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In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of what I deem to be important but not readily available, or accessible to economists. For this reason I have excluded from the notes many results on various estimation methods or their statistical properties because they are amply discussed in many standard texts on time series or on statistics.
Analysis of Economic Time Series: A Synthesis integrates several topics in economic time-series analysis, including the formulation and estimation of distributed-lag models of dynamic economic behavior; the application of spectral analysis in the study of the behavior of economic time series; and unobserved-components models for economic time series and the closely related problem of seasonal adjustment. Comprised of 14 chapters, this volume begins with a historical background on the use of unobserved components in the analysis of economic time series, followed by an Introduction to the theory of stationary time series. Subsequent chapters focus on the spectral representation and its estimation; formulation of distributed-lag models; elements of the theory of prediction and extraction; and formulation of unobserved-components models and canonical forms. Seasonal adjustment techniques and multivariate mixed moving-average autoregressive time-series models are also considered. Finally, a time-series model of the U.S. cattle industry is presented. This monograph will be of value to mathematicians, economists, and those interested in economic theory, econometrics, and mathematical economics.
This book has been updated to reflect developments in time series analysis and forecasting theory and practice, particularly as applied to economics. The second edition pays attention to such problems as how to evaluate and compare forecasts.
The application of time series techniques in economics has become increasingly important, both for forecasting purposes and in the empirical analysis of time series in general. In this book, Terence Mills not only brings together recent research at the frontiers of the subject, but also analyses the areas of most importance to applied economics. It is an up-to-date text which extends the basic techniques of analysis to cover the development of methods that can be used to analyse a wide range of economic problems. The book analyses three basic areas of time series analysis: univariate models, multivariate models, and non-linear models. In each case the basic theory is outlined and then extended to cover recent developments. Particular emphasis is placed on applications of the theory to important areas of applied economics and on the computer software and programs needed to implement the techniques. This book clearly distinguishes itself from its competitors by emphasising the techniques of time series modelling rather than technical aspects such as estimation, and by the breadth of the models considered. It features many detailed real-world examples using a wide range of actual time series. It will be useful to econometricians and specialists in forecasting and finance and accessible to most practitioners in economics and the allied professions.
Economic Theory, Econometrics, and Mathematical Economics, Second Edition: Forecasting Economic Time Series presents the developments in time series analysis and forecasting theory and practice. This book discusses the application of time series procedures in mainstream economic theory and econometric model building. Organized into 10 chapters, this edition begins with an overview of the problem of dealing with time series possessing a deterministic seasonal component. This text then provides a description of time series in terms of models known as the time-domain approach. Other chapters consider an alternative approach, known as spectral or frequency-domain analysis, that often provides useful insights into the properties of a series. This book discusses as well a unified approach to the fitting of linear models to a given time series. The final chapter deals with the main advantage of having a Gaussian series wherein the optimal single series, least-squares forecast will be a linear forecast. This book is a valuable resource for economists.
The present 'Introductory Lectures on Arbitrage-based Financial Asset Pricing' are a first attempt to give a comprehensive presentation of Arbitrage Theory in a discrete time framework (by the way: all the re sults given in these lectures apply to a continuous time framework but, probably, in continuous time we could achieve stronger results - of course at the price of stronger assumptions). It has been turned out in the last few years that capital market theory as derived and evolved from the capital asset pricing model (CAPM) in the middle sixties, can, to an astonishing extent, be based on arbitrage arguments only, rather than on mean-variance preferences of investors. On the other hand, ar bitrage arguments provided access to a wider range of results which could not be obtained by standard CAPM-methods, e. g. the valuation of contingent claims (derivative assets) Dr the_ investigation of futures prices. To some extent the presentation will loosely follow historical lines. A selected set of capital asset pricing models will be derived according to their historical progress and their increasing complexity as well. It will be seen that they all share common structural properties. After having made this observation the presentation will become an axiomatical one: it will be stated in precise terms what arbitrage is about and what the consequences are if markets do not allow for risk-free arbitrage opportunities. The presentation will partly be accompanied by an illus trating example: two-state option pricing.
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.
The problem of predicting interregional commodity movements and the regional prices of these commodities has intrigued economists, geographers and operations researchers for years. In 1838, A. A. Cournot (1838) discussed the equilibrium of trade between New York and Paris and noted how the equilibrium prices depended upon the transport costs. Enke (1951) recognized that this problem of predicting interregional flows and regional prices could be formulated as a network problem, and in 1952, . Paul Samuelson (1952) used the then recent advances in mathe matical programming to formalize the spatial price equilibrium problem as a nonlinear optimization problem. From this formula tion, Takayama and Judge (1964) derived their quadratic program ming representation of the spatial price equilibrium problem, which they and other scholars then applied to a wide variety of problem contexts. Since these early beginnings, the spatial price equilibrium problem has been widely studied, extended and applied; the paper by Harker (1985) reviews many of these results. In recent years, there has been a growing interest in this problem, as evidenced by the numerous publications listed in Harker (1985). The reasons for this renewed interest are many. First, new applications of this concept have arisen which challenge the theoretical underpinnings of this model. The spatial price equilibrium concept is founded on the assumption of perfect or pure competition. The applications to energy markets, steel markets, etc. have led scholars to rethink the basic structure of this model.
The distribution of capital and income in general and its re lation to wealth and economic growth in particular have attrac ted economists' interest for a long time already. Especially the, at least partially, conflicting nature of the two politi cal objectives, namely to obtain substantially large economic growth and a "just" income distribution at the same time, has caused the topic to become a subject of political discussions. As a result of these discussions, numerous models of workers' participation in the profits of growing economies have been developed. To a minor extent and with quite diverse success, some have been implemented in practice. It is far beyond the scope of this work to outline all these approaches from the past centuries and, in particular, the past decades. In economic theory many authors, for instance Kaldor [1955], Krelle [1968], [1983], Pasinetti [1962], Samuelson and Modigli ani [1966], to name but a few, have analyzed the long-term eco nomic implications of workers' saving and investment. While most of this extensive literature is highly interesting, it suffers from the fact that it does not explicitly consider either workers' or capitalists' objectives and thus neglects their impacts on economic growth. Thus, in the framework of a neo-classical model, these objectives and their impacts will be emphasized here.
This thesis is a theoretical study of the optimal dynamic policies of a, to some extent, slowly adjusting firm that faces an exogeneously given technological progress and an exogeneously given business cycle. It belongs to the area of mathematical economics. It is intended to appeal to mathematical economists in the first place, economists in the second place and mathematicians in the third place. It entails an attempt to stretch the limits of the application of deterministic dynamic optimisation to economics, in particular to firm behaviour. A well-known· Dutch economist (and trained mathematician) recently stated in 1 a local university newspaper that mathematical economists give economics a bad reputation, since they formulate their problems from a mathematical point of view and they are only interested in technical, mathematical problems. At the same time, however, "profound as economists may be, when it comes to extending or modifying the existing theory to make it applicable to a certain economic problem, an understanding of optimal control theory (which is the mathematical theory used in this thesis, ovh) based solely on heuristic arguments will often turn out to be inadequate" (SydS