Download Free Robust Methods And Asymptotic Theory In Nonlinear Econometrics Book in PDF and EPUB Free Download. You can read online Robust Methods And Asymptotic Theory In Nonlinear Econometrics and write the review.

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.
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 book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.
Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
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.