Population aging raises a number of issues regarding the optimality of public debt policy and the systems of public pension provisions that are in use in developed countries. The studies in this book address these questions using computable general equilibrium models. They give illuminating insights and new empirical estimates of future prospects of pay-as-you-go pension schemes in the "big seven" OECD countries, the possible distortions introduced by the pension systems in four large European economies, the effects of lifetime uncertainty in analyzing a potential reform of the Dutch pension system, effects of increasing international mobility of financial capital to pension policies, and public debt reduction policies in relation to possible adverse effects of taxation on wage formation and unemployment.

This book was born out of a five-years research at Sonderforschungsbe reich 303 by the Deutsche Forschungsgemeinschaft (DFG) at Rheinische Friedrich-Wilhelms-Universitiit Bonn and was approved as my doctoral thesis by the Rechts-und Staatswissenschaftliche Fakultiit in December 1994. It was my former colleague Wolfgang Peters who had drawn my atten tion to overlapping-generations models and to problems of intergenerational efficiency and distribution. The subtle connection between the latter two has been fascinating me from the very beginning: redistribution of the results of free trade can become necessary from the point of view of efficiency, although no externalities hamper the development of an economy. In spite of being a matured part of economics, neoclassical growth theory had left many questions unsolved, some of them even unrecognized by a large part of our profession. I took up the challenge to contribute to the investigation of some of these thorny problems. One of these issues is the often quoted idea of the inter generational con tract. Although intergenerational transfers can improve intertemporal effi ciency, the design of pension schemes to achieve an improvement of well-being of some generations without hurting that of any other, is not an easy task in an economy with flexible prices. Quite frequently, only interest rate and growth rate are taken into account when deciding on whether a generation wins or looses."

1.1 Economic issues to be analyzed This research examines two elements of the Swiss market for electricity: the residential electricity demand by time-of-use and the cost structure of municipal electricity distribution utilities. The empirical results of demand and cost elasticities allow the investigation of interesting economic and policy issues such as the desirability of a widespread introduction of time-of-use pricing for residential customers, the desirability of side-by-side competition in the distribution of electricity and, more generally, the economic effects of a reduction of the load factor and of mergers between electric distribution utilities on costs. Desirability of time-of-use pricing In the last decade there has been an intensifying debate in Switzerland about efficacy of electricity rate reforms in order to improve the efficiency of electricity use. This debate was initiated by two main events. First, there was an important growth of electricity consumption. Second, the Chernobyl accident in 1986 aroused widespread public concern about the problems associated with nuclear power and waste disposal. As a result, in 1991 the Swiss voted, in a referendum, a lO-year moratorium on the 2 construction of new nuclear power plants. Moreover, plans to expand production of hydroelectric power (construction of new dams or expanding existing ones) have been stiffly opposed by environmental groups. These developments have consistently curtailed potential expansion of domestic electricity supply. As a result, Switzerland during the winter has to import electricity from foreign countries.

The present book was accepted as a dissertation at the Humboldt Universitat zu Berlin in summer 1996. I am very much obliged to thank my advisor, Professor Wolfgang Hardie, for the continuous, always inspiring support and for opening me the world of non parametric statistics. Without him I probably would have worked on a different, less exciting topic and this book would not exist. Also, I would like to thank my second advisor, Professor Helmut Liitkepohl, for his excellent introduction to time series analysis and for always helpful comments on my work. This work was financially supported by the Deutsche Forschungsgemein schaft, in the first stage while I was a member of the Graduiertenkolleg "Ap plied Microeconomics," and later when I came to the Sonderforschungsbereich 373. For an interestingly widespread academic surrounding I want to thank the members of the Graduiertenkolleg and the Sonderforschungsbereich, es pecially Stefan Sperlich and Axel Werwatz. For the use of XploRe and many other issues I received substantial help from my colleagues Sigbert Klinke, Thomas Kotter, Marlene Miiller and Swetlana Schmelzer. Concerning many central topics of this dissertation, helpful and improving comments were given by Jorg Breitung, Helmut Herwartz, RolfTschernig and Lijian Yang, who also revised most parts of the manuscript. I have much reason to thank them for their help. Of course, all remaining errors are mine. Berlin, July 1997 CHRISTIAN M 0 HAFNER Contents Preface . . . . . IX List of Tables ."

Valuing and Investing in Equities: CROCI: Cash Return on Capital Investment develops a common-sense framework for value investors. By distinguishing investors from speculators, it acknowledges the variety of styles and goals in the financial markets. After explaining the intuition behind due diligence, portfolio construction, and stock picking, it shows the reader how to perform these steps and how to evaluate their results. Francesco Curto illuminates the costs and opportunities afforded by valuation strategies, inflation, and bubbles, emphasizing their effects on each other within the CROCI framework. Balancing analytics with an engaging clarity, the book neatly describes a comprehensive, time-tested approach to investing. Annual returns from this investment approach demand everyone's attention.

We live in a time of economic virtualism, whereby our lives are made to conform to the virtual reality of economic thought. Globalization, transnational capitalism, structural adjustment programmes and the decay of welfare are all signs of the growing power of economics, one of the most potent forces of recent decades. In the last thirty years, economics has ceased to be just an academic discipline concerned with the study of economy, and has come to be the only legitimate way to think about all aspects of society and how we order our lives. Economic models are no longer measured against the world they seek to describe, but instead the world is measured against them, found wanting and made to conform.This profound and dangerous change in the power of abstract economics to shape the lives of people in rich and poor countries alike is the subject of this interdisciplinary study. Contributors show how economics has come to portray a virtual reality -- a world that seems real but is merely a reflection of a neo-classical model -- and how governments, the World Bank and the IMF combine to stamp the world with a virtual image that condemns as irrational our local social and cultural arrangements. Further, it is argued that virtualism represents the worrying emergence of new forms of abstraction in the political economy, of which economics is just one example.

Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.

1. 1 Introduction In economics, one often observes time series that exhibit different patterns of qualitative behavior, both regular and irregular, symmetric and asymmetric. There exist two different perspectives to explain this kind of behavior within the framework of a dynamical model. The traditional belief is that the time evolution of the series can be explained by a linear dynamic model that is exogenously disturbed by a stochastic process. In that case, the observed irregular behavior is explained by the influence of external random shocks which do not necessarily have an economic reason. A more recent theory has evolved in economics that attributes the patterns of change in economic time series to an underlying nonlinear structure, which means that fluctua tions can as well be caused endogenously by the influence of market forces, preference relations, or technological progress. One of the main reasons why nonlinear dynamic models are so interesting to economists is that they are able to produce a great variety of possible dynamic outcomes - from regular predictable behavior to the most complex irregular behavior - rich enough to meet the economists' objectives of modeling. The traditional linear models can only capture a limited number of possi ble dynamic phenomena, which are basically convergence to an equilibrium point, steady oscillations, and unbounded divergence. In any case, for a lin ear system one can write down exactly the solutions to a set of differential or difference equations and classify them."

The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice.

This book provides a self-contained account of periodic models for seasonally observed economic time series with stochastic trends. Two key concepts are periodic integration and periodic cointegration. Periodic integration implies that a seasonally varying differencing filter is required to remove a stochastic trend. Periodic cointegration amounts to allowing cointegration paort-term adjustment parameters to vary with the season. The emphasis is on useful econrameters and shometric models that explicitly describe seasonal variation and can reasonably be interpreted in terms of economic behaviour. The analysis considers econometric theory, Monte Carlo simulation, and forecasting, and it is illustrated with numerous empirical time series. A key feature of the proposed models is that changing seasonal fluctuations depend on the trend and business cycle fluctuations. In the case of such dependence, it is shown that seasonal adjustment leads to inappropriate results.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

Migration, commuting, and tourism are prominent phenomena demonstrating the political and economic relevance of the spatial choice behavior of households. The identification of the determinants and effects of the households' location choice is necessary for both entrepreneurial and policy planners who attempt to predict (or regulate) the future demand for location-specific commodities, such as infrastructure, land, or housing, and the supply of labor. Microeconomic studies of the spatial behavior of individuals have typically focused upon the demand for a single, homogeneous, yet location-specific com 2 modity (such as land or housing ) or their supply of labor3 and investigated the formation of location-specific prices and wages in the presence of transportation and migration costs or analyzed the individual-and location-specific character istics triggering spatial rather than quantitative or temporal adjustments. In contrast to many theoretical analyses, empirical studies of the causes or con sequences of individual demand for location-specific commodities have often considered several "brands" of a heterogeneous good that are offered at various locations, are perfect substitutes, and may be produced by varying production 4 technologies. lCf. Alonso (1964) 2Cf. Muth (1969). 3Cf. Sjaastad (1962) and Greenwood (1975)."

This study is a revised version of my doctoral dissertation at the Economics Department of the University of Munich. I want to take the opportunity to express my gratitude to some people who have helped me in my work. My greatest thanks go to the supervisor of this dissertation, Professor Claude Billinger. Bis ideas have formed the basis of my work. Be permanently sup ported it with a host of ideas, criticism and encouragement. Furthermore, he provided a stimulating research environment at SEMECON. This study would not have been possible in this form without the help of my present and former colleagues at SEMECON. I am indebted to Rudolf Kohne-Volland, Monika Sebold-Bender and Ulrich Woitek for providing soft ware and guidance for the data analysis. Discussions with them and with Thilo Weser have helped me to take many hurdles, particularly in the early stages of the project. My sincere thanks go to them all. I had the opportunity to present a former version of my growth model at a workshop of Professor Klaus Zimmermann. I want to thank all the parti cipants for their helpful comments. I also acknowledge critical and constructive comments from an anonymous referee. Table of Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Part I. Methodology 1. Importance of Stylized Facts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.1 Limitations of statistical testing. . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Evaluating economic models. . . . . . . . . . . . . . . . . . .. . . . 11 . . . . . . 2. Further Methodological Issues . . . . . . . . . . . . . . . . . .. . . . 13 . . . . . ."

This book brings together a wide range of topics and perspectives in the growing field of Classification and related methods of Exploratory and Multivariate Data Analysis. It gives a broad view on the state ofthe art, useful for those in the scientific community who gather data and seek tools for analyzing and interpreting large sets of data. As it presents a wide field of applications, this book is not only of interest for data analysts, mathematicians and statisticians, but also for scientists from many areas and disciplines concerned with real data, e. g. , medicine, biology, astronomy, image analysis, pattern recognition, social sciences, psychology, marketing, etc. It contains 79 invited or selected and refereed papers presented during the Fourth Bi- ennial Conference of the International Federation of Classification Societies (IFCS'93) held in Paris. Previous conferences were held at Aachen (Germany), Charlottesville (USA) and Edinburgh (U. K. ). The conference at Paris emerged from the elose coop- eration between the eight members of the IFCS: British Classification Society (BCS), Classification Society of North America (CSNA), Gesellschaft fur Klassifikation (GfKl), J apanese Classification Society (J CS), Jugoslovenska Sekcija za Klasifikacije (JSK), Societe Francophone de Classification (SFC), Societa. Italiana di Statistica (SIS), Vereniging voor Ordinatie en Classificatie (VOC), and was organized by INRIA ("Institut National de Recherche en Informatique et en Automatique"), Rocquencourt and the "Ecole Nationale Superieure des Telecommuni- cations," Paris.

This two-volume work aims to present as completely as possible the methods of statistical inference with special reference to their economic applications. It is a well-integrated textbook presenting a wide diversity of models in a coherent and unified framework. The reader will find a description not only of the classical concepts and results of mathematical statistics, but also of concepts and methods recently developed for the specific needs of econometrics. Although the two volumes do not demand a high level of mathematical knowledge, they do draw on linear algebra and probability theory. The breadth of approaches and the extensive coverage of this two-volume work provide for a thorough and entirely self-contained course in modern economics. Volume 1 provides an introduction to general concepts and methods in statistics and econometrics, and goes on to cover estimation and prediction. Volume 2 focuses on testing, confidence regions, model selection, and asymptotic theory.

In this compelling 1995 book, David Hendry and Mary Morgan bring together the classic papers of the pioneer econometricians. Together, these papers form the foundations of econometric thought. They are essential reading for anyone seeking to understand the aims, method and methodology of econometrics and the development of this statistical approach in economics. However, because they are technically straightforward, the book is also accessible to students and non-specialists. An editorial commentary places the readings in their historical context and indicates the continuing relevance of these early, yet highly sophisticated, works for current econometric analysis. While this book provides a companion volume to Mary Morgan's acclaimed The History of Econometric Ideas, the editors' commentary both adds to that earlier volume and also provides a stand-alone and synthetic account of the development of econometrics.

Many models in this volume can be used in solving portfolio problems, in assessing forecasts, in understanding the possible effects of shocks and disturbances.

The papers collected in this volume are contributions to T.I.Tech./K.E.S. Conference on Nonlinear and Convex Analysis in Economic Theory, which was held at Keio University, July 2-4, 1993. The conference was organized by Tokyo Institute of Technology (T. I. Tech.) and the Keio Economic Society (K. E. S.) , and supported by Nihon Keizai Shimbun Inc .. A lot of economic problems can be formulated as constrained optimiza tions and equilibrations of their solutions. Nonlinear-convex analysis has been supplying economists with indispensable mathematical machineries for these problems arising in economic theory. Conversely, mathematicians working in this discipline of analysis have been stimulated by various mathematical difficulties raised by economic the ories. Although our special emphasis was laid upon "nonlinearity" and "con vexity" in relation with economic theories, we also incorporated stochastic aspects of financial economics in our project taking account of the remark able rapid growth of this discipline during the last decade. The conference was designed to bring together those mathematicians who were seriously interested in getting new challenging stimuli from economic theories with those economists who were seeking for effective mathematical weapons for their researches. Thirty invited talks (six of them were plenary talks) given at the conf- ence were roughly classified under the following six headings : 1) Nonlinear Dynamical Systems and Business Fluctuations, . 2) Fixed Point Theory, 3) Convex Analysis and Optimization, 4) Eigenvalue of Positive Operators, 5) Stochastic Analysis and Financial Market, 6) General Equilibrium Analysis.

As large physical capital stock projects need long periods to be built, a time-to-build specification is incorporated in factor demand models. Time-to-build and adjustment costs dynamics are identified since by the first moving average dynamics, whereas by the latter autoregressive dynamics are induced. Empirical evidence for time-to-build is obtained from data from the Dutch construction industry and by the estimation result from the manufacturing industry of six OECD countries.

This book presents a review of recent developments in the theory and construction of index numbers using the stochastic approach, demonstrating the versatility of this approach in handling various index number problems within a single conceptual framework. It also contains a brief, but complete, review of the existing approaches to index numbers with illustrative numerical examples.;The stochastic approach considers the index number problem as a signal extraction problem. The strength and reliability of the signal extracted from price and quantity changes for different commodities depends on the messages received and the information content of the messages. The most important applications of the new approach are to be found in the context of measuring rate of inflation and fixed and chain base index numbers for temporal comparisons and for spatial inter-country comparisons - the latter generally require special index number formulae that result in transitive and base invariant comparisons.

1. 1 Integrating results The empirical study of macroeconomic time series is interesting. It is also difficult and not immediately rewarding. Many statistical and economic issues are involved. The main problems is that these issues are so interrelated that it does not seem sensible to address them one at a time. As soon as one sets about the making of a model of macroeconomic time series one has to choose which problems one will try to tackle oneself and which problems one will leave unresolved or to be solved by others. From a theoretic point of view it can be fruitful to concentrate oneself on only one problem. If one follows this strategy in empirical application one runs a serious risk of making a seemingly interesting model, that is just a corollary of some important mistake in the handling of other problems. Two well known examples of statistical artifacts are the finding of Kuznets "pseudo-waves" of about 20 years in economic activity (Sargent (1979, p. 248)) and the "spurious regression" of macroeconomic time series described in Granger and Newbold (1986, 6. 4). The easiest way to get away with possible mistakes is to admit they may be there in the first place, but that time constraints and unfamiliarity with the solution do not allow the researcher to do something about them. This can be a viable argument."

As a new type of technique, simplicial methods have yielded extremely important contributions toward solutions of a system of nonlinear equations. Theoretical investigations and numerical tests have shown that the performance of simplicial methods depends critically on the triangulations underlying them. This monograph describes some recent developments in triangulations and simplicial methods. It includes the D1-triangulation and its applications to simplicial methods. As a result, efficiency of simplicial methods has been improved significantly. Thus more effective simplicial methods have been developed.

A macroeconomic disequilibrium model is developed for the Federal Republic of Germany. Starting with a microeconomic model of firm's behaviour, the optimal dynamic adjustment of employment and investment is derived. The model of the firm is complemented by an explicite aggregation procedure which allows to derive macroeconomic relations. The model is estimated with macroeconomic data for the Federal Republic of Germany. An important feature is the consistent introduction of dynamic adjustment into a model of the firm. A new method is the particular approach of a delayed adjustment of employment and investment. The estimation results show significant underutilizations of labour and capital and indicate the importance of supply constraints for imports and exports. As the most prominent result, they reveal the importance of the slow adjustment of employment and investment for the macroeconomic situation in Germany and especially for the persistence of high unemployment in the eighties.

A discussion of various aspects of dynamic behavior of empirical macroeconomic, and in particular, macroeconometric models, is presented in this book. The book addresses in depth several theoretical and practical aspects concerning the modeling and analysis of long-run equilibrium behavior, adjustment dynamics and stability. Tools are developed to identify and interpret the main determinants of the dynamics of models. The tools involve, among others, error-correction mechanisms, eigenvalue analysis, feedback closure rules, graph theory, learning behavior, steady-state analysis, and stochastic simulation. Their usefulness is demonstrated by interesting applications to a number of well-known national and multi-national models.

This graduate level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic.

This book reports new developments in applied econometrics. All papers originated in two international workshops that were organized in the University of Munich on July 6-7, 1989, and on January 11 - 12, 1990. Financial support for these conferences by the University of Munich and the Thyssen Foundation is gratefully acknowledged. Since then all papers were substantially revised and updated. We wish to thank all authors for their patience with the revisions and Thomas Bauer, Lucie Merkle and Gisela Loos for editorial help. The ftrst section of the book collects contributions that address new "Methodological Developments." Two of them deal with problems in microeconometrics, the other two consider multi-equation systems. Martin Kukuk and Gerd Ronning treat "Ordinal Variables in Microeconometric Models." They especially deal with the case of limited-dependent variable models where some exogenous variables are either measured on an interval scale or a nominal scale. They discuss and compare two methods to deal with the problem. In his paper on "Goodness of Fit in Qualitative Choice Models: Review and Evaluation," Klaus F. Zimmermann investigates methods to summarize the predictive quality of models that deal with discrete alternatives. For these models, a widely accepted measure for evaluation like the R2, as in the case of ordinary least squares, does not exist. The paper summarizes the literature and suggests reasonable choices for evaluation on the basis of large-scale Monte Carlo investigations.