Ebook The Bank Lending Channel: a FAVAR Analysis
Since Bernanke and Blinderps (1992) observation that significant 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 finance (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 differences 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 find that increases in the Federal funds rate are followed by significant declines in lending volume for the smallest (in terms of assets) and least liquid banks. Den Haan et al. (2007) consider loan components aggregated across banks and find that real estate and consumer loans decline sharply in response to a monetary contraction while commercial and industrial (C&I) loans increase. While Perez (1998), Ashcraft (2006), and others have questioned the macroeconomic significance 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 commonly 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 responding to both bank specific and aggregate sources of fluctuations simultaneously. Previous analyses which either support or refute the BLC are in some way subject to these limitations. 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 auto regression (FAVAR).
A FAVAR, which combines standard structural VAR methods with factor analysis, exploits 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 effects 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. Second, one does not need to take a stand on specific 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 fluctuations 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). While these variables illustrate how aggregate bank lending responds to an improved identification of monetary policy shocks, we also consider a large amount of lending data at the individual bank level. This allows us to disentangle the fluctuations 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 specific conditions. To our knowledge, this is the first 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 policy.
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