Asymptotic Theory For Econometricians
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Author | : Halbert White |
Publisher | : Academic Press |
Total Pages | : 241 |
Release | : 2014-06-28 |
Genre | : Business & Economics |
ISBN | : 1483294420 |
Download Asymptotic Theory for Econometricians Book in PDF, Epub and Kindle
This book is intended to provide a somewhat more comprehensive and unified treatment of large sample theory than has been available previously and to relate the fundamental tools of asymptotic theory directly to many of the estimators of interest to econometricians. In addition, because economic data are generated in a variety of different contexts (time series, cross sections, time series--cross sections), we pay particular attention to the similarities and differences in the techniques appropriate to each of these contexts.
Author | : Halbert White |
Publisher | : Academic Press Incorporated |
Total Pages | : 288 |
Release | : 2001 |
Genre | : Business & Economics |
ISBN | : |
Download Asymptotic Theory for Econometricians Book in PDF, Epub and Kindle
An econometric estimator is a solution to an optimization problem. This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples.
Author | : Benedikt M. Pötscher |
Publisher | : Springer Science & Business Media |
Total Pages | : 307 |
Release | : 2013-03-09 |
Genre | : Business & Economics |
ISBN | : 3662034867 |
Download Dynamic Nonlinear Econometric Models Book in PDF, Epub and Kindle
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.
Author | : H. J. Bierens |
Publisher | : Springer Science & Business Media |
Total Pages | : 211 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 3642455298 |
Download Robust Methods and Asymptotic Theory in Nonlinear Econometrics Book in PDF, Epub and Kindle
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.
Author | : Herman J. Bierens |
Publisher | : Springer |
Total Pages | : 214 |
Release | : 1981 |
Genre | : Business & Economics |
ISBN | : |
Download Robust Methods and Asymptotic Theory in Nonlinear Econometrics Book in PDF, Epub and Kindle
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.
Author | : Yoosoon Chang |
Publisher | : Emerald Group Publishing |
Total Pages | : 360 |
Release | : 2023-04-24 |
Genre | : Business & Economics |
ISBN | : 1837532109 |
Download Essays in Honor of Joon Y. Park Book in PDF, Epub and Kindle
Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.
Author | : William A. Barnett |
Publisher | : Cambridge University Press |
Total Pages | : 512 |
Release | : 1991-06-28 |
Genre | : Business & Economics |
ISBN | : 9780521424318 |
Download Nonparametric and Semiparametric Methods in Econometrics and Statistics Book in PDF, Epub and Kindle
Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Author | : Roger Koenker |
Publisher | : |
Total Pages | : 12 |
Release | : 1986 |
Genre | : |
ISBN | : |
Download Asymptotic Theory and Econometric Practice Book in PDF, Epub and Kindle
Author | : Christian Gourieroux |
Publisher | : Cambridge University Press |
Total Pages | : 548 |
Release | : 1995-10-26 |
Genre | : Business & Economics |
ISBN | : 9780521477451 |
Download Statistics and Econometric Models Book in PDF, Epub and Kindle
This two volume work aims to present as completely as possible the methods of statistical inference with special reference to their economic applications. Volume II focuses on testing, confidence regions, model selection, and asymptotic theory
Author | : Stanislav Anatolyev |
Publisher | : CRC Press |
Total Pages | : 230 |
Release | : 2011-06-07 |
Genre | : Business & Economics |
ISBN | : 1439838267 |
Download Methods for Estimation and Inference in Modern Econometrics Book in PDF, Epub and Kindle
This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference.