This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.

This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.

Many relationships in economics, and also in other fields, are both dynamic and nonlinear. A major advance in econometrics over the last fifteen years has been the development of a theory of estimation and inference for dy namic nonlinear models. This advance was accompanied by improvements in computer technology that facilitate the practical implementation of such estimation methods. In two articles in Econometric Reviews, i.e., Pötscher and Prucha {1991a,b), we provided -an expository discussion of the basic structure of the asymptotic theory of M-estimators in dynamic nonlinear models and a review of the literature up to the beginning of this decade. Among others, the class of M-estimators contains least mean distance estimators (includ ing maximum likelihood estimators) and generalized method of moment estimators. The present book expands and revises the discussion in those articles. It is geared towards the professional econometrician or statistician. Besides reviewing the literature we also presented in the above men tioned articles a number of then new results. One example is a consis tency result for the case where the identifiable uniqueness condition fails.

This volume presents the results of the 6th Input-Output Meeting, organized in Warsaw, Poland, December 16-18, 1985 by IIASA and the Institute of Econometrics and Statistics, University of Lodz. The main aim of the meeting was to demonstrate the use of integrated input-output models in economic policy making, both at the national and the industrial level.

In the past, technological as well as economic forces dominated the evolution of industrial structures: these factors have been treated extensively in numerous studies. However, another major factor which has begun to have a decisive influ ence on the performance of the chemical industry is technological risk and public and environmental health considerations, in particular those related to toxic and hazardous substances used in industrial production processes. The issues of con trolling process risk, waste streams, and potential environmental consequences of accidental or routine release of hazardous chemicals are rapidly gaining in impor tance vis CI vis narrow economic considerations, and are increasingly reflected in national and international legislation. In the context of several ongoing R&D projects aiming at the development of a new generation of tools for "intelligent" decision support, two related problem areas that have been identified are: (i) Structuring the industry or plant for the minimum cost of production as well as least risk - e.g., toxicity of chemicals involved. In this multi-criteria framework, we seek to resolve the conflict between industrial structure or plant design established by economic considerations and the one shaped by environmental concerns. This can be formulated as a design problem for nor mal production conditions. In section 3.1. and 3.2. an approach on how to deal with this problem at the industry and plant level is discussed.

Since the beginning of the fifties, the ruling paradigm in the discipline of economics has been that of a competitive general equilibrium. Associated dynamic analyses have therefore been preoccupied with the stability of this equilibrium state, corresponding simply to studies of comparative statics. The need to permeate the boundaries of this paradigm in order to open up new pathways for genuine dynamic analysis is now pressing. The contributions contained in this volume spring from this very ambition. A growing circle of economists have recently been inspired by two distinct but complementary sources: (i) the pathbreaking work of Joseph Schumpeter, and (ii) recent contributions to physics, chemistry and theoretical biology. It turns out that problems which are firmly rooted in the economic discipline, such as innovation, technological change, business cycles and economic development, contain many clear parallels with phenomena from the natural sciences such as the slaving principle, adiabatic elimination and self-organization. In such dynamic worlds, adjustment processes and adaptive behaviour are modelled with the aid of the mathematical theory of nonlinear dynamical systems. The dynamics is defined for a much wider set of conditions or states than simply a set of competitive equilibria. A common objective is to study and classify ways in which the qualitative properties of each system change as the parameters describing the system vary.

These Proceedings report the scientific results of an International Workshop on Large-Scale Modelling and Interactive Decision Analysis organized Jointly by the System and Decision Sciences Program of the International Institute for Applied Systems Analysis (IIASA, located in Laxenburg, Austria), and the Institute for Informatics of the Academy of Sciences of the GDR (located in Berlin, GDR). The Workshop was held at a historically well-known place - the Wartburg Castl- near Eisenach (GDR). (Here Martin Luther translated the Bible into German.) More than fifty scientists representing thirteen countries participated. This Workshop is one of a series of meetings organizE!d by or In collaboration with IIASA about which two of the Lecture Notes In Economics and Mathematical Systems have already reported (Voi. 229 and Vol. 246). This time the aim of the meeting was to discuss methodological and practical problems associated with the modelling of large-scale systems and new approaches In interactive decision analysis based on advanced information processing systems.

Cost Analysis is an emerging sub-discipline of Economics and Operations Research. This is the first collection of readings that spans the discipline. The contributions are both theoretical and applications oriented. This book is directed to researchers in Economics, Operations Research, Industrial Engineering, and Managerial Accounting. In particular, the book provides an overview of the types of problems that are of interest to cost researchers. These papers are a subset of the papers that were presented at the 1989 Joint National Meeting of the Institute of Cost Analysis and the National Estimating Society in Washington, D.C.

This book is intended for use in a rigorous introductory PhD level course in econometrics.