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Author: Joseph Plasmans
Publisher: Springer Science & Business Media
ISBN: 9780387257600
Category : Business & Economics
Languages : en
Pages : 412
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Book Description
The basic characteristic of Modern Linear and Nonlinear Econometrics is that it presents a unified approach of modern linear and nonlinear econometrics in a concise and intuitive way. It covers four major parts of modern econometrics: linear and nonlinear estimation and testing, time series analysis, models with categorical and limited dependent variables, and, finally, a thorough analysis of linear and nonlinear panel data modeling. Distinctive features of this handbook are: -A unified approach of both linear and nonlinear econometrics, with an integration of the theory and the practice in modern econometrics. Emphasis on sound theoretical and empirical relevance and intuition. Focus on econometric and statistical methods for the analysis of linear and nonlinear processes in economics and finance, including computational methods and numerical tools. -Completely worked out empirical illustrations are provided throughout, the macroeconomic and microeconomic (household and firm level) data sets of which are available from the internet; these empirical illustrations are taken from finance (e.g. CAPM and derivatives), international economics (e.g. exchange rates), innovation economics (e.g. patenting), business cycle analysis, monetary economics, housing economics, labor and educational economics (e.g. demand for teachers according to gender) and many others. -Exercises are added to the chapters, with a focus on the interpretation of results; several of these exercises involve the use of actual data that are typical for current empirical work and that are made available on the internet. What is also distinguishable in Modern Linear and Nonlinear Econometrics is that every major topic has a number of examples, exercises or case studies. By this `learning by doing' method the intention is to prepare the reader to be able to design, develop and successfully finish his or her own research and/or solve real world problems.
Author: Joseph Plasmans
Publisher: Springer Science & Business Media
ISBN: 9780387257600
Category : Business & Economics
Languages : en
Pages : 412
Get Book
Book Description
The basic characteristic of Modern Linear and Nonlinear Econometrics is that it presents a unified approach of modern linear and nonlinear econometrics in a concise and intuitive way. It covers four major parts of modern econometrics: linear and nonlinear estimation and testing, time series analysis, models with categorical and limited dependent variables, and, finally, a thorough analysis of linear and nonlinear panel data modeling. Distinctive features of this handbook are: -A unified approach of both linear and nonlinear econometrics, with an integration of the theory and the practice in modern econometrics. Emphasis on sound theoretical and empirical relevance and intuition. Focus on econometric and statistical methods for the analysis of linear and nonlinear processes in economics and finance, including computational methods and numerical tools. -Completely worked out empirical illustrations are provided throughout, the macroeconomic and microeconomic (household and firm level) data sets of which are available from the internet; these empirical illustrations are taken from finance (e.g. CAPM and derivatives), international economics (e.g. exchange rates), innovation economics (e.g. patenting), business cycle analysis, monetary economics, housing economics, labor and educational economics (e.g. demand for teachers according to gender) and many others. -Exercises are added to the chapters, with a focus on the interpretation of results; several of these exercises involve the use of actual data that are typical for current empirical work and that are made available on the internet. What is also distinguishable in Modern Linear and Nonlinear Econometrics is that every major topic has a number of examples, exercises or case studies. By this `learning by doing' method the intention is to prepare the reader to be able to design, develop and successfully finish his or her own research and/or solve real world problems.
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: Mehmet Terzioğlu
Publisher: BoD – Books on Demand
ISBN: 1839624868
Category : Business & Economics
Languages : en
Pages : 339
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Book Description
The importance of experimental economics and econometric methods increases with each passing day as data quality and software performance develops. New econometric models are developed by diverging from earlier cliché econometric models with the emergence of specialized fields of study. This book, which is expected to be an extensive and useful reference by bringing together some of the latest developments in the field of econometrics, also contains quantitative examples and problem sets. We thank all the authors who contributed to this book with their studies that provide extensive and accessible explanations of the existing econometric methods.
Author: Phoebus J. Dhrymes
Publisher: Springer Science & Business Media
ISBN: 1461243025
Category : Business & Economics
Languages : en
Pages : 411
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Book Description
This book is intended for second year graduate students and professionals who have an interest in linear and nonlinear simultaneous equations mod els. It basically traces the evolution of econometrics beyond the general linear model (GLM), beginning with the general linear structural econo metric model (GLSEM) and ending with the generalized method of mo ments (GMM). Thus, it covers the identification problem (Chapter 3), maximum likelihood (ML) methods (Chapters 3 and 4), two and three stage least squares (2SLS, 3SLS) (Chapters 1 and 2), the general nonlinear model (GNLM) (Chapter 5), the general nonlinear simultaneous equations model (GNLSEM), the special ca'3e of GNLSEM with additive errors, non linear two and three stage least squares (NL2SLS, NL3SLS), the GMM for GNLSEIVl, and finally ends with a brief overview of causality and re lated issues, (Chapter 6). There is no discussion either of limited dependent variables, or of unit root related topics. It also contains a number of significant innovations. In a departure from the custom of the literature, identification and consistency for nonlinear models is handled through the Kullback information apparatus, as well as the theory of minimum contrast (MC) estimators. In fact, nearly all estimation problems handled in this volume can be approached through the theory of MC estimators. The power of this approach is demonstrated in Chapter 5, where the entire set of identification requirements for the GLSEM, in an ML context, is obtained almost effortlessly, through the apparatus of Kullback information.
Author: Christopher F. Baum
Publisher: Stata Press
ISBN: 1597180130
Category : Business & Economics
Languages : en
Pages : 362
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Book Description
Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, this introduction illustrates how to apply econometric theories used in modern empirical research using Stata. The author emphasizes the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how to apply the theories to real data sets. The book first builds familiarity with the basic skills needed to work with econometric data in Stata before delving into the core topics, which range from the multiple linear regression model to instrumental-variables estimation.
Author: William A. Barnett
Publisher: Cambridge University Press
ISBN: 9780521594240
Category : Business & Economics
Languages : en
Pages : 248
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Book Description
This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.
Author: A. Colin Cameron
Publisher: Cambridge University Press
ISBN: 1139444867
Category : Business & Economics
Languages : en
Pages :
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Book Description
This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.
Author: Giuseppe Orlando
Publisher: Springer Nature
ISBN: 3030709825
Category : Business & Economics
Languages : en
Pages : 361
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Book Description
This interdisciplinary book argues that the economy has an underlying non-linear structure and that business cycles are endogenous, which allows a greater explanatory power with respect to the traditional assumption that dynamics are stochastic and shocks are exogenous. The first part of this work is formal-methodological and provides the mathematical background needed for the remainder, while the second part presents the view that signal processing involves construction and deconstruction of information and that the efficacy of this process can be measured. The third part focuses on economics and provides the related background and literature on economic dynamics and the fourth part is devoted to new perspectives in understanding nonlinearities in economic dynamics: growth and cycles. By pursuing this approach, the book seeks to (1) determine whether, and if so where, common features exist, (2) discover some hidden features of economic dynamics, and (3) highlight specific indicators of structural changes in time series. Accordingly, it is a must read for everyone interested in a better understanding of economic dynamics, business cycles, econometrics and complex systems, as well as non-linear dynamics and chaos theory.
Author: Benedikt M. Pötscher
Publisher: Springer Science & Business Media
ISBN: 3662034867
Category : Business & Economics
Languages : en
Pages : 307
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Book Description
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: Alvin C. Rencher
Publisher: John Wiley & Sons
ISBN: 0470192607
Category : Mathematics
Languages : en
Pages : 690
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Book Description
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
