The Bank Lending Channel: a FAVAR Analysis?
University of Texas at Dallas
Scott J. Dressler
University of Texas at Dallas
We examine the bank lending channel (BLC) of monetary transmission in a factor-
augmented vector autoregression (FAVAR). A FAVAR exploits a large number of
macroeconomic indicators to identify monetary policy shocks, and we add commonly
used lending aggregates as well as lending data at the bank level. While our results
suggest that the BLC is stronger than previously thought, this feature is not robust.
In addition, our results indicate a di¤use response to monetary innovations from indi-
vidual banks grouped across asset sizes and loan components. This suggests that other
bank characteristics could improve the identi…cation of the BLC.
Keywords: Bank Lending Channel; FAVAR; Monetary Policy
JEL: E51, E52, C32
?We are grateful to seminar participants at the Federal Reserve Bank of Dallas, and to Marc Giannoni
for providing us with some of our data. All errors and omissions are those of the author.
yCorresponding author. Address: University of Texas at Dallas; School of Economic, Political and Policy
Sciences; 800 W. Campbell Rd.; Richardson, TX 75080. Phone: (972) 883-2306. Fax: (972) 883-6486.
Since Bernanke and Blinder’s (1992) observation that signi…cant movements in aggregate
bank lending volume follow changes in the stance of monetary policy, the bank lending
channel (henceforth, BLC) has been a prominent mechanism in the literature on monetary
transmission. The BLC focuses on the balance sheets of commercial banks and assumes that
insured, reservable deposits and other forms of external loan …nance (e.g. time deposits, CDs,
etc.) are not perfect substitutes due to the higher costs of acquiring the latter. Therefore, a
monetary contraction resulting in less reservable deposits should result in a decrease in the
supply of loans.
Building upon the initial intuition for the BLC, the literature has since stressed cross-
sectional di¤erences among commercial banks’ balance-sheets as well as loan components.
Kashyap and Stein (1995, 2000) considered bank assets and liquidity positions as aggregating
criteria and …nd that increases in the Federal funds rate are followed by signi…cant declines
in lending volume for the smallest (in terms of assets) and least liquid banks.1Den Haan
et al. (2007) consider loan components aggregated across banks and …nd that real estate
and consumer loans decline sharply in response to a monetary contraction while commercial
and industrial (C&I) loans increase.2While Perez (1998), Ashcraft (2006), and others have
questioned the macroeconomic signi…cance of the BLC in monetary transmission, Kashyap
and Stein (1995, 2000) and Den Haan et al. (2007) remain as evidence for its existence.
This evidence is not without its limitations. For example, the Federal funds rate com-
monly used as the monetary policy instrument may not appropriately identify monetary
policy innovations. In addition, aggregating bank lending across either asset categories or
loan components may contaminate the true responses of individual banks who are respond-
ing to both bank-speci…c and aggregate sources of ‡uctuations simultaneously. Previous
1Kishan and Opiela (2000) further …nd that banks with the weakest capital positions are the most
responsive to monetary policy.
2The authors suggest that the perverse response of C&I loans could still be consistent with the BLC due
to a bank’s preference for the relative safety and term of a C&I loan rather than a longer-term asset (such
as a real estate loan).
analyses which either support or refute the BLC are in some way subject to these limita-
tions. The goal of this paper is to put these limitations to the test by examining the lending
response of commercial banks in a new and increasingly popular empirical framework - a
factor-augmented vector autoregression (FAVAR).
A FAVAR, which combines standard structural VAR methods with factor analysis, ex-
ploits a large number of time series and summarizes the information into a relatively small
set of estimated indexes (i.e. factors).This provides many desirable properties for an
analysis of the BLC. First, utilizing a large data set of macroeconomic variables like those
used by central banks is important when properly identifying monetary policy innovations.
Bernanke et al. (2005) (henceforth, BBE) motivate the use of a FAVAR in their analysis
of the macroeconomic e¤ects of monetary policy shock by arguing that the measurement of
policy innovations is likely to be contaminated by limiting the analysis to a small number
of comprehensive macroeconomic variables.3Second, one does not need to take a stand on
speci…c observables (such as industrial production or real GDP) to correspond to theoretical
concepts (such as economic activity) because a FAVAR summarizes these concepts using large
amounts of economic information. Finally, a FAVAR provides impulse responses for every
variable in the conditioning set, as well as a decomposition of their individual ‡uctuations
into those due to aggregate factors and those due to individual, idiosyncratic innovations.
Our FAVAR framework considers the set of macroeconomic indicators used by BBE, and
extends this data by appending a variety of commercial-bank lending variables. First, total
loan growth and growth in loan components are aggregated up to the total banking system (as
in Bernanke and Blinder, 1992 and Den Haan et al., 2007) as well as up to groups according
to asset size (as in Kashyap and Stein, 1995 and 2000).4While these variables illustrate how
aggregate bank lending responds to an improved identi…cation of monetary policy shocks,
3Imperfectly controlling for the information central bankers may have is exactly Sim’s (1992) critical
interpretation of an increase in aggregate prices in response to a monetary contraction (i.e.
Puzzle) observed in traditional VAR analyses.
4Following Kashyap and Stein (1995, 2000), we consider the asset groups to be banks with assets within
the 95th percentile or less (small), banks with assets within the 95th and 99th percentile (medium), and
banks with assets within the 99th percentile or more (large).
we also consider a large amount of lending data at the individual-bank level. This allows
us to disentangle the ‡uctuations in bank-level lending data which are due to aggregate
macroeconomic factors (such as a change in monetary policy) from those that are due to
bank-speci…c conditions. To our knowledge, this is the …rst analysis which considers purely
disaggregated lending data within the same framework as their commonly used aggregates,
and compares the responses of individual and aggregate lending in response to monetary
Our …ndings are twofold. First, when appending the FAVAR of BBE with aggregated
lending data, we …nd that total, C&I, and individual consumer loan growth all signi…cantly
decline after a monetary policy contraction for the entire banking sector as well as bank
groups categorized by asset size. While this suggests that the BLC e¤ects more than just
the smallest banks (asset wise) and is stronger than previously thought, this result is not
robust when considering post-1984 data. Second, when appending the FAVAR further with
a balanced panel of bank-level lending data, we …nd that their individual responses to a
monetary policy innovation are quite di¤use. There are almost as many banks who increase
lending in response to a monetary innovation as those who decrease, and this result remains
when controlling for bank groups and loan components. A main reason for these di¤use
responses is that macroeconomic ‡uctuations explain on average between 8 and 22 percent
of the variation in individual bank lending across our various loan categories. Therefore,
most of the variation in individual bank-lending re‡ects bank-speci…c shocks to which banks
respond immediately. Nonetheless, when considering lending aggregates comprised of only
those banks we observe individually, there are signi…cant declines for all bank groups in one
or more loan components, and these declines remain when employing post-1984 data.
A goal of the empirical research in this literature is to help target and identify the
important features of intermediation in monetary transmission that prove useful to theorists
by indicating which features of banking must be incorporated into their environments. The
picture our results paint is that while particular measures of the BLC are strengthened by
our FAVAR framework, the large degree of heterogeneity observed in the individual-bank
responses cast doubt on the notion that the BLC is stronger for banks based on asset size or
loan components. This implies that other bank characteristics might prove more suitable to
di¤erentiate banks which display a BLC e¤ect. For example, Cetorelli and Goldberg (2009)
show that the degree of globalization of a commercial bank could matter for the BLC because
globalized banks can activate foreign capital markets to insulate themselves from domestic
Our analysis of bank-level lending data is related to the analysis of Boivin et al. (2009)
who append the FAVAR of BBE with sector-speci…c price data with the goal of delineating
the e¤ects of sector-speci…c price shocks from aggregate price shocks. Their results sug-
gest that individual prices appear less persistent than their aggregate due to sector-speci…c
volatility. Once the responses of the individual prices to monetary policy shocks are iden-
ti…ed, they uncover a degree of persistence which accords with that observed in aggregate
prices. Therefore, our analysis of the BLC with a large number of individual banks included
in the FAVAR allows us to characterize responses to local shocks versus aggregate shocks in
the same manner as their analysis of prices.
The rest of the paper is organized as follows. Section 2 outlines the formulation and
estimation of the FAVAR. Section 3 discusses the data. Section 4 presents our empirical
results by …rst detailing the impulse responses of loan aggregates to a monetary policy shock,
and then examining the characteristics of disaggregated loan data. Section 5 concludes.
2. The FAVAR
Our implementation of the FAVAR follows BBE, and a brief outline of the framework
is as follows. Assume the economy is a¤ected by a vector Ctof common components. For
example, a measure of the stance of monetary policy is a common component, and we follow
the literature by assuming that this stance is measured by the Federal funds rate (Rt).
The remaining dynamics in‡uencing all other variables in the data set are captured by a
K?1 vector of unobserved factors Ft, where K is relatively small. These unobserved factors
capture ‡uctuations in general economic concepts such as economic activity, aggregate prices,
credit conditions, etc., that cannot be easily represented by a few time series but rather are
re‡ected in a wide range of economic variables.
We assume that the joint dynamics of Ftand Rtare given by
Ct= ?(L)Ct?1+ ?t
tRt] and ?(L) is a conformable lag polynomial of in…nite order which can
contain a priori restrictions as in the structural VAR literature. The error term ?tis i.i.d.
with zero mean and covariance matrix Q. While (1) is a VAR in Ct, it cannot be directly
estimated because the factors comprising Ftare unobserved.
Since the factors in Ctare interpreted as forces a¤ecting many economic variables, one
can potentially use a large set of observed ‘informational’ series to infer something about
them. Let Xtdenote the N ?1 vector of these informational variables, where N is relatively
large. It is assumed that Xtis related to all common components according to
Xt= ?Ct+ et
where ? is an N ?(K + 1) matrix of factor loadings. The N ?1 vector etcontains the zero-
mean, series-speci…c components that are uncorrelated with Ct, but allowed to be serially
correlated and weakly correlated across indicators. Equation (2) re‡ects that Ctrepresents
pervasive forces which drive the common dynamics of Xt. Conditional on Rt, the variables
in Xtare thus noisy measures of the underlying unobserved factors Ft. BBE note that the
implication of Xtdepending only on current factors is not restrictive in practice, as Ftcan
be interpreted as including arbitrary lags of the fundamental factors.
Estimation of the above model involves a two-step principal components approach. In
the …rst step, principal components are extracted from Xtto obtain consistent estimates of
the common factors. In the second step, the Federal funds rate is added to the estimated
common factors and the data set is used to estimate (1). In particular, estimation of our
model follows Boivin et al. (2009), who slightly di¤er from the estimation described by BBE
insofar that it is assumed that Rtis one of the factors in the …rst-step. This guarantees that
the latent factors recover common dynamics not captured by the Federal funds rate.5
Our analysis begins with the set of macroeconomic indicators considered in the initial
FAVAR analyses of BBE and Boivin et al.6The data is quarterly from 1976:1 to 2005:3 and
includes measures of industrial production, price indices, interest rates, employment, and
other key macroeconomic and …nancial variables which have been found to contain useful
information in identifying the state of the economy and monetary policy shocks. All data
are transformed to induce stationarity as in previous FAVAR studies.
We extend this data set by including various types of bank lending data to fully assess
the power of the FAVAR and the response of lending to monetary policy. Our lending
data is taken from the Consolidated Report of Condition and Income (Call Reports) that
all insured banks submit to the Federal Reserve. For each commercial bank, data on total
loans, total C&I, total real estate loans, and individual loans were collected following the
detailed instructions on forming consistent time series attributable to Kashyap and Stein
(2000). For each quarter, we used total asset holdings of the commercial banks to assign
each bank into one of three size categories: banks with total assets below the 95thpercentile
(small banks), banks with total assets between the 95thand 99thpercentile (medium banks),
and banks with total assets above the 99thpercentile (large banks). To retain comparability
with previous BLC studies, we use these asset categories to construct a coarse disaggregation
5See Boivin et al. (2009) for details.
6We are grateful to Marc Giannoni for providing us with this data.
of the commercial banking data. In particular, we use the lending data to construct loan
growths for all loan components aggregated up to the entire sector as well as the three asset
groups. However, since the FAVAR can handle large amounts of data, we also keep individual
banks separate and construct a balanced panel of loan growths using asset size categories
and loan types to determine if there are any common movements in banks that di¤er across
In order to arrive at a manageable data set for our FAVAR analysis, we had to apply sev-
eral …lters on the individual bank-level data. In particular, our balanced panel of commercial
banks initially consisted of 4743 individual banks. Of these, 219 banks were removed because
their bank size was not consistent throughout the sample. The resulting data set consisted
of 18 large banks, 24 medium banks, and 4482 small banks. Since the small banks are still
too numerous, the data set we settled on consists of a random selection of 10 percent of the
small bank population.7We then used these banks to construct time series for their loan
growths in the exact same way as in the loan aggregates. In addition, in order to directly
compare the individual bank responses with some aggregate measure of their response, we
constructed loan aggregates similar to those constructed for all banks but only using the
banks we observe in our balanced panel.
In summary, we analyze three data sets which share the macroeconomic indicators found
in previous studies. The …rst data set includes aggregated lending data following Kashyap
and Stein (2000) to discover how aggregate lending responds to an improved identi…cation
of monetary policy shocks delivered by the FAVAR. The second data set adds our balanced
panel of individual bank lending to discover if there is any impact on the identi…cation of
monetary policy. The third and …nal data set replaces the loan aggregates with aggregated
loan data constructed exclusively from the individual banks that are in our balanced panel in
order to have a direct comparison between loan aggregates and the individual bank lending
7The estimation was conducted for several di¤erent samples of small banks to ensure robustness of the
4. Estimation Results
We estimate the above system (1) and (2) for four di¤erent FAVARs which di¤er in the
type of bank lending (total, C&I, real estate, and individual). For each FAVAR, we chose the
size of factors Ftafter some experimentation to ensure that our conclusions are not a¤ected
by additional latent factors.8All models use 4 quarterly lags in estimating (1).
The …rst subsection focuses on the response of aggregated lending data to a monetary
policy shock, while the second subsection focuses on the characteristics and behavior of the
disaggregated lending data.
Following BBE, we assume that the Federal funds rate may respond to contempora-
neous ‡uctuations in estimated factors, but that none of the latent common components
can contemporaneously respond to monetary policy shocks. This is the FAVAR extension
of the standard recursive identi…cation of monetary policy shocks in conventional VARs,
which has been used by Den Haan et al. (2007) and others. Note that in contrast to VARs,
the macroeconomic indicators (Xt) are allowed to contemporaneously respond to monetary
shocks. We can therefore disentangle monetary policy shocks from the other macroeconomic
The responses of our lending aggregates to an unexpected (25 basis point) increase of
the Federal funds rate are illustrated in Figure 1 when using the data set with only loan
aggregates, and Figure 3 when using the data set with loan aggregates as well as our balanced
panel of individual banks. Each panel in the …gures illustrates the response of a particular
loan component for loans aggregated across all banks as well as the three asset groups. A
diamond on the response paths indicate that the impulse response at that particular time
horizon is signi…cantly di¤erent than zero at the 90 percent level.9As both …gures indicate,
8Our FAVARs for Total and Real Estate loans required 5 latent factors, while C&I and Individual loans
9Con…dence intervals were constructed using a bootstrapping method and employs a similar metric as
there are signi…cant and persistent declines in Total, C&I, and Individual loans in response
to a monetary policy shock for all bank groups as well as the entire sector. Since previous
analyses only …nd a signi…cant BLC in either the smallest (asset-wise) or least liquid banks,
this result suggests that the BLC is actually stronger than previously thought under a FAVAR
identi…cation of monetary policy shocks. Another similarity between Figures 1 and 3 is that
although there are initial declines in the growth rates of Real Estate Loans, barely none of
these responses are statistically signi…cant.
While these results suggest a stronger BLC than previously thought, we …nd that they
are not a robust feature of the data. In particular, due to the recent research providing
evidence of widespread instability in many macroeconomic series, of changes in monetary
policy over our full sample, and a decline in overall macroeconomic volatility around 1984,
we re-estimated our FAVARs using post-1984 data.10Figures 2 and 4 illustrate the impulse
responses using the same variables discussed above under the truncated time dimension.
While the responses under the full sample are somewhat similar and accord with the BLC
literature, this exercise uncovers a large disparity between the data sets with and without
the bank-speci…c panel as well as a reduction in the signi…cance of the responses. In fact,
most loan components actually increase (albeit, insigni…cantly) after a monetary contraction.
The only loan component which retains a signi…cant BLC is Individual loans in Figure 4,
which accords with the results of Den Haan et al. (2007) who …nd the largest BLC e¤ect in
aggregate consumer loans.11
We interpret our results for aggregate lending as follows. First, the results under the full
data sample suggest a BLC e¤ect for many bank groups and loan categories which further
suggests a stronger BLC than one which only in‡uences the banks with the least amount
DenHaan et al. (2007).
10See Stock and Watson (1996, 2002) for evidence concerning the instability in VARs. See Bernanke
and Mihov (1998), Clarida et al. (2000), Cogley and Sargent (2001, 2005), Boivin (2006), and Boivin and
Giannoni (2002, 2006) for analyses concerning the changes in monetary policy and decline in macroeconomic
11It should be noted that our results for real estate loans also mimic the results of Den Haan et al. (2007),
but fail to be signi…cant.
of assets. Second, the decline in a signi…cant BLC e¤ect for the post-1984 sample suggests
a breakdown in the classic BLC story due to globalization, …nancial innovations, etc. For
example, Memmel and Schertler (2009) uncover a decline in the asset-liability dependency
of commercial banks in Germany and attribute this result to changes in regulatory bank
capital. Finally, we interpret the discrepancy between our post-1984 results under the two
data sets as suggesting that the inclusion of our balanced panel of individual lending improves
the FAVAR identi…cation of monetary policy shocks. Therefore, while the BLC e¤ect has
undoubtedly weakened as suggested by the literature, it still remains in certain components
of aggregated lending as suggested by Den Haan et al. (2007).
4.2. Disaggregated Lending
This section turns the analysis to the bank-speci…c lending data which was used along
with the loan aggregates for estimating the system (1) and (2). For all loan growth series
considered, (2) implies
where xitis the quarterly change in loan growth for bank i. The ‡uctuations for all banks
due to the macroeconomic factors are represented by the common components Ct which
have a di¤use e¤ect on the individual banks due to di¤erences in ?i, while the bank-speci…c
‡uctuations are captured by eit.
We detail some summary statistics on the average volatility of loan components and their
corresponding aggregates in Table 1. It should be noted that the corresponding aggregates
are not the aggregates discussed in the previous section, but the aggregation of banks that
appear in our bank-level panel. This comparison serves to illustrate potential aggregation
e¤ects among the individual banks.
The …rst column of Table 1 suggests a large amount of average volatility in our bank-level
data. This volatility is decomposed into volatility stemming from common macroeconomic
and speci…c factors, and the R2statistic measures the fraction of the variance in aggregate
lending explained by the common components. The results suggest that loan ‡uctuations
stemming from aggregate or common shocks make up a very small amount of the average
volatility in our lending data. For example, when considering banks with assets less than the
95th percentile, bank-speci…c shocks account on average for 76 percent of their ‡uctuations
in total lending and as much as 92 percent of their ‡uctuations in lending components.
When comparing these volatilities with their corresponding aggregates, one …nds a large
reduction in volatility due to a reduction in the bank-speci…c component. The R2statistics
now state that 75 percent or more of the ‡uctuations in these variables are attributable to
‡uctuations in macroeconomic components. While this comparison is the most stark for the
smallest bank group, similar comparisons for all bank groups and all loan components report
a reduction in volatility due to aggregating the bank-level data as well as an increased R2.
Quite naturally, these results suggests that disturbances arising at the individual bank level
tend to cancel each other upon aggregation.
Returning focus to the bank-level data, Figure 5 illustrates a strongly positive correlation
between the macroeconomic and bank-speci…c components of lending volatility. While the
…gure considers all banks, this positive relationship between the volatility of the idiosyncratic
shocks (Sd(ei)) and the volatility of the common component (Sd(?0
iCt)) would remain if we
considered banks groups separately. Among the loan components, the tightest relationships
are among C&I and Real Estate loans, with a weaker relationship among Individual loans and
the total. All slope coe¢cients are statistically di¤erent from zero at the 95 percent level, and
are corrected for possible heteroscedasticity.12From this perspective, banks with the highest
idiosyncratic volatility respond the strongest to macroeconomic shocks. Therefore, whatever
characteristics which help banks smooth over individual shocks will also help smooth over
Our …nal analysis of the bank-level data is to document how loan growth responds to
12The respective t-statistics for the slope coe¢cients are 10.5, 22.9, 26.7, and 12.4.
bank-speci…c and macroeconomic disturbances. These impulse responses are illustrated in
Figures 6 through 8 for large, medium, and small banks, respectively. The left panels of
the …gures report the response of each of the individual banks to an adverse (one standard
deviation) shock to its bank-speci…c component. The solid lines represent an unweighted
average response. Across all bank groups and loan components, lending responds sharply
and promptly to bank-speci…c disturbances. There is very little persistence in the response
of all banks to these individual disturbances, and they quickly reach a new equilibrium.13
While bank-speci…c shocks rapidly shift the loan growth of individual banks to a new
level, the response to macroeconomic shocks are quite di¤erent. The middle panels of the
…gures illustrate the response of each bank group and loan component to an innovation (of
minus one standard deviation) to its common component ?0
iCt. These …gures suggest a large
amount of sluggishness in the response to macroeconomic disturbances, and this persistence
is shared by all bank groups and all loan components. In particular, the …gures illustrate that
all banks behave quite similarly to a common macroeconomic shock. It should be noted that
this exercise fails to identify a speci…c structural macroeconomic shock and instead illustrates
the response to a combination of macroeconomic shocks. Nonetheless, the response to these
shocks strongly contrast with the responses to bank-speci…c shocks.
We …nally turn to the e¤ects of monetary policy on our bank-lending panel. The iden-
ti…cation of monetary policy shocks is accomplished the same as in the previous section
focusing on the aggregated lending data, and the results are illustrated in the third columns
of Figures 6 through 8 for our three bank groups. The thick solid lines again represent an
unweighted average response, while the thick dashed lines illustrate the response of the cor-
responding aggregated data mentioned in the discussion of Table 1. A circle indicates that
the impulse response at that particular time horizon for the aggregated data is signi…cantly
di¤erent than zero at the 90 percent level. Similar to the responses illustrated in Figures 3
13It should be noted that individual shocks put banks on a di¤erent equilibrium path not because the
shocks are permanent, but because these shocks are speci…c to an individual bank and therefore too small
to in‡uence the macroeconomic factors in Ct:
and 1, there is a fair amount of signi…cance in the aggregated data for all bank groups. The
most signi…cant declines appear to be coming from C&I and Real Estate loan components,
with only a few signi…cant periods of decline for Individual loans exclusively for the small
bank group. It should be kept in mind that these aggregates are not over the entire banking
sector, but for the banks that made it into our bank-level panel. These banks have their own
individual response to the same monetary policy shock, and are illustrated in the …gures
by the thin dotted lines. A striking feature of these bank-speci…c responses is that there
is a large amount of heterogeneity among banks of similar asset size, with almost as many
banks increasing their lending in response to a surprise monetary contraction as there are
banks decreasing their lending. This is a robust feature of the data across bank size and loan
components, and suggests a rather stark discrepancy between an individual bank response
and the aggregate of which it is a member.
We interpret these results as contradicting the notion that bank asset size and loan
components serve as adequate aggregating criteria to identify the BLC. In particular, while
the loan aggregates constructed using the banks for which we have individual data throughout
the sample accords with the loan aggregates constructed from the entire sector (see Figure
3), the individual banks which comprise the aggregates have a very di¤use e¤ect to the same
monetary policy shock. One would ideally like to see a large majority of the banks within
the each category to behave like their corresponding aggregate in response to a monetary
shock similar to how they respond to a general, common component shock in the middle
columns of the …gures. This is clearly not the case exhibited.
4.3.1. Disaggregated Lending, Post-1984
Similar to the aggregate lending results, the disaggregated bank-level data was re-
estimated using post-1984 data. In contrast to the aggregates, the disaggregated-loan results
tell a similar story to the results under the full sample. As illustrated in Table 2, we …nd Download full-text
much less volatility in the aggregated time series relative to the average volatility of the
bank-level panel, as well as a much larger percentage of the aggregate ‡uctuations being
attributable to ‡uctuations in macroeconomic components. More importantly, our impulse
response analysis under post-1984 data retains much of the signi…cance of the BLC illus-
trated under the full sample. These are illustrated in Figures 9-11. Again, it should be
noted that these aggregates are constructed using only the individual banks in our panel
and therefore is not a complete picture of aggregate lending.
4.3.2. Alternative Factor Estimations
In order to verify that the number of factors in our FAVARs were reasonable, we per-
formed robustness checks as in BBE by re-estimating the FAVARs with an increased number
of factors. The impulse responses for bank speci…c, common component and monetary pol-
icy shocks did not qualitatively change from the main results discussed above for total,
C&I, or Real Estate loans. The only minor change in impulse responses was for individ-
ual loans, which displayed a slightly positive response to a contractionary monetary shock
for aggregated large banks. In terms of the disaggregated data, increasing the number of
factors did not qualitatively change the results reported in Table 1, in particular, the R2
calculations measuring the amount of ‡uctuation in individual bank lending attributable to
This paper examined the role of commercial banks in monetary transmission in a factor-
augmented vector autoregression (FAVAR). The ability of a FAVAR to exploit a large con-
ditioning set of macroeconomic indicators when identifying monetary policy shocks, coupled
with the ability to calculate impulse responses for every variable in this set, allows us to assess