Academic journal article Economic Inquiry

The Time-Varying Performance of the Long-Run Demand for Money in the United States

Academic journal article Economic Inquiry

The Time-Varying Performance of the Long-Run Demand for Money in the United States

Article excerpt

GEORGE S. TAVLAS [*]

This article investigates the issues of the stability and predictability and interestsensitivity of money demand over 1870-1997 Two different estimation methodologies are used-random coefficient (RC) modeling and vector error correction (VEC) modeling. The former procedure allows the profiles of the coefficients to be traced over time and relaxes several restrictions routinely imposed in applied work. The results indicate that different estimation methodologies using different data periods and frequencies yield estimates of some of the coefficients of the long-run demand for money that fall within a fairly narrow range. The results also suggest that specification errors have had an important influence on the time profile of the interest elasticity of money demand and that there is a tendency for the interest elasticity to decline in absolute value as interest rates decline. (JEL C20, E47)

I. INTRODUCTION

This article reinvestigates the issues of the stability, predictability, and interestsensitivity of the long-run demand for money in the United States. [1] The model used is closely based on that used by Friedman and Schwartz (1982). The data are also those of Friedman and Schwartz--extended by Bordo et al. (1997) and us to include more recent observations--while the data frequency is annual rather than averaged over business cycle phases (as used by Friedman and Schwartz). [2] Two very different methodologies are used to examine the issue of money-demand behavior--vector error correction (VEC) modeling and random coefficient (RC) modeling. The former approach is aimed at addressing problems of spurious correlation produced by integrated variables and dynamic misspecification due to inadequate lag structures and attempts to integrate short-run dynamics with departures from long-run equilibrium relationships. The underlying philosophy of this approach is the general-to-specific methodology popularized by Hend ry and his associates (e.g., Hendry and Ericsson [1991]; Ericsson et al. [1998]). The RC approach, developed by Swamy and Tinsley (1980), Swamy and Tavlas (1992, 1995, 2000), and Christou et al. (1996, 1998), is aimed at dealing with four major specification problems (discussed in section II) that often arise in econometric estimation. We use it to shed light on such issues as the stability and predictability of money demand and whether the demand for money was more interest sensitive during the 1930s Great Depression than in other periods (and, indeed, whether a liquidity trap existed in the 1930s).

The remainder of this article is divided into three sections. Section II discusses the basic money-demand specification, the data, and the VEC and RC estimation procedures. Section III presents the estimation results. Both the VEC and RC models are estimated using annual data covering 1870-1989 and postsample forecasts are generated over 1990-1997. The models are then reestimated over earlier periods to test how well they forecast in a variety of circumstances, including the Great Depression years. For as Goldfeld (1992, 623) put it in his survey of the money-demand literature: "Ultimately, of course, such models need to stand the forward test of time; that is, they need to continue to hold outside the period of estimation." [3] An aim of the article is to inquire whether two different empirical methodologies can provide a stable long-run demand for money function that can perform well in prediction. If so, this should provide some reassurance about the empirical properties of that function. Section IV concl udes.

II. THE MODEL, DATA, AND ESTIMATION PROCEDURES

A Basic Friedman-Schwartz Specification

Friedman and Schwartz (1982) posited the following money-demand function:

(1) ln([m.sub.t]) = [[alpha].sub.0] + [[alpha].sub.1][r.sub.t] + [[alpha].sub.2]ln([y.sub.t]) + [[alpha].sub.3][g.sub.yt] + [u.sub.t],

where [m. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.