This book was accepted in 1999 as doctoral thesis (Dr. oec. publ. ) by the faculty of economics of the Ludwig-Maximilians-Universitat in Munich. It won the dissertation price of the Alumni-Club of the economics faculty in the academic year 2000. Financial assistance by the German Research Asso ciation (DFG) for printing costs is gratefully acknowledged. I have to thank the people behind the B\'IF,X-project, in particular Donald Knuth and Leslie Lamport, for their wonderful typesetting program. The supervisors of my thesis were Prof. Dr. G. Flaig and Prof. Dr. D. Marin. I would like thank both for their thorough reading of the book and their ideas and critical remarks. lowe much gratitude to my academic teacher Prof. Dr. G. Flaig for many insightful conversations about modern time series econometrics, asymptotic theory and data problems. His empha sis on clear theory combined with sound econometric methods formed my personal ideal of how to do applied economics. After he left the university in 1998I had the opportunity to continue my work at the Center for Economic Studies. Prof. Dr. H. -W. Sinn and my new colleaguesat CES provided a stim ulating environment for the completion of my dissertation. Finally, I want to thank my parents, my sister and my friends for being there and sharing my cheers and worries. Munich, October 2000 Michael Reutter Our main concern in philosophy and in science should be the search fortruth. Justification is not an aim; and brilliance and cleverness as such are boring.

Modern business cycle theory and growth theory uses stochastic dynamic general equilibrium models. In order to solve these models, economists need to use many mathematical tools. This book presents various methods in order to compute the dynamics of general equilibrium models. In part I, the representative-agent stochastic growth model is solved with the help of value function iteration, linear and linear quadratic approximation methods, parameterised expectations and projection methods. In order to apply these methods, fundamentals from numerical analysis are reviewed in detail. In particular, the book discusses issues that are often neglected in existing work on computational methods, e.g. how to find a good initial value. In part II, the authors discuss methods in order to solve heterogeneous-agent economies. In such economies, the distribution of the individual state variables is endogenous. This part of the book also serves as an introduction to the modern theory of distribution economics. Applications include the dynamics of the income distribution over the business cycle or the overlapping-generations model. In an accompanying home page to this book, computer codes to all applications can be downloaded.

This book gives a comprehensive description of macroeconometric modeling and its development over time. The first part depicts the history of macroeconometric model building, starting with Jan Tinbergen's and Lawrence R. Klein's contributions. It is unique in summarizing the development and specific structure of macroeconometric models built in North America, Europe, and various other parts of the world. The work thus offers an extensive source for researchers in the field. The second part of the book covers the systematic characteristics of macroeconometric models. It includes the household and enterprise sectors, disequilibria, financial flows, and money market sectors.

This monograph grew out of a project which was sponsored by the Swiss National Foundation ("Schweizerischer Nationalfonds") under grant no. 4. 636-0. 83. 09. Yithin this project, prediction-oriented estimation methods for the canonical econometric disequilibrium model were developed. The present monograph deals with the application of these estimation techniques to three aggregative markets of the Swiss economy. Parts of the monograph have been presented at various places: the estimation techniques described in chapter 3 at the European Meeting of the Econometric Society, Madrid 1984; the application to residential investment described in chapter 4 at a symposium on housing policy at the University of Mannheim, 1984; the empirical study on the money stock described in chapter 5 at the Symposium on Money, Banking and Insurance held at the University of Karlsruhe, 1984, as well as at a joint seminar of the University of Basle and the Bank for International Settlements (BIS), 1985; and, finally, the empirical study on the aggregate labor market described in chapter 6 at a seminar of the University of ZUrich, 1985. Comments from toe seminar participants, in particular from Palle S. Andersen (BIS) who served as a discussant, Pascal Bridel (Swiss National Bank, SNB), Franz Ettlin (SNB), and Kurt Schiltknecht (Nordfinanz-Bank, Zurich) are gratefully acknowledged, without implying any responsibility on their part. The methodological part described in chapters 2 and 3 is contributed by G. Frei and B.

This volume contains selected papers presented at the "International Workshop on Computationally Intensive Methods in Simulation and Op th th timization" held from 23 to 25 August 1990 at the International Institute for Applied Systems Analysis (nASA) in La~enburg, Austria. The purpose of this workshop was to evaluate and to compare recently developed methods dealing with optimization in uncertain environments. It is one of the nASA's activities to study optimal decisions for uncertain systems and to make the result usable in economic, financial, ecological and resource planning. Over 40 participants from 12 different countries contributed to the success of the workshop, 12 papers were selected for this volume. Prof. A. Kurzhanskii Chairman of the Systems and Decision Sciences Program nASA Preface Optimization in an random environment has become an important branch of Applied Mathematics and Operations Research. It deals with optimal de cisions when only incomplete information of t.he future is available. Consider the following example: you have to make the decision about the amount of production although the future demand is unknown. If the size of the de mand can be described by a probability distribution, the problem is called a stochastic optimization problem.

Top scholars synthesize and analyze scholarship on this widely used tool of policy analysis in 27 articles, setting forth its accomplishments, difficulties, and means of implementation. Though CGE modeling does not play a prominent role in top U.S. graduate schools, it is employed universally in the development of economic policy. This collection is particularly important because it presents a history of modeling applications and examines competing points of view. - Presents coherent summaries of CGE theories that inform major model types - Covers the construction of CGE databases, model solving, and computer-assisted interpretation of results - Shows how CGE modeling has made a contribution to economic policy