Aggregate Money Demand Functions - Hoffman, Dennis L.

. .

Résumé :
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.

Sommaire:
1 Background.- 2 The Development and Failures of the Empirical Literature on the Demand for Money.- 2.1 Some Equilibrium Money Demand Studies.- 2.2 Partial Adjustment Models.- 2.3 Specification Testing - The U.S. Experience.- 2.4 Another Look at U.S. Money Demand.- 3 Identification, Estimation, and Inference in Cointegrated Systems.- 3.1 Nonstationary and the Estimation of Money Demand Models.- 3.2 Nonstationarity in the Money Demand Model: Is There Evidence of Integration or Cointegration?.- 3.3 Identification of Long-Run Parameters from Knowledge of Cointegration Rank.- 3.4 Identifying the Source of Nonstationarity.- 3.5 Estimation and Inference Regarding Long-Run Parameters.- 3.6 Testing Constancy of the Cointegration Space.- 3.7 Conclusion.- 4 A Framework for Structural and Dynamic Analysis in Cointegrated Systems.- 4.1 Identification.- 4.2 Identification in the Structural VECM.- 4.3 Assessing Structural VAR Specifications.- 4.4 Weak Exogeneity in Cointegrated Structural VAR Models.- 4.5 Summary.- 5 A Prototype Economic Model Characterized by Cointegration.- 5.1 Solution of the Rational Expectations Model.- 5.2 The Vector Error Correction Representation.- 5.3 Moving Average (Wold) Representation.- 5.4 Impulse Response Functions.- 5.5 Reduced Model VECM.- 6 Analysis of Three Variable VECM Models Including Demand Functions for Real Balances.- 6.1 The Friedman-Kuttner Challenge.- 6.2 Estimation, Testing, and Analysis.- 6.3 Time Disaggregation.- 6.4 Analysis of Dynamic Responses to Permanent Shocks.- 6.5 Summary.- 7 Higher Dimensional VECM Models with Long-Run Money Demand Functions.- 7.1 Real Balances, Inflation, Real Output and Interest Rates.- 7.2 Real Balances, Output, Short and Long Term Rates.- 7.3 Conclusions.- 8 Combining Term Structure and Fisher Effects.-8.1 Recursive Estimates of Five Variable VECMs.- 8.2 Dynamic Analysis.- 8.3 Summary and Conclusions.- Appendix: Some Extensions of the Goodfriend Errors-In Variables Model.- A.1 Multiple Regressors with Permanent and Transitory Components.- A.2 Persistence in the Disturbance Shocks.- A.3 Differenced Equations.- Author Index.