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This book is the result of my doctoral dissertation research at the Department of Econometrics of the University of Geneva, Switzerland. This research was also partially financed by the Swiss National Science Foundation (grants 12- 31072.91 and 12-40300.94). First and foremost, I wish to express my deepest gratitude to Professor Manfred Gilli, my thesis supervisor, for his constant support and help. I would also like to thank the president of my jury, Professor Fabrizio Carlevaro, as well as the other members of the jury, Professor Andrew Hughes Hallett, Professor Jean-Philippe Vial and Professor Gerhard Wanner. I am grateful to my colleagues and friends of the Departement of Econometrics, especially David Miceli who provided constant help and kind understanding during all the stages of my research. I would also like to thank Pascale Mignon for proofreading my text and im proving my English. Finally, I am greatly indebted to my parents for their kindness and encourage ments without which I could never have achieved my goals. Giorgio Pauletto Department of Econometrics, University of Geneva, Geneva, Switzerland Chapter 1 Introduction The purpose of this book is to present the available methodologies for the solution of large-scale macroeconometric models. This work reviews classical solution methods and introduces more recent techniques, such as parallel com puting and nonstationary iterative algorithms.
Macroeconomic Modelling has undergone radical changes in the last few years. There has been considerable innovation in developing robust solution techniques for the new breed of increasingly complex models. Similarly there has been a growing consensus on their long run and dynamic properties, as well as much development on existing themes such as modelling expectations and policy rules. This edited volume focuses on those areas which have undergone the most significant and imaginative developments and brings together the very best of modelling practice. We include specific sections on (I) Solving Large Macroeconomic Models, (II) Rational Expectations and Learning Approaches, (III) Macro Dynamics, and (IV) Long Run and Closures. All of the contributions offer new research whilst putting their developments firmly in context and as such will influence much future research in the area. It will be an invaluable text for those in policy institutions as well as academics and advanced students in the fields of economics, mathematics, business and government. Our contributors include those working in central banks, the IMF, European Commission and established academics.
This publication contains a substantial amount of detail about the broad history of the development of econometric software based on the personal recollections of many people. For economists, the computer has increasingly become the primary applied research tool, and it is software that makes the computer work.
This book arose out of research carried out by the authors in the period 1983-1987 whilst at the National Institute of Economic and Social Research. A number of things combined to impart the basic thrust of the research: partly the developments in formulating and estimating rational expectations models, and partly actual developments in the UK economy itself.An application of recent developments in dynamic modelling to a complete macroeconometric model of the UK is presented. Rational expectations modelling, co-integration and disequilibrium modelling are covered. The book also develops computational procedures for obtaining efficient solutions to large-scale models, and illustrates model solutions assuming rational expectations and stochastic simulations. Finally, sections on the analysis of models using optimal control methods illustrate applications of a large-scale econometric model. This section also discusses policy applications, including the derivation of time-consistent policies in the presence of rational expectations, giving quantified illustrations.
Computational Techniques for Modelling Learning in Economics offers a critical overview of the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.
Observers and Macroeconomic Systems is concerned with the computational aspects of using a control-theoretic approach to the analysis of dynamic macroeconomic systems. The focus is on using a separate model for the development of the control policies. In particular, it uses the observer-based approach whereby the separate model learns to behave in a similar manner to the economic system through output-injections. The book shows how this approach can be used to learn the forward-looking behaviour of economic actors which is a distinguishing feature of dynamic macroeconomic models. It also shows how it can be used in conjunction with low-order models to undertake policy analysis with a large practical econometric model. This overcomes some of the computational problems arising from using just the large econometric models to compute optimal policy trajectories. The work also develops visual simulation software tools that can be used for policy analysis with dynamic macroeconomic systems.
Parallel Algorithms for Linear Models provides a complete and detailed account of the design, analysis and implementation of parallel algorithms for solving large-scale linear models. It investigates and presents efficient, numerically stable algorithms for computing the least-squares estimators and other quantities of interest on massively parallel systems. The monograph is in two parts. The first part consists of four chapters and deals with the computational aspects for solving linear models that have applicability in diverse areas. The remaining two chapters form the second part, which concentrates on numerical and computational methods for solving various problems associated with seemingly unrelated regression equations (SURE) and simultaneous equations models. The practical issues of the parallel algorithms and the theoretical aspects of the numerical methods will be of interest to a broad range of researchers working in the areas of numerical and computational methods in statistics and econometrics, parallel numerical algorithms, parallel computing and numerical linear algebra. The aim of this monograph is to promote research in the interface of econometrics, computational statistics, numerical linear algebra and parallelism.
Econometric techniques and models are still being extensively used in the business of forecasting and policy advice. This book presents recent advances in the theory and applications of quantitative economic policy, with particular emphasis on fiscal and monetary policies in a European and global context. The volume honors Andrew Hughes Hallett, a pioneer and major scientist in quantitative economic policy analysis, whose contributors are among his friends and former students.
Agent-Based Computer Simulation of Dichotomous Economic Growth reports a project in agent-based computer stimulation of processes of economic growth in a population of boundedly rational learning agents. The study is an exercise in comparative simulation. That is, the same family of growth models will be simulated under different assumptions about the nature of the learning process and details of the production and growth processes. The purpose of this procedure is to establish a relationship between the assumptions and the simulation results. The study brings together a number of theoretical and technical developments, only some of which may be familiar to any particular reader. In this first chapter, some issues in economic growth are reviewed and the objectives of the study are outlined. In the second chapter, the simulation techniques are introduced and illustrated with baseline simulations of boundedly rational learning processes that do not involve the complications of dealing with long-run economic growth. The third chapter sketches the consensus modern theory of economic growth which is the starting point for further study. In the fourth chapter, a family of steady growth models are simulated, bringing the simulation, growth and learning aspects of the study together. In subsequent chapters, variants on the growth model are explored in a similar way. The ninth chapter introduces trade, with a spacial trading model that is combined with the growth model in the tenth chapter. The book returns again and again to the key question: to what extent can the simulations `explain' the puzzles of economic growth, and particularly the key puzzle of dichotomization, by constructing growth and learning processes that produce the puzzling results? And just what assumptions of the simulations are most predictable associated with the puzzling results?
One of the major controversies in macroeconomics over the last 30 years has been that on the effectiveness of stabilization policies. However, this debate, between those who believe that this kind of policies is useless if not harmful and those who argue in favor of it, has been mainly theoretical so far. The Rational Expectation Hypothesis, Time-Varying Parameters and Adaptive Control wants to represent a step toward the construction of a common ground on which to empirically compare the two "beliefs" and to do this three strands of literature are brought together. The first strand is the research on time-varying parameters (TVP), the second strand is the work on adaptive control and the third one is the literature on linear stationary models with rational expectations (RE). The material presented in The Rational Expectation Hypothesis, Time-Varying Parameters and Adaptive Control is divided into two parts. Part 1 combines the strand of literature on adaptive control with that on TVP. It generalizes the approach pioneered by Tse and Bar-Shalom (1973) and Kendrick (1981) and one recently used in Amman and Kendrick (2002), where the law of motion of the TVP and the hyperstructural parameters are assumed known, to the case where the hyperstructural parameters are assumed unknown. Part 2 is devoted to the linear single-equation stationary RE model estimated with the error-in-variables (EV) method. It presents a new formulation of this problem based on the use of TVP in an EV model. This new formulation opens the door to a very promising development. All the theory developed in the first part to control a model with TVP can sic et simpliciter be applied to control a model with RE.