The Demand for M1 in the U.S.A., 1960-1988

Abstract

Estimated U.S. M1 demand functions appear unstable, regularly "breaking down," over 1960. We propose a money demand function whose arguments include inflation, real income, long-term bond yield and risk, T-bill interest rates, and learning curve weighted yields on newly introduced instruments in M1 and non-transactions M2. The model is estimated in dynamic error-correction form; it is constant and, with an equation standard error of 0·4%, variance-dominates most previous models. Estimating alternative specifications explains earlier "breakdowns," showing the model's distinctive features to be important in accounting for the data.

A vector error-correction forecasting model of the US economy

“…Hoffman and Rasche (1991), Johansen and Juselius (1990), Baba, Hendry, and Starr (1992), Stock and Watson (1993), and Lucas (1994) among others present evidence on the stationarity of money demand relations. This is sometimes estimated as a velocity relation that links income velocity to movements in a measure of nominal interest rates as in Hoffman, Rasche and Tieslau (1995).…”

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“…The validity of the over-identified specification is, instead, tested by evaluating the restrictions implicitly imposed on the general reduced form. The most popular applications of the general-to-specific specification strategy are in the area of money demand (Baba et al (1992)) and aggregate consumption expenditure (see, for example, Hendry et al (1990)). As is well discussed in Fukac and Pagan (2006), the LSE approach was influential in the development of a second generation of large equation models, such as the Canadian model RDX2 (Helliwell et al (1991))-and the MPS model at the Fed (Gramlich (2004)), which, apart from introducing much stronger supply side features with respect to traditional IS-LM models, paid considerable attention to dynamic specification and to the implementation of error correction models.…”

“…In fact, it is a VAR potentially with stochastic singularity, as the dimension of the vector of shocks is typically smaller than that of the vector of variables included in the VAR. However, this problem is promptly solved by adding the appropriate number of measurement errors 5 . Canonical RBC models (see, for example, Kydland and Prescott (1982), and King, Plosser and Rebelo (1988)) contained a limited number of parameters, and within this class of models the role of estimation was clearly de-emphasized and parameters have been often calibrated rather than estimated.…”

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“…There is no consensus on the existence of a long-run stable money demand function empirically when a linear framework is applied (Hendry and Ericsson, 1991;Hoffman and Rasche, 1991;Baba et al, 1992;Stock and Watson, 1993;Haug and Lucas, 1996). The restriction of a linear cointegration, or the ECM approach, is that it assumes a constant speed of adjustment, which may not be appropriate when there exist transaction costs as suggested by Miller and Orr (1966).…”