The Monetary Base and the Bank Deposit Channel of Monetary Transmission in the Age of ZIRP

A Mark Sadowski post

In this post we are going to add a measure of US bank deposits to the baseline VAR which I developed in these three posts.(1, 2 and 3).

In particular, we are going to add Deposits, All Commercial Banks (DPSACBM027SBOG).

The first thing I want to do is to demonstrate that the monetary base Granger causes bank deposits during the period from December 2008 through May 2015. Here is a graph of the natural log of SBASENS and DPSACBM027SBOG.

Sadowski GC10_1

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 two. 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 stable, and the Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are not cointegrated at this lag length.

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 3, I left the intervals at 1 to 2 and declared the lag of each variable to be exogenous variables. Here are the Granger causality test results.

Sadowski GC10_2

Thus I fail to reject the null hypothesis that bank deposits do not Granger cause the monetary base, but I reject the null hypothesis that the monetary base does not Granger cause bank deposits at the 10% significance level. In other words there is evidence that the monetary base Granger causes bank deposits, but not the other way around.

Since the monetary base Granger causes bank deposits, 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 deposits.

With the bank deposits added to the baseline VAR model, most information criteria suggest a maximum lag length of two. An LM test suggests that there is a problem with serial correlation at this lag length, but this problem disappears when the lag length is increased to four. An AR roots table shows the VAR to be dynamically stable.

The Johansen’s Trace Test indicates that there exists two cointegrating equations, and the Maximum Eigenvalue Test indicates that there exists one cointegrating equation at this lag length. In any case, this is expected, since we already have evidence that the monetary base is cointegrated with industrial production. This matter is addressed in greater detail in the three posts where the baseline VAR is developed.

I am using a recursive identification strategy (Cholesky 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 deposits in the four-variable VAR.

Sadowski GC10_3

The instantaneous response of bank deposits to a positive shock to the monetary base is positive, but statistically insignificant. This is followed by a statistically significant positive response in months two and three. However, a positive shock to bank deposits 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 deposits of 0.24% in month three.

So why might an increase in the monetary base lead to an increase in bank deposits?

If the Federal Reserve purchases a Treasury or an Agency security from a nonbank, the money received by the nonbank is either deposited into a bank or it is retained as currency. Since banks have nearly consistently increased their holdings of Treasury and Agency securities throughout this time period, purchases of Treasury and Agency securities by the Federal Reserve have probably mostly ended up adding to the amount of bank deposits.

Why hasn’t the increase in bank deposits led to a more statistically significant increase in the level of industrial production or the price level during the age of zero interest rate policy (ZIRP)? I’m not sure, but it certainly is a challenge for those who believe that broad money has more explanatory power than the monetary base.

In my next post I shall add commercial bank credit to the baseline VAR. Do positive shocks to the monetary base affect the quantity of commercial bank credit? And does the amount of commercial bank credit have an effect on the output level and the price level?

Tune in next time and find out.

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