Data envelopment analysis develops a set of nonparametric and semiparametric techniques for measuring economic efficiency among firms and nonprofit organizations. Over the past decade this technique has found most widespread applications in public sector organizations. However these applications have been mostly static. This monograph extends this static framework of efficiency analysis in several new directions. These include but are not limited to the following: (1) a dynamic view of the production and cost frontier, where capital inputs are treated differently from the current inputs, (2) a direct role of the technological progress and regress, which is so often stressed in total factor productivity discussion in modem growth theory in economics, (3) stochastic efficiency in a dynamic setting, where reliability improvement competes with technical efficiency, (4) flexible manufacturing systems, where flexibility of the production process and the economies of scope play an important role in efficiency analysis and (5) the role of economic factors such as externalities and input interdependences. Efficiency is viewed here in the framework of a general systems theory model. Such a view is intended to broaden the scope of applications of this promising new technique of data envelopment analysis. The monograph stresses the various applied aspects of the dynamic theory, so that it can be empirically implemented in different situations. As far as possible abstract mathematical treatments are avoided and emphasis placed on the statistical examples and empirical illustrations.

This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover stochastic differential equations, the concentration here on inference makes this book stand out. No other direct competitors are known to date. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners and students with minimal mathematical background. What's more, because of the many R programs, the information here is appropriate for many mathematically well educated practitioners, too.

hen my husband died in 1973 I had to go through his W papers. Some of them were still in manuscript form and had never before been published. I selected several of these, plus a number of other articles that had appeared in periodicals but were no longer in print. This book is the result. At my request Richard Ebeling wrote an introduction which he has done in great detail. The depth of Ebeling's understanding of my husband's work is certainly apparent in his writing. I am pleased to have the Ludwig von Mises Institute present this volume to the public. Margit von Mises New York City September 1989 vii Introduction I I n the 1920s and the 1930s, Ludwig von Mises was recognized as one of the leading economic theorists on the European Conti nent. I F. A. Hayek has said that Mises's critique of the possibilities for economic calculation under socialism had "the most profound impression on my generation . . . . To none of us '" who read his] book Socialism] when it appeared was the world ever the same again., 2 Lord Lionel Robbins, in introducing the Austrian School literature on money and the trade cycle to English-speaking readers in 1931, emphasized the "marvelous renaissance" the "School of Vienna" had experienced "under the leadership of . . . Professor Mises."

Econometrics of Health Care - which we have sometimes called 'medico metrics' - is a field in full expansion. The reasons are numerous: our knowl edge of quantitative relations in the field of health econometrics is far from being perfect, a large number of analytical difficulties - combining medical (latent factors, e. g. ) and economic facts (spatial behaviour, e. g. ) are faced by the research worker, medical and pharmaceutical techniques change rapidly, medical costs rocket more than proportionally with available resources, of being tightened. medical budgets are in the process So it is not surprising that the practice of 'hygieconometrics' - to produce a neologism - is more and more included in the programmes of econometri cians. The Applied Econometrics Association has devoted to the topic two symposia in less than three years (Lyons, February 1983; Rotterdam, December 1985), without experiencing any difficulties in getting valuable papers: on econometrics of risks and medical insurance, on the measurement of health status and of efficiency of medical techniques, on general models allowing simulation. These were the themes for the second meeting, but other aspects of medical-economic problems had presented themselves already to the analyst: medical decision making and its consequences, the behaviour of the actors - patients and physicians -, regional medicometrics and what not: some of them have been covered by the first meeting. Finally, in July 1988 took place in Lyons the Fourth International Conference on System Science in Health Care; it should not be astonishing ."

Empirical Studies on Volatility in International Stock Markets describes the existing techniques for the measurement and estimation of volatility in international stock markets with emphasis on the SV model and its empirical application. Eugenie Hol develops various extensions of the SV model, which allow for additional variables in both the mean and the variance equation. In addition, the forecasting performance of SV models is compared not only to that of the well-established GARCH model but also to implied volatility and so-called realised volatility models which are based on intraday volatility measures. The intended readers are financial professionals who seek to obtain more accurate volatility forecasts and wish to gain insight about state-of-the-art volatility modelling techniques and their empirical value, and academic researchers and students who are interested in financial market volatility and want to obtain an updated overview of the various methods available in this area.

This highly useful book contains methodology for the analysis of data that arise from multiscale processes. It brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. These methods can handle different amounts of prior knowledge at different scales, as often occurs in practice.

1. A self-contained introduction to probability, exchangeability and Bayes rule provides a theoretical understanding of the applied material.

2. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.

3. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

The place in survival analysis now occupied by proportional hazards models and their generalizations is so large that it is no longer conceivable to offer a course on the subject without devoting at least half of the content to this topic alone. This book focuses on the theory and applications of a very broad class of models - proportional hazards and non-proportional hazards models, the former being viewed as a special case of the latter - which underlie modern survival analysis. Researchers and students alike will find that this text differs from most recent works in that it is mostly concerned with methodological issues rather than the analysis itself.

Written by a world leader in the field and aimed at researchers in applied and engineering sciences, this brilliant text has as its main goal imparting an understanding of the methods so that practitioners can make immediate use of existing algorithms and software, and so that researchers can extend the state of the art and find new applications. It includes algorithms on seeking feasibility and analyzing infeasibility, as well as describing new and surprising applications.

This book provides a synthesis of some recent issues and an up-to-date treatment of some of the major important issues in distributional analysis that I have covered in my previous book Ethical Social Index Numbers, which was widely accepted by students, teachers, researchers and practitioners in the area. Wide coverage of on-going and advanced topics and their analytical, articulate and authoritative p- sentation make the book theoretically and methodologically quite contemporary and inclusive, and highly responsive to the practical problems of recent concern. Since many countries of the world are still characterized by high levels of income inequality, Chap. 1 analyzes the problems of income inequality measurement in detail. Poverty alleviation is an overriding goal of development and social policy. To formulate antipoverty policies, research on poverty has mostly focused on inco- based indices. In view of this, a substantive analysis of income-based poverty has been presented in Chap. 2. The subject of Chap. 3 is people's perception about income inequality in terms of deprivation. Since polarization is of current concern to analysts and social decisi- makers, a discussion on polarization is presented in Chap. 4.

Income Elasticity and Economic Development Methods and Applications is mainly concerned with methods of estimating income elasticity. This field is connected with economic development that can be achieved by reducing income inequality. This is highly relevant in today's world, where the gap between rich and poor is widening with the growth of economic development. Income Elasticity and Economic Development Methods and Applications provides a good example in showing how to calculate income elasticity, using a number of methods from widely available grouped data. Some of the techniques presented here can be used in a wide range of policy areas in all developed, developing and under-developed countries. Policy analysts, economists, business analysts and market researchers will find this book very useful.

The econometric consequences of nonstationary data have wide ranging im plications for empirical research in economics. Specifically, these issues have implications for the study of empirical relations such as a money demand func tion that links macroeconomic aggregates: real money balances, real income and a nominal interest rate. Traditional monetary theory predicts that these nonsta tionary series form a cointegrating relation and accordingly, that the dynamics of a vector process comprised of these variables generates distinct patterns. Re cent econometric developments designed to cope with nonstationarities have changed the course of empirical research in the area, but many fundamental challenges, for example the issue of identification, remain. This book represents the efforts undertaken by the authors in recent years in an effort to determine the consequences that nonstationarity has for the study of aggregate money demand relations. We have brought together an empirical methodology that we find useful in conducting empirical research. Some of the work was undertaken during the authors' sabbatical periods and we wish to acknowledge the generous support of Arizona State University and Michigan State University respectively. Professor Hoffman wishes to acknowledge the support of the Fulbright-Hays Foundation that supported sabbattical research in Europe and separate support of the Council of 100 Summer Research Program at Arizona State University."

Lawrence Klein, University of Pennsylvania Jaime Marquez, Federal Reserve BoarrI* All examination of the economics literature over the last twenty years reveals a marked tendency towards polarisation. On the one hand, there has been a propensity to develop theoretical models which have little connection with either empirical verification or problems requiring immediate attention. On the other iland, empirical analyses are generally typified by testing for its own sake, with limited examination of the implications of the results. As a result, the number of papers confronting theory with facts towards the solution of economic problems has been on the decline for years. To fill this growing gap in the literature, we have invited a number of authors to write papers using both theoretical and empirical techniques to address current issues of interest to the profession at large: the US trade deficit and the global implications of policies that attempt to reduce it, the international ramifications of the debt crisis, the international oil market and its implications for the US oil industry, and the development of new econometric techniques. In addressing these issues, each author has approached the subject matter from an eclectic standpoint - that is, avoiding strict adherence to a given doctrine.

Markov chains have increasingly become useful way of capturing stochastic nature of many economic and financial variables. Although the hidden Markov processes have been widely employed for some time in many engineering applications e.g. speech recognition, its effectiveness has now been recognized in areas of social science research as well. The main aim of Hidden Markov Models: Applications to Financial Economics is to make such techniques available to more researchers in financial economics. As such we only cover the necessary theoretical aspects in each chapter while focusing on real life applications using contemporary data mainly from OECD group of countries. The underlying assumption here is that the researchers in financial economics would be familiar with such application although empirical techniques would be more traditional econometrics. Keeping the application level in a more familiar level, we focus on the methodology based on hidden Markov processes. This will, we believe, help the reader to develop more in-depth understanding of the modeling issues thereby benefiting their future research.

In a relatively short period of time, data envelopment analysis (DEA) has grown into a powerful analytical tool for measuring and evaluating performance. DEA is computational at its core and this book is one of several Springer aim to publish on the subject. This work deals with the micro aspects of handling and modeling data issues in DEA problems. It is a handbook treatment dealing with specific data problems, including imprecise data and undesirable outputs.

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."

Patrick Artus and Yves Barroux The Applied Econometric Association organised an international conference on "Monetary and Financial Models" in Geneva in January 1987. The purpose of this book is to make available to the public a choice of the papers that were presented at the conference. The selected papers all deal with the setting of monetary targets and the effects of monetary policy on the economy as well as with the analysis of the financial behaviours of economic agents. Other papers presented at the same conference but dealing with the external aspects of monetary policy (exchange rate policy, international coordination of economic policies, international transmission of business cycles, . . . ) are the matter of a distinct publication. The papers put together to make up this book either are theoretical research contributions or consist of applied statistical or econometric work. It seemed to be more logical to start with the more theoretical papers. The topics tackled in the first two parts of the book have in common the fact that they appeared just recently in the field of economic research and deal with the analysis of the behaviour of Central Banks. They analyse this behaviour so as to be able to exhibit its major determinants as well as revealed preferences of Central Banks: this topic comes under the caption "optimal monetary policy and reaction function of the monetary authorities."

Simulation methods are revolutionizing the practice of applied economic analysis. This volume collects eighteen chapters written by leading researchers from prestigious research institutions the world over. The common denominator of the papers is their relevance for applied research in environmental and resource economics.

The topics range from discrete choice modeling with heterogeneity of preferences, to Bayesian estimation, to Monte Carlo experiments, to structural estimation of Kuhn-Tucker demand systems, to evaluation of simulation noise in maximum simulated likelihood estimates, to dynamic natural resource modeling. Empirical cases are used to show the practical use and the results brought forth by the different methods.

A non-technical introduction to the question of modeling with time-varying parameters, using the beta coefficient from Financial Economics as the main example. After a brief introduction to this coefficient for those not versed in finance, the book presents a number of rather well known tests for constant coefficients and then performs these tests on data from the Stockholm Exchange. The Kalman filter is then introduced and a simple example is used to demonstrate the power of the filter. The filter is then used to estimate the market model with time-varying betas. The book concludes with further examples of how the Kalman filter may be used in estimation models used in analyzing other aspects of finance. Since both the programs and the data used in the book are available for downloading, the book is especially valuable for students and other researchers interested in learning the art of modeling with time varying coefficients.

Non-Parametric Statistical Diagnosis

Productivity growth is a keyword for sustainable economic growth in a knowledge-based society. There has been significant methodological development in the literature on productivity and efficiency analysis, e.g. SFA (Stochastic Frontier Analysis) and DEA (Data Envelopment Analysis). All these methodological developments should be matched with applications in order to provide practical implications for private and public decision-makers. This volume provides a collection of up-to-date and new applications of productivity and efficiency analysis. In particular, the case studies cover various economic issues in the Asia-Pacific region. The authors analyze the performance of manufacturing firms, banks, venture capital, broadcasting firms, as well as the issues of efficiency in the education sector, regional development, and defense industry. These case studies will shed light on the potential contribution of productivity and efficiency analysis to the enhancement of economic performance.

World-renowned experts in spatial statistics and spatial econometrics present the latest advances in specification and estimation of spatial econometric models. This includes information on the development of tools and software, and various applications. The text introduces new tests and estimators for spatial regression models, including discrete choice and simultaneous equation models. The performance of techniques is demonstrated through simulation results and a wide array of applications related to economic growth, international trade, knowledge externalities, population-employment dynamics, urban crime, land use, and environmental issues. An exciting new text for academics with a theoretical interest in spatial statistics and econometrics, and for practitioners looking for modern and up-to-date techniques.

This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.

This second edition sees the light three years after the first one: too short a time to feel seriously concerned to redesign the entire book, but sufficient to be challenged by the prospect of sharpening our investigation on the working of econometric dynamic models and to be inclined to change the title of the new edition by dropping the "Topics in" of the former edition. After considerable soul searching we agreed to include several results related to topics already covered, as well as additional sections devoted to new and sophisticated techniques, which hinge mostly on the latest research work on linear matrix polynomials by the second author. This explains the growth of chapter one and the deeper insight into representation theorems in the last chapter of the book. The role of the second chapter is that of providing a bridge between the mathematical techniques in the backstage and the econometric profiles in the forefront of dynamic modelling. For this purpose, we decided to add a new section where the reader can find the stochastic rationale of vector autoregressive specifications in econometrics. The third (and last) chapter improves on that of the first edition by re- ing the fruits of the thorough analytic equipment previously drawn up."