The econometric consequences of nonstationary data have wide ranging im� plications for empirical research in economics. Specifically, these issues have implications for the study of empirical relations such as a money demand func� tion that links macroeconomic aggregates: real money balances, real income and a nominal interest rate. Traditional monetary theory predicts that these nonsta� tionary series form a cointegrating relation and accordingly, that the dynamics of a vector process comprised of these variables generates distinct patterns. Re� cent econometric developments designed to cope with nonstationarities have changed the course of empirical research in the area, but many fundamental challenges, for example the issue of identification, remain. This book represents the efforts undertaken by the authors in recent years in an effort to determine the consequences that nonstationarity has for the study of aggregate money demand relations. We have brought together an empirical methodology that we find useful in conducting empirical research. Some of the work was undertaken during the authors' sabbatical periods and we wish to acknowledge the generous support of Arizona State University and Michigan State University respectively. Professor Hoffman wishes to acknowledge the support of the Fulbright-Hays Foundation that supported sabbattical research in Europe and separate support of the Council of 100 Summer Research Program at Arizona State University.

Synopsis

The econometric consequences of nonstationary data have wide-ranging implications for empirical research in economics. Specifically, these issues have implications for the study of empirical relations such as a money demand function that links macroeconomic aggregates: real money balances, real income and a nominal interest rate. Traditional monetary theory predicts that these nonstationary series form a co-integrating relation and, accordingly, that the dynamics of a vector process comprising these variables generates distinct patterns. Recent econometric developments designed to cope with nonstationarities have changed the course of empirical research in the area, but many fundamental challenges, for example the issue of identification, remain. This book is an effort to determine the consequences that nonstationarity has for the study of aggregate money demand relations. The object of this study is to utilize the tools of modern time series analysis to determine the role of an aggregate demand for real balances in the generation of macroeconomic time series.

A significant characteristic of this research is the identification and estimation of this demand function in a multivariate framework, in contrast to most existing studies that concentrate on a single equation framework.

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