consumer loans and mortgages are accounted for, given that US

firms are relatively less bank dependent in comparison to households.

6. Non-Structural Nature of Estimated System: The part of our analysis that is

most fragile to its assumptions is the counterfactual section. Implicitly, when we

construct counterfactuals, we are assuming that every other aspect of the system

remains unchanged. In order to do so we would need to know the deep, structural

form of the economy. Any changes to macro variables is therefore vulnerable to

the Lucas Critique (1976) - typically this is invoked to say that human behaviour

is not independent of policy, so policy changes must account for the most funda-

mental structures that govern decision making - preferences, technology etc. The

classic example used is the following proposal: Given Fort Knox has never been

robbed, the guards are useless, so we can remove the guards, and not be robbed.

Such a policy would be vulnerable to the Lucas critique given that agents would

observe the new state of the world (no guards) and change their behaviour from

what was previously seen. We have no way to know that when we shut down a

credit channel, or create an artificial shock to the system to create our counter-

factual, we are making the same kind of we have never been robbed therefore...

statement. As such, our counterfactual exercises should always be viewed with

this critique in mind.

7. Conclusion

In this paper, we have contributed to a methodology for identifying the credit

channel in US monetary policy transmission, consistent with periods of zero lower

bound. First, we have introduced FAVAR estimation technique to enhance the

methodology of Ciccarelli et. al.(2013) on identifying the credit channel in mon-

etary policy transmission mechanism. Secondly, our analysis incorporated the

shadow rate (Wu and Xia, 2013) providing a more robust measure of monetary

policy operations in a low interest rate environment.

Through specific counterfactual responses, we attempt to identify the contribu-

tion of the credit channel transmission mechanisms on the macroeconomic vari-

ables in the period from 1991Q4 to 2013Q4. Our analysis suggests that the broad

18

credit channel is the stronger of the two and is a notable driver of the macroeco-

nomic effects that arise from monetary policy operations. This result is line with

the findings of recent papers (Amiti, 2013; Chodorow-Reich, 2013; Ciccarelli et

al., 2013) on the macroeconomic effects of bank lending shocks.

Furthermore, we replicated the hypothetical what if? exercise, developed by Wu

and Xia (2013), to examine what would have happened if the monetary authority

had not adopted unconventional measures. Our study does not find evidence of

the effects of unconventional monetary policies on credit demand or supply vari-

ables in the zero lower bound period so far, suggesting that the policies do not

support economic activity through the credit channel.

However, one must note that our constructed counterfactuals are subject to the

Lucas critique. For this reason, we view our results with a degree of scepticism,

and more as a single piece of information that would only work in combination

with the results other investigations - such as micro founded DSGE model simu-

lations.

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Annex

The following tables list the mnemonics, short names and transformations for the 97

macroeconomic series used in the paper. The transformation codes are: 1 no trans-

formation; 2 first difference; 4 logarithm; 5 first difference of logarithm. All series

are from the Federal Reserve of St. Louis Economic Database (FRED). Slow-moving

variables are marked with 1.

Results are presented through impulse response functions, historical decompositions

and counterfactual analyses. All impulse response functions are standardized, and

result from a 25 basis points monetary policy tightening.

21

Table 1: Macroeconomic dataNo. Mnemonic Short name Transformation Slow

Real output and income

1 CBI Change in Private Inventories 1 12 GDPC96 Real Gross Domestic Product 5 13 FINSLC96 Real Final Sales of Domestic Product 5 14 CIVA Corporate Inventory Valuation Adjustment 1 15 CP Corporate Profits After Tax 5 16 CNCF Corporate Net Cash Flow 5 17 GDPCTPI Gross Domestic Product: Chain-type Price Index 5 18 FPI Fixed Private Investment 5 19 GSAVE Gross Saving 5 110 PRFI Private Residential Fixed Investment 5 111 INDPRO Industrial Production Index 5 112 NAPM ISM Manufacturing: PMI Composite Index 1 113 HCOMPBS Business Sector: Compensation Per Hour 5 114 HOABS Business Sector: Hours of All Persons 5 115 RCPHBS Business Sector: Real Compensation Per Hour 5 116 ULCBS Business Sector: Unit Labor Cost 5 117 COMPNFB Nonfarm Business Sector: Compensation Per Hour 5 118 HOANBS Nonfarm Business Sector: Hours of All Persons 5 119 COMPRNFB Nonfarm Business Sector: Real Compensation Per Hour 5 120 ULCNFB Nonfarm Business Sector: Unit Labor Cost 5 1

Employment and hours

21 UEMPLT5 Civilians Unemployed - Less Than 5 Weeks 5 122 UEMP5TO14 Civilian Unemployed for 5-14 Weeks 5 123 UEMP15OV Civilians Unemployed - 15 Weeks and Over 5 124 UEMP15T26 Civilians Unemployed for 15-26 Weeks 5 125 UEMP27OV Civilians Unemployed for 27 Weeks and Over 5 126 NDMANEMP All Employees: Nondurable Goods 5 127 MANEMP Employees on Nonfarm Payrolls: Manufacturing 5 128 SRVPRD All Employees: Service-Providing Industries 5 129 USTPU All Employees: Trade, Transportation & Utilities 5 130 USWTRADE All Employees: Wholesale Trade 5 131 USTRADE All Employees: Retail Trade 5 132 USFIRE All Employees: Financial Activities 5 133 USEHS All Employees: Education & Health Services 5 134 USPBS All Employees: Professional & Business Services 5 135 USINFO All Employees: Information Services 5 136 USSERV All Employees: Other Services 5 137 USPRIV All Employees: Total Private Industries 5 138 USGOVT All Employees: Government 5 139 USLAH All Employees: Leisure & Hospitality 5 140 AHECONS Average Hourly Earnings: Construction 5 141 AHEMAN Average Hourly Earnings: Manufacturing 5 142 AHETPI Average Hourly Earnings: Total Private Industries 5 143 AWOTMAN Average Weekly Hours: Overtime: Manufacturing 1 144 AWHMAN Average Weekly Hours: Manufacturing 1 1

22

Table 2: Macroeconomic dataNo. Mnemonic Short name Transformation Slow

Housing starts and sales

45 HOUST Housing Starts: New Privately Owned Housing Units Started 4 046 HOUSTNE Housing Starts in Northeast Census Region 4 047 HOUSTMW Housing Starts in Midwest Census Region 4 048 HOUSTS Housing Starts in South Census Region 4 049 HOUSTW Housing Starts in West Census Region 4 050 HOUST1F Privately Owned Housing Starts: 1-Unit Structures 4 051 PERMIT New Private Housing Units Authorized by Building Permit 4 0

Credit aggregates

52 NONREVSL Total Nonrevolving Credit Outstanding, SA, Billions of Dollars 5 053 USGSEC U.S. Government Securities at All Commercial Banks 5 054 OTHSEC Other Securities at All Commercial Banks 5 055 TOTALSL Total Consumer Credit Outstanding 5 056 BUSLOANS Commercial and Industrial Loans at All Commercial Banks 5 057 CONSUMER Consumer (Individual) Loans at All Commercial Banks 5 058 LOANS Total Loans and Leases at Commercial Banks 5 059 LOANINV Total Loans and Investments at All Commercial Banks 5 060 INVEST Total Investments at All Commercial Banks 5 061 REALLN Real Estate Loans at All Commercial Banks 5 062 BOGAMBSL Board of Governors Monetary Base 5 063 TRARR Board of Governors Total Reserves 5 064 NFORBRES Net Free or Borrowed Reserves of Depository Institutions 1 0

Monetary aggregates

65 M1SL M1 Money Stock 5 066 CURRSL Currency Component of M1 5 067 CURRDD Currency Component of M1 Plus Demand Deposits 5 068 DEMDEPSL Demand Deposits at Commercial Banks 5 069 TCDSL Total Checkable Deposits 5 0

Interest rates and yields

70 TB3MS 3-Month Treasury Bill: Secondary Market Rate 1 071 TB6MS 6-Month Treasury Bill: Secondary Market Rate 1 072 GS1 1-Year Treasury Constant Maturity Rate 1 073 GS3 3-Year Treasury Constant Maturity Rate 1 074 GS5 5-Year Treasury Constant Maturity Rate 1 075 GS10 10-Year Treasury Constant Maturity Rate 1 076 MPRIME Bank Prime Loan Rate 1 077 AAA Moody’s Seasoned Aaa Corporate Bond Yield 1 078 BAA Moody’s Seasoned Baa Corporate Bond Yield 1 079 sTB3MS TB3MS - FEDFUNDS 1 080 sTB6MS TB6MS - FEDFUNDS 1 081 sGS1 GS1 - FEDFUNDS 1 082 sGS3 GS3 - FEDFUNDS 1 083 sGS5 GS5 - FEDFUNDS 1 084 sGS10 GS10 - FEDFUNDS 1 085 sMPRIME MPRIME - FEDFUNDS 1 086 sAAA AAA - FEDFUNDS 1 087 sBAA BBB - FEDFUNDS 1 0

23

Table 3: Macroeconomic dataNo. Mnemonic Short name Transformation Slow

Exchange rates

88 EXSZUS Switzerland / U.S. Foreign Exchange Rate 5 089 EXJPUS Japan / U.S. Foreign Exchange Rate 5 0

Producer price indices

90 PPIACO Producer Price Index: All Commodities 5 191 PPICRM Producer Price Index: Crude Materials for Further Processing 5 192 PPIFCF Producer Price Index: Finished Consumer Foods 5 193 PPIFCG Producer Price Index: Finished Consumer Goods 5 194 PFCGEF Producer Price Index: Finished Consumer Goods Non Foods 5 195 PPIFGS Producer Price Index: Finished Goods 5 196 PPICPE Producer Price Index Finished Goods: Capital Equipment 5 197 PPIENG Producer Price Index: Fuels & Power 5 198 PPIIDC Producer Price Index: Industrial Commodities 5 199 PPIITM Producer Price Index: Supplies & Components 5 1

Consumer price indices

100 CPIAUCSL Consumer Price Index For All Urban: All Items 5 1101 CPIUFDSL Consumer Price Index for All Urban: Food 5 1102 CPIENGSL Consumer Price Index for All Urban: Energy 5 1103 CPILEGSL Consumer Price Index for All Urban: All Items Less Energy 5 1104 CPIULFSL Consumer Price Index for All Urban: All Items Less Food 5 1105 CPILFESL Consumer Price Index for All Urban: All Items Less Food & Energy 5 1

Commodity prices

106 OILPRICE Spot Oil Price: West Texas Intermediate 5 1

New orders, sales, inventories

107 NAPM Purchasing Managers Index (SA) 1 1108 NAPMNOI NAPM New Orders Index (%) 1 1109 NAPMSDI NAPM Vendor Deliveries Index (%) 1 1110 PMNV NAPM Inventories Index (%) 1 1

Table 4: Senior Loan Officer Questions

Channel SLOS Question

Broad credit channel Over the past three months, how have your banks credit standards changed for approvingapplications for C&I loans or credit lines other than those to be used to financemergers and acquisitions to large and middle-market firms changed?

Credit demand channel Apart from normal seasonal variation how has demand for C&I loans changed over the pastthree months?

24

Figure 1: Impulse responses to a 25 basis points monetary policy tightening for a selectionof macro series, pre-crisis, no survey variables included, 3 factors, 1 lag, 90% confidencebands

25

0 12 24−0.04

−0.02

0

0.02

0.04

0.06

0.08

0.1Turbo Rate

0 12 24−0.8

−0.6

−0.4

−0.2

0

0.2Industrial Production

0 12 24−0.5

0

0.5CPI

0 12 24−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2GDP Deflator

0 12 24−0.5

0

0.5

1

1.5

2Unemployment

0 12 24−0.06

−0.04

−0.02

0

0.02

0.04

0.06Housing Starts

0 12 24−0.1

−0.08

−0.06

−0.04

−0.02

0

0.02Change in Private Inventories

0 12 24−0.8

−0.6

−0.4

−0.2

0Fixed Private Investment

Figure 2: Impulse responses to a 25 basis points monetary policy tightening for a selectionof macro series for the entire sample, no survey variables included, 3 factors, 1 lag, 90%confidence bands

26

Figure 3: Quarterly changes in credit demand (blue) and net tightening (red),as reported bythe Senior Loan Officer Survey, for the period 1991Q4-2013Q4. Figures in net percentages

Figure 4: Impulse responses to a 25 basis points monetary policy tightening for creditdemand and credit supply variables, pre-crisis, 3 factors, 1 lag, 90% confidence bands

27

Figure 5: Impulse responses to a 25 basis points monetary policy tightening for a selectionof macro series, pre-crisis, including survey variables, 3 factors, 1 lag, 90% confidence bands

Figure 6: Impulse responses to a 25 basis points monetary policy tightening for creditdemand and credit supply variables, including crisis, 3 factors, 1 lag, 90% confidence bands

28

Figure 7: Impulse responses to a 25 basis points monetary policy tightening for a selectionof macro series, including crisis, including survey variables, 3 factors, 1 lag, 90% confidencebands

Figure 8: Impulse responses to a 25 basis points monetary policy tightening for creditdemand and credit supply variables,shutting down demand channel pre-crisis, 3 factors, 1lag

29

Figure 9: Impulse responses to a 25 basis points monetary policy tightening for selectedmacro variables,shutting down demand channel pre-crisis, 3 factors, 1 lag

Figure 10: Impulse responses to a 25 basis points monetary policy tightening for selectedmacro variables,shutting down demand channel, including crisis, 3 factors, 1 lag

30

Figure 11: Impulse responses to a 25 basis points monetary policy tightening for creditdemand and credit supply variables,shutting down supply channel, pre-crisis, 3 factors, 1lag

Figure 12: Impulse responses to a 25 basis points monetary policy tightening for selectedmacro variables,shutting down supply channel pre-crisis, 3 factors, 1 lag

31

Figure 13: Impulse responses to a 25 basis points monetary policy tightening for selectedmacro variables,shutting down supply channel, including crisis, 3 factors, 1 lag

32

Shadow Rate

Demand

Supply

Figure 14: Counterfactual series using Historical decomposition: Credit demand and creditsupply - actual values (blue); counterfactuals (pink) by setting the shadow rate at the zerolower bound st = 0.25, Date 0 = start of ZLB period, Dec. 2008.

33

Figure 15: Counterfactual series using Historical decomposition: Credit supply - actualvalues (blue); counterfactuals (pink) by setting the shadow rate at the zero lower boundst = 0.25

34

−5

0

5Loadings on Factor 1

−5

0

5Loadings on Factor 2

−5

0

5Loadings on Factor 3

−1

0

1

Loadings on Demand for Loans variable

−1

0

1

Loadings on Broad Credit Supply variable

−1

0

1

Loadings on Turbo Rate

Real OutputEmployment and HoursHousing Starts and SalesInventories and OrdersExchange RatesInterest Rates and SpreadsMoney and Credit aggregatesPrice IndexesHourly Earnings

Figure 16: Factor loadings, 3 factors, 1 lag, pre-crisis

35