The Effects of Monetary Policy Shocks: Comparing Contemporaneous versus Long-Run Identifying Restrictions.

• Author: McMillin, W. Douglas
• Position: Research information

W. Douglas McMillin [*]

This study compares the effects of monetary policy shocks on the macroeconomy using four different procedures for identifying policy shocks that use contemporaneous restrictions and a procedure that uses long-run restrictions. Impulse response functions are computed using the same vector autoregressive (VAR) model and sample period. The comparison is done for a model that includes only a short-term interest rate and for a model that adds a long-term rate as well. Sources of differences in the magnitude of effects across identification schemes are examined.

1. Introduction

   Vector autoregressive (VAR) models have been widely used in recent years to analyze the effects of monetary policy shocks. However, estimates of the macroeconomic effects of monetary policy often differ across studies with regard to both timing and magnitude. The studies generating these estimates frequently differ in terms of the variables constituting the model, the sample period for estimation, and the method of identifying policy shocks (see, e.g., Christiano, Eichenbaum, and Evans 1994, 1996, 1998; Gordon and Leeper 1994; Lastrapes and Selgin 1995; Pagan and Robertson 1995, 1998; Leeper, Sims, and Zha 1996).

   Certainly, a critical element in the estimation of the effects of policy shocks is the identification of these policy shocks, that is, the determination of exogenous shocks to monetary policy. Two methods have been widely used in the VAR literature to identify structural shocks to monetary policy. One general approach employs restrictions on the contemporaneous relations among the variables of the VAR model, while the second general approach imposes restrictions on the long-run relations among the variables. Although economic and institutional arguments can be used to rationalize each identification scheme, there is no consensus as to which approach to identifying shocks is preferred, and the weaknesses of both approaches have been discussed in the literature. [1] Keating (1992), Lastrapes and Selgin (1995), and McCarthy (1995) consider limitations of the use of contemporaneous identifying restrictions. Faust and Leeper (1997) discuss potential drawbacks of imposing long-run restrictions.

   The aim of this study is to examine the implications of contemporaneous versus long-run Identification schemes for estimating the effects of monetary policy shocks within the VAR model used by Christiano, Eichenbaum, and Evans (1994, 1996, 1998; hereafter CEE) and Bernanke and Mihov (1998; hereafter BM) over a particular sample period. Holding constant the variables in the VAR model and the sample period allows one to clearly observe the effect of the identification scheme in estimating the timing and magnitude of the effects of monetary policy actions. The model employed comprises output, the price level, commodity prices, and three reserves market variables: total reserves, nonborrowed reserves, and the federal funds rate. The focus on the reserves market is important since it allows a more thorough consideration of how policy actions are implemented than does a model that includes only a reserve aggregate or the federal funds rate as the policy variable. Following BM (1998), monthly data are used in estim ating the model; use of monthly data reduces problems that may arise with temporal aggregation (see Christiano and Eichenbaum 1987). The effects of monetary policy shocks for different identification schemes are evaluated by computing impulse response functions.

   The approach in this paper is similar in spirit to Keating (1992) and Lastrapes (1998). However, these studies focused on the effects of money supply shocks, while the focus of the current study is on monetary policy shocks. It is generally thought that, since money supply shocks typically confound policy actions and nonpolicy events, they are not a good measure of monetary policy shocks. For example, consider a textbook model of the money supply process in which the money supply equals the product of a money multiplier and a reserve aggregate like nonborrowed reserves. The money multiplier is affected by portfolio decisions of the nonbank public as reflected in changes in the currency/checkable deposit ratio and, depending on the definition of money considered and whether reserves are imposed on time deposits, the time deposit/checkable deposit ratio. The money multiplier is also affected by bank behavior as embodied in the ratio of excess reserves to checkable deposits, by reserve requirements set by the ce ntral bank, and in some formulations by the discount rate set by the central bank. A change in either the money multiplier or the reserve aggregate will alter the money supply, and, since changes in the money multiplier and reserve aggregates frequently occur in the same period, changes in the money supply will often reflect the behavior of the central bank, banks, and the nonbank public. Fackler and McMillin (1998) demonstrated the importance of separating money supply shocks into reserve aggregate shocks and money multiplier shocks within the context of a VAR model that used long-run restrictions to identify structural shocks to the money multiplier, a reserve aggregate, and money demand, as well as structural shocks to aggregate supply and the IS curve. They found differences in the timing and magnitude of the effects of the money multiplier and reserve aggregate shocks on macro variables. This suggests that considering just money supply shocks may yield a distorted picture of the effects of monetary polic y actions.

   Although BM (1998) and CEE (1998) compared the effects of alternative monetary policy shocks identified using contemporaneous restrictions within a common model and sample period, no comparison was made with monetary policy shocks identified using long-run restrictions. In their study of alternative approaches to estimating the liquidity effect, Pagan and Robertson (1995) explicitly considered the CEE model, but, within this specific framework, they did not consider the Strongin, Bernanke-Mihov, Bernanke-Blinder, or long-run restrictions identification schemes. They impose CEE-type and Strongin-type restrictions within other models that comprise a subset of the CEE model variables but do not consider long-run restrictions schemes or the Bernanke-Blinder or Bernanke-Mihov schemes within these models. They also compare estimates of the impact liquidity effect for money supply shocks within a four-variable model that includes money, price, output, and an interest rate for a long-run restrictions scheme, a scheme that blends long-run and contemporaneous restrictions, and a scheme that uses only contemporaneous identifying restrictions.

   Pagan and Robertson (1998) compared estimates of the liquidity effect of a shock to a reserve or monetary aggregate within three different VAR models. One model used only contemporaneous restrictions to identify a shock to total reserves; one model used only long-run restrictions to identify a shock to either the monetary base, Ml, or M2; and the third used a blend of contemporaneous and long-run restrictions to identify a money supply (M1) shock. The variables in each model differ, and the same sample period is not used for all models.

   Although these previous studies have provided valuable information about estimating the macro effects of either the money supply or monetary policy, it seems important to compare the effects of contemporaneous versus long-run restrictions within a model that contains the major reserve market variables over a common sample period, something not done in previous studies. Section 2 of the paper discusses the model and the alternative identification schemes in more detail. Section 3 presents the impulse response functions, while section 4 provides a brief summary and conclusion.

2. Model Specification and Identification of Monetary Policy Shocks

   As noted earlier, the model consists of output, the price level, a commodity price index, total reserves, nonborrowed reserves, and the federal funds rate. The commodity price index is included in light of the "price puzzle" often generated in VAR models that do not include a variable that proxies for information about future inflation. The reserves market variables are the ones generally considered critical in specifying a model of this market.

   The model is estimated using monthly data for the period 1962:1-1996:12. Data from 1962:1-1964:12 are used as presample data, and estimation is done for 1965:1-1996:12. The three-year gap between the beginning of the data and the start of the estimation period is necessitated by the manner in which the reserve variables are constructed. Following GEE (1994), a lag of 12 months is used in all VAR models. All data are from the DRI Basic Economics database, and the database name is enclosed in parentheses after the variable description. Following BM (1998), output is measured by the log of real GDP (gdpq [chain-weighted real GDP]) interpolated from quarterly data). [2] The price level is measured by the log of the interpolated chain-weighted price index for GDP (gdpdfc). The commodity price index is the log of the Commodity Research Bureau's spot market price index for all commodities (psccom).

   Total reserves (fmrra) are adjusted for reserve requirement changes, as are nonborrowed reserves (fmrnbc). The nonborrowed reserves measure includes extended credit; the series with only nonborrowed reserves exhibits a sharp drop at the time of the Continental Illinois crisis in 1984. Following BM (1998), both total reserves and nonborrowed reserves are normalized by a 36-month moving average of total reserves. They do this rather than take logs since they employ a linear model of the reserves market in their identification scheme. Since the BM scheme is considered in this paper, their method of constructing the reserves variables is used. The level of the federal funds rate (fyff) is employed.

   As noted...