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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.
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
Stochastic Programming offers models and methods for decision problems wheresome of the data are uncertain. These models have features and structural properties which are preferably exploited by SP methods within the solution process. This work contributes to the methodology for two-stagemodels. In these models the objective function is given as an integral, whose integrand depends on a random vector, on its probability measure and on a decision. The main results of this work have been derived with the intention to ease these difficulties: After investigating duality relations for convex optimization problems with supply/demand and prices being treated as parameters, a stability criterion is stated and proves subdifferentiability of the value function. This criterion is employed for proving the existence of bilinear functions, which minorize/majorize the integrand. Additionally, these minorants/majorants support the integrand on generalized barycenters of simplicial faces of specially shaped polytopes and amount to an approach which is denoted barycentric approximation scheme.
1. Introduction 1 2. Identification Analysis and F.I.M.L. Estimation for the K-Mode1 10 3. Identification Analysis and F.I.ML. Estimation for the C-Model 23 4. Identification Analysis and F.I.M.L. Estimation for the AB-Model 32 5. Impulse Response Analysis and Forecast Error Variance Decomposition in SVAR Modeling 44 5 .a Impulse Response Analysis 44 5.b Variance Decomposition (by Antonio Lanzarotti) 51 6. Long-run A-priori Information. Deterministic Components. Cointegration 58 6.a Long-run A-priori Information 58 6.b Deterministic Components 62 6.c Cointegration 65 7. The Working of an AB-Model 71 Annex 1: The Notions ofReduced Form and Structure in Structural VAR Modeling 83 Annex 2: Some Considerations on the Semantics, Choice and Management of the K, C and AB-Models 87 Appendix A 93 Appendix B 96 Appendix C (by Antonio Lanzarotti and Mario Seghelini) 99 Appendix D (by Antonio Lanzarotti and Mario Seghelini) 109 References 128 Foreword In recent years a growing interest in the structural VAR approach (SVAR) has followed the path-breaking works by Blanchard and Watson (1986), Bemanke (1986) and Sims (1986), especially in U.S. applied macroeconometric literature. The approach can be used in two different, partially overlapping directions: the interpretation ofbusiness cycle fluctuations of a small number of significantmacroeconomic variables and the identification of the effects of different policies.
In February 1992, I defended my doctoral thesis: Engineering Optimiza tion - selected contributions (IMSOR, The Technical University of Den mark, 1992, p. 92). This dissertation presents retrospectively my central contributions to the theoretical and applied aspects of optimization. When I had finished my thesis I became interested in editing a volume related to a new expanding area of applied optimization. I considered several approaches: simulated annealing, tabu search, genetic algorithms, neural networks, heuristics, expert systems, generalized multipliers, etc. Finally, I decided to edit a volume related to simulated annealing. My main three reasons for this choice were the following: (i) During the last four years my colleagues at IMSOR and I have car ried out several applied projects where simulated annealing was an essential. element in the problem-solving process. Most of the avail able reports and papers have been written in Danish. After a short review I was convinced that most of these works deserved to be pub lished for a wider audience. (ii) After the first reported applications of simulated annealing (1983- 1985), a tremendous amount of theoretical and applied work have been published within many different disciplines. Thus, I believe that simulated annealing is an approach that deserves to be in the curricula of, e.g. Engineering, Physics, Operations Research, Math ematical Programming, Economics, System Sciences, etc. (iii) A contact to an international network of well-known researchers showed that several individuals were willing to contribute to such a volume.
In this book overlapping generations economies are analysed from a game theoretical point of view and the social acceptability of consumption allocations is studied in infinite horizon models of pure exchange economieswith agents with finite lifetimes who behave cooperatively. The core of such economies and its relation with competitive equilibria, both walrasian and monetary and the essential characteristics of the overlapping generations model are examined. The author defines the problem of trust in intertemporal consumption allocations as a question of belonging or not to the core of economy and provides a full characterization of the core allocations for n-goods pure exchange economies with one agent per generation: a consumption allocation belongs to the core if and only it is Pareto optimal and Sequentially Individually Rational. From this it follows that for one commodity economies no consumption allocation involving intertemporal transfers can belong to the core of the economy. In other words, no monetary equilibrium is socially viable. This result is no longer true for many goods models. For that case it is demonstrated that there exist bounds on the real value of equilibrium money purchases beyond which monetary equilibria are not socially viableand with many agents in every generation it is shown that as the economy becomes large and monetary (as well as IOU) equilibria become eventually excluded from the core of the economy. These results provide an analytical rationale for the fact that in most countries fiat money is legal tender.