A Mark Sadowski post
In particular, we are going to add Bank Credit at All Commercial Banks (LOANINV).
The first thing I want to do is to demonstrate that the monetary base Granger causes bank credit during the period from December 2008 through May 2015. Here is a graph of the natural log of SBASENS and LOANINV.
The following analysis is performed using a technique developed by Toda and Yamamoto (1995).
Using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests I find that the order of integration is one for both series. I set up a two-equation VAR in the log levels of the data including an intercept for each equation.
Most information criteria suggest a maximum lag length of four. An LM test suggests that there is no problem with serial correlation at this lag length. The AR roots table suggests that the VAR is dynamically unstable at this lag length, and increasing the lag length does not seem to fix the issue. This would be a problem if our primary objective in estimating the bivariate VAR was look at its impulse response functions (IRFs). Fortunately, the Granger causality test results do not rely on the bivariate VAR being dynamically stable. Finally, the Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are cointegrated at this lag length. This suggests that there must be Granger causality in at least one direction between the monetary base and bank credit.
Then I re-estimated the level VAR with one extra lag of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 5, I left the intervals at 1 to 4 and declared the lag of each variable to be exogenous variables. Here are the Granger causality test results.
Thus I fail to reject the null hypothesis that bank credit does not Granger cause the monetary base, but I reject the null hypothesis that the monetary base does not Granger cause bank credit at the 5% significance level. In other words there is evidence that the monetary base Granger causes bank credit, but not the other way around.
Since the monetary base Granger causes bank credit, it should probably be added to our baseline VAR model. This is because, under these circumstances, we might expect shocks to the monetary base in the VAR model to lead to statistically significant changes in bank credit.
With the bank credit added to the baseline VAR model, most information criteria suggest a maximum lag length of four. An LM test suggests that there is no problem with serial correlation at this lag length. An AR roots table shows the four-variable VAR to be dynamically stable.
The Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that there exists two cointegrating equations at this lag length. But this is expected, since we already have evidence that the monetary base is cointegrated with both industrial production and bank credit. This matter is addressed in greater detail in the three posts where the baseline VAR is developed.
I am using a recursive identification strategy (Choleskey decomposition), which is the dominant practice in the empirical literature on the transmission of monetary policy shocks. Such a strategy means that the order of the variables affects the results. For the four-variable VARs I am arranging the output level first, the price level second, the monetary policy instrument third, and the financial variable last in the vector. This ordering assumes that the Federal Open Market Committee (FOMC) sees the current output level and price level when it sets the policy instrument, and that the output level and price level respond to a policy shock with one lag, but that financial markets respond to a policy shock with no lag.
As before, the response standard errors I will show are analytic, since Monte Carlo standard errors change each time an Impulse Response Function (IRF) is generated. Here are the responses to the monetary base and to bank credit in the four-variable VAR.
The instantaneous response of bank credit to a positive shock to the monetary base is negative, but it is relatively small and is statistically insignificant. This is followed by a statistically significant positive response in months 11 through 18. However, a positive shock to bank credit does not lead to a statistically significant response in either the level of industrial production or the price level in any month.
The IRFs show that a positive 2.2% shock to the monetary base in month one leads to a peak increase in bank credit of 0.32% in month 14.
So why might an increase in the monetary base lead to an increase in bank credit?
A positive shock to the monetary base raises Nominal GDP (NGDP) expectations, which as we saw in this post, leads to higher stock prices. Higher stock prices raise the net worth of firms, which lowers the perception of adverse selection and moral hazard problems, which makes it more likely that banks will lend for physical investment spending. This so-called balance sheet channel has been amply described in surveys by Bernanke and Gertler (1995), Cecchetti (1995), and Hubbard (1995).
The firm balance sheet channel has its counterpart in the household liquidity effects channel. When stock prices rise, the value of financial assets rise, and consumer borrowing for durable expenditures rise because consumers have a more secure financial position and decreased expectations of suffering financial distress. The household liquidity effects channel was found to be an important factor by Mishkin (1978) during the Great Depression.
But why hasn’t the positive response of bank credit to positive shocks to the monetary base had a statistically significant affect on the level of industrial production or the price level during the age of zero interest rate policy (ZIRP)?
One possibility is that the financial crisis rendered the bank lending channel less effective than it otherwise would be.
But a more likely possibility is simply that nominal spending causes nominal lending, and not the other way around.
Thus the response of the output level and the price level to increased NGDP expectations would be the same regardless of the level of bank lending.
In my next post I will discuss the main conclusions and implications of this series.