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Rpresentative agent models have become a predominant means of studying the macroeconomy in modern economics without there being much discussion in the literature about their propriety or usefulness. This volume evaluates the use of these models in macroeconomics, examining the justifications for their use and concluding that representative agent models are neither a proper nor a particularly useful means of studying aggregate behaviour.
We show the importance of a dynamic aggregation bias in accounting for the PPP puzzle. We prove that established time-series and panel methods substantially exaggerate the persistence of real exchange rates because of heterogeneity in the dynamics of disaggregated relative prices. When heterogeneity is properly taken into account, estimates of the real exchange rate half-life fall dramatically, to little more than one year, or significantly below Rogoff''s "consensus view" of three to five years. We show that corrected estimates are consistent with plausible nominal rigidities, thus, arguably, solving the PPP puzzle.
Despite spatial statistics and spatial econometrics both being recent sprouts of the general tree "spatial analysis with measurement"—some may remember the debate after WWII about "theory without measurement" versus "measurement without theory"—several general themes have emerged in the pertaining literature. But exploring selected other fields of possible interest is tantalizing, and this is what the authors intend to report here, hoping that they will suscitate interest in the methodologies exposed and possible further applications of these methodologies. The authors hope that reactions about their publication will ensue, and they would be grateful to reader(s) motivated by some of the research efforts exposed hereafter letting them know about these experiences.
Microeconomic modeling has been an important tool for agricultural economists for several decades and promises to be important for ad-dressing the research problems of the 1980s as well. This volume explores the possibilities for using micromodeling to analyze how individual farm businesses react to and are affected by farm policies. Although this purpose represents only one potential use of micro-modeling, effective modeling for policy analysis necessitates a broad look from several historical, analytical, and institutional perspectives. The Micromodeling Conference held November 18-20, 1981, at Airlie House, Virginia, under the auspices of the U.S. Department of Agri-culture's Economic Research Service and the Farm Foundation reflected these concerns.
This book treats the notion of morphisms in spatial analysis, paralleling these concepts in spatial statistics (Part I) and spatial econometrics (Part II). The principal concept is morphism (e.g., isomorphisms, homomorphisms, and allomorphisms), which is defined as a structure preserving the functional linkage between mathematical properties or operations in spatial statistics and spatial econometrics, among other disciplines. The purpose of this book is to present selected conceptions in both domains that are structurally the same, even though their labelling and the notation for their elements may differ. As the approaches presented here are applied to empirical materials in geography and economics, the book will also be of interest to scholars of regional science, quantitative geography and the geospatial sciences. It is a follow-up to the book “Non-standard Spatial Statistics and Spatial Econometrics” by the same authors, which was published by Springer in 2011.
This short monograph which presents a unified treatment of the theory of estimating an economic relationship from a time series of cross-sections, is based on my Ph. D. dissertation submitted to the University of Wisconsin, Madison. To the material developed for that purpose, I have added the substance of two subsequent papers: "Efficient methods of estimating a regression equation with equi-correlated disturbances", and "The exact finite sample properties of estimators of coefficients in error components regression models" (with Arora) which form the basis for Chapters 11 and III respectively. One way of increasing the amount of statistical information is to assemble the cross-sections of successive years. To analyze such a body of data the traditional linear regression model is not appropriate and we have to introduce some additional complications and assumptions due to the hetero geneity of behavior among individuals. These complications have been discussed in this monograph. Limitations of economic data, particularly their non-experimental nature, do not permit us to know a priori the correct specification of a model. I have considered several different sets of assumptionR about the stability of coeffi cients and error variances across individuals and developed appropriate inference procedures. I have considered only those sets of assumptions which lead to opera tional procedures. Following the suggestions of Kuh, Klein and Zellner, I have adopted the linear regression models with some or all of their coefficients varying randomly across individuals.
This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy, and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. Although many have attempted to make such cross-level inferences, scholars agree that all existing methods yield very inaccurate conclusions about the world. In this volume, Gary King lays out a unique--and reliable--solution to this venerable problem. King begins with a qualitative overview, readable even by those without a statistical background. He then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. He then presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. The method works in practice. King's solution to the ecological inference problem will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable, and move forward fields of inquiry in which progress has been stifled by this problem.
One cannot exaggerate the importance of estimating how international trade responds to changes in income and prices. But there is a tension between whether one should use models that fit the data but that contradict certain aspects of the underlying theory or models that fit the theory but contradict certain aspects of the data. The essays in Estimating Trade Elasticities book offer one practical approach to deal with this tension. The analysis starts with the practical implications of optimising behaviour for estimation and it follows with a re-examination of the puzzling income elasticity for US imports that three decades of studies have not resolved. The analysis then turns to the study of the role of income and prices in determining the expansion in Asian trade, a study largely neglected in fifty years of research. With the new estimates of trade elasticities, the book examines how they assist in restoring the consistency between elasticity estimates and the world trade identity.