Aggregate Money Demand Functions: Empirical Applications in by Dennis L. Hoffman PDF

By Dennis L. Hoffman

ISBN-10: 9400918143

ISBN-13: 9789400918146

ISBN-10: 9401073082

ISBN-13: 9789401073080

The econometric effects of nonstationary information have vast ranging im­ plications for empirical study in economics. particularly, those matters have implications for the research of empirical relatives corresponding to a cash call for func­ tion that hyperlinks macroeconomic aggregates: actual cash balances, actual source of revenue and a nominal rate of interest. conventional financial conception predicts that those nonsta­ tionary sequence shape a cointegrating relation and as a result, that the dynamics of a vector approach created from those variables generates specified styles. Re­ cent econometric advancements designed to deal with nonstationarities have replaced the process empirical learn within the zone, yet many basic demanding situations, for instance the difficulty of id, stay. This publication represents the efforts undertaken through the authors lately that allows you to ascertain the implications that nonstationarity has for the learn of mixture cash call for family. we've introduced jointly an empirical technique that we discover necessary in accomplishing empirical study. many of the paintings was once undertaken in the course of the authors' sabbatical sessions and we want to recognize the beneficiant aid of Arizona country college and Michigan country college respectively. Professor Hoffman needs to recognize the aid of the Fulbright-Hays origin that supported sabbattical examine in Europe and separate aid of the Council of a hundred summer time learn application at Arizona nation University.

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Additional resources for Aggregate Money Demand Functions: Empirical Applications in Cointegrated Systems

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In Chapter 4 we discuss the implications for conducting dynamic analysis with structures that exhibit "nonfundamental" (non-invertible) moving average representations. This issue is discussed by Lippi and Reichlin (1993), among others. 32 CHAPTER 3. 9) reveals that unless Ci (l) = 0, Zti will contain a random walk component that imparts nonstationarity to Zti. Clearly, any innovation in this t random walk; I: Csi, will leave a lasting imprint on the value maintained by 8=1 Zti at any point in the future.

In standard applications, the reduced form errors are assumed to be free of persistence so we depart further from Zellner and Palm in assuming F(L) = I. 21) where and IIi and II are defined above. 19)choosing instead to focus on reduced form representations of vector processes. But, structural innovations analysis that typically accompany these exercises are identification exercises that require demanding sets of restrictions in their own right. Below we see that a distinct set of identification issues arise in cointegrated systems even if we concentrate exclusively on the long-run properties of the data and make no attempt to identify the complete contemporaneous structure of the model.

The numbers in parentheses under the coefficient estimates are the conventional estimated standard errors, while the numbers in braces are the estimated steady-state coefficients. The second equation in that table shows that for this sample, the results are quite insensitive to the inclusion of an AR(l) disturbance structure. For all practical purposes, all that is accomplished by the addition of the autoregressive coefficient is to reduce the serial correlation in the estimated residuals and improve the efficiency of the coefficient estimates.

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Aggregate Money Demand Functions: Empirical Applications in Cointegrated Systems by Dennis L. Hoffman


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