One of the current questions in the literature on the demand for money is whether the adjustment of actual to desired money holdings is in nominal or real terms.This paper describes a simple procedure than can be used to test the nominal against the real hypothesis.The test is carried out for 27 countries.The paper also tests the structural stability of the demand for money equations and the correctness of the dynamic specification.The results are strongly in favor of the nominal adjustment hypothesis.The estimated equations are quite good in terms of the number of coefficient estimates that are of the right sign and that are significant.The equations also stand up well when tested against a more general dynamic specification.There is, however, some evidence of structural instability before and after 1973, although the instability is generally moderate.The instability does not affect the conclusion that the nominal adjustment hypothesis dominates the real adjustment hypothesis.

In previous work I have developed an equation explaining votes for president in the United States that seems to have a remarkable predictive ability. In this paper the equation is updated through the 1984 election and then used to predict the 1988 election.

InttwIttctionN important question in political economy A.' ts how, if at all, economic events affect voting behavior.Although there is by now a fairly large literature devoted to this question,' there is no widely agreed upon answer.Kramer (1971), for example, concluded from his analysis of U.S. voting behavior that economic fluctuations have an important influence on congressional elections, whereas Stigler (1973) concluded that they do not.This debate'has been continued by Arcelus and Meltzer (1975a, b), Bloom and Price (1975), and Goodman and Kramer (1975)?Many of the disagreements in this area are over statistical procedures and the interpretation of empirical results, but it is also clear that there is no single theory of voting behavior to which everyone subscribes.Unfortunately, the distinction between theoretical and empirical disagreements in this literature is often not very sharp, and there has been no systematic testing of one theory against another.This paper has two main purposes.The first is to present a model of voting behavior that is general enough to incorporate what appear to be most of the theories of voting behavior in the recent literature and that allows one to test

T HE purpose of this paper is to point out how the efficient instrumental-variables technique discussed by Brundy and Jorgenson (1971) can be modified to take into account auto-regressive properties of the error terms. The limited-information and full-information estimators proposed in this paper are consistent and have the same asymptotic distributions as the limited-information and full-information maximum likelihood estimators, respectively. The full-information estimation of simultaneous equations models with auto-regressive errors has been discussed by Sargan (1961), Hendry (1971), Chow and Fair (1973), and Dhrymes (1971). Sargan originally proposed the full-information maximum likelihood estimation of such models, and Hendry and Chow and Fair have recently developed computationally feasible methods for obtaining the maximum likelihood estimates. Hendry considered only the case of completely unrestricted auto-regressive coefficient matrices (i.e., no zero elements), whereas Chow and Fair considered the case of restricted auto-regressive coefficient matrices as well. Dhrymes has recently proposed the three-stage least squares estimator of simultaneous equations models with auto-regressive errors. Dhrymes also considered only the case of completely unrestricted auto-regressive coefficient matrices. The limited-information estimation of simultaneous equations models with auto-regressive errors has been discussed by Sargan (1961), Amemiya (1966), and Fair (1970), among others. Sargan proposed the limited-information maximum likelihood estimation of such models, and Amemiya and Fair considered various two-stage least souares estimators of such models. Most of the work on limited-information estimators has been concerned with the case of diagonal auto-regressive coefficient matrices. Brundy and Jorgenson's criticism of the twoand three-stage least squares estimators, namely, that the first stage involves estimating reduced form equations with a very large number of variables included in them, holds even more so for models with auto-regressive errors. For these models, the reduced form equations include not only all of the predetermined variables in the system but also all of the lagged endogenous and lagged predetermined variables. In fact, one of the main purposes of the work by Fair (1970) was to suggest ways in which the number of variables used in the first stage regressions of two-stage least squares might be decreased with perhaps small loss of asymptotic efficiency. The advantage of the instrumental-variables techniques proposed in the Brundy-Jorgenson paper and in this paper is that the first stage regressions need not be run.

The “NAIRU ” view of the relationship between inflation and the unemployment rate is that there is a value of the unemployment rate (the NAIRU) below which the price level forever accelerates and above which the price level forever decelerates. 1 This view imposes two important restrictions on the dynamics of the price process. This can be seen by examining a simple version of the NAIRU equation: πt − πt−1 = β(ut − u ∗ ) + γst +ɛt, β < 0, γ> 0, (1) where t is the time period, πt is the rate of inflation, ut is the unemployment rate, st is a cost shock variable, ɛt is an error term, and u ∗ is the NAIRU. If ut equals u ∗ for all t, the rate of inflation will not change over time aside from the short-run effects of st and ɛt (assuming st and ɛt have zero means). Otherwise, the rate of inflation will increase over time (the price level will accelerate) if ut is less than u ∗ for all t and will decrease over time (the price level will decelerate) if ut is greater than u ∗ for all t.

On October 6. 1979, the Federal Reserve announced what most people interpreted as a change in monetary policy. The purpose of this paper is to estimate the effects of this change on the 1980-81 economy. The effects of the change are estimated from simulations with my model of the U.S. economy (1976, 1980b).

The housing and mortgage markets have long been considered to be markets that may not always be in equilibrium, and many econometric models of the housing and mortgage markets have tried in one way or another to account for disequilibrium effects. In this paper a critique of previous models of the housing