Nonlinear Methods in Econometrics PDF Download
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Author: Stephen M. Goldfeld
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
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Book Description
Numerical aptimization; Least squares theory; Confidence intervals and maximum likelihood estimation; Analyses of heteroscedastiaty; Estimation of regressions with dummy dependent variable; Cobb-douglas type functions with multiplicative and additive errors; Estimator behavior for a nonlinear model of production; Autocorrelation in simutaneous equation systems; Estimation of discontinuos parameter changes.
Author: Stephen M. Goldfeld
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
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Book Description
Numerical aptimization; Least squares theory; Confidence intervals and maximum likelihood estimation; Analyses of heteroscedastiaty; Estimation of regressions with dummy dependent variable; Cobb-douglas type functions with multiplicative and additive errors; Estimator behavior for a nonlinear model of production; Autocorrelation in simutaneous equation systems; Estimation of discontinuos parameter changes.
Author: H. J. Bierens
Publisher: Springer Science & Business Media
ISBN: 3642455298
Category : Mathematics
Languages : en
Pages : 211
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Book Description
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: Jeffrey M. Wooldridge
Publisher: South Western Educational Publishing
ISBN: 9780324788907
Category : Econometrics
Languages : en
Pages : 865
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Book Description
INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e International Edition illustrates how empirical researchers think about and apply econometric methods in real-world practice. The text's unique approach reflects the fact that undergraduate econometrics has moved beyond just a set of abstract tools to being genuinely useful for answering questions in business, policy evaluation, and forecasting environments. The systematic approach, which reduces clutter by introducing assumptions only as they are needed, makes absorbing the material easier and leads to better econometric practices. Its unique organization separates topics by the kinds of data being analyzed , leading to an appreciation for the important issues that arise in drawing conclusions from the different kinds of data economists use. Packed with relevant applications, INTRODUCTORY ECONOMETRICS offers a wealth of interesting data sets that can be used to reproduce the examples in the text or as the starting point for original research projects.
Author: Christian Kleiber
Publisher: Springer Science & Business Media
ISBN: 0387773185
Category : Business & Economics
Languages : en
Pages : 229
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Book Description
R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
Author: Takeshi Amemiya
Publisher: Cambridge University Press
ISBN: 9780521662468
Category : Business & Economics
Languages : en
Pages : 472
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Book Description
This collection investigates parametric, semiparametric, nonparametric, and nonlinear estimation techniques in statistical modeling.
Author: Jeffrey M. Wooldridge
Publisher: MIT Press
ISBN: 0262232588
Category : Business & Economics
Languages : en
Pages : 1095
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Book Description
The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
Author: William Edwards Deming
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 298
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Book Description
Introduction to basic concepts of statistics, curve fitting, least squares solution, conditions without parameter, conditions containing parameters. 26 exercises worked out.
Author: Richard J. Murnane
Publisher: Oxford University Press
ISBN: 0199890153
Category : Psychology
Languages : en
Pages : 414
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Book Description
Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.
Author: Johannes Gottlieb
Publisher: Springer Science & Business Media
ISBN: 940091704X
Category : Science
Languages : en
Pages : 307
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Book Description
The Workshop on Parameter Identification and Inverse Problems in Hydrology, Geology and Ecology, Karlsruhe, April 10-12, 1995, was organized to bring to gether an interdisciplinary group drawn from the areas of science, engineering and mathematics for the following purposes: - to promote, encourage and influence more understanding and cooperation in the community of parameter identifiers from various disciplines, - to forge unity in diversity by bringing together a variety of disciplines that attempt to understand the reconstruction of inner model parameters, un known nonlinear constitutive relations, heterogeneous structures inside of geological objects, sources or sinks from observational data, - to discuss modern regularization tools for handling improperly posed pro blems and strategies of incorporating a priori knowledge from the applied problem into the model and its treatment. These proceedings contain some of the results of the workshop, representing a bal anced selection of contributions from the various groups of participants. The reviewed invited and contributed articles are grouped according to the broad headings of hydrology, non-linear diffusion and soil physics, geophysical methods, mathematical analysis of inverse and ill-posed problems and parallel algorithms for inverse problems. Some of the issues adressed by the articles in these proceedings include the rela tion between least squares and direct formulations of inverse problems for partial differential equations, nonlinear regularization, identification of nonlinear consti tutive relations, fast parallel algorithms for large scale inverse problems, reduction of model structures, geostatistical inversion techniques.
Author: Phoebus J. Dhrymes
Publisher: Springer Science & Business Media
ISBN: 1461245486
Category : Business & Economics
Languages : en
Pages : 390
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Book Description
For sometime now, I felt that the evolution of the literature of econo metrics had mandated a higher level of mathematical proficiency. This is particularly evident beyond the level of the general linear model (GLM) and the general linear structural econometric model (GLSEM). The problems one encounters in nonlinear econometrics are not easily amenable to treatment by the analytical methods one typically acquires, when one learns about probability and inference through the use of den sity functions. Even in standard traditional topics, one is often compelled to resort to heuristics; for example, it is difficult to prove central limit theorems for nonidentically distributed or martingale sequences, solely by the use of characteristic functions. Yet such proofs are essential, even in only moderately sophisticated classroom exposition. Unfortunately, relatively few students enter a graduate economics de partment ready to tackle probability theory in measure theoretic terms. The present volume has grown out of the need to lay the foundation for such discussions. The motivating forces were, chiefly, (a) the frustration one encounters in attempting to communicate certain concepts to stu dents wholly in analytic terms; and (b) the unwillingness of the typical student to sit through several courses in mathematics departments, in order to acquire the requisite background.
