Specification testing and quasi-maximum likelihood estimation

by Jeffrey M. Wooldridge

Publisher: Dept. of Economics, Massachusetts Institute of Technology in Cambridge, MA

Written in English
Cover of: Specification testing and quasi-maximum likelihood estimation | Jeffrey M. Wooldridge
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Edition Notes

Bibliography: p. 45-46.

Other titlesQuasi-maximum likelihood estimation, Specification testing and.
Statementby Jeffrey M. Wooldridge
SeriesWorking paper -- no. 479, Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 479.
ContributionsMassachusetts Institute of Technology. Dept. of Economics
The Physical Object
Pagination46 p. ;
Number of Pages46
ID Numbers
Open LibraryOL24636856M
OCLC/WorldCa18946846

Cambridge University Press, p. Themes in Modern Econometrics. ISBN This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are.   This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation.5/5(10). Econometric modelling with time series [electronic resource]: specification, estimation and testing / Vance Martin, University of Melbourne, Australia, Stan Hurn, Queensland University of Technology, Australia, David Harris, Monash University, Australia Cambridge University Press Cambridge This book does not provide detailed coverage of simulation-based estimation techniques, resampling methods for estimating the distributions of estimators and test statistics, or nonparametric methods. The author’s focused approach leads to .

"This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. Estimation results. We invoke (quasi) maximum likelihood methods to estimate the linear ACD model with exponential, Weibull, Burr, and generalized gamma precisely, as advanced in Section 5, we first compute the QML estimates using the exponential yields (35) ψ i = + z i-1 + ψ i-1, () () () Cited by: Econometrics for Financial and Macroeconomic Time Series Overview: The specification, estimation, This book offers a systematic framework for the specification, testing, and estimation of time series models. It strikes an excellent balance between formal theory, intuition, and actual "Quasi-Maximum Likelihood Estimation and Inference in File Size: 16KB. Time Series Econometrics Overview: The specification, estimation, diagnostic testing, and practical usage of dynamic "Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances," Econometric Reviews, 11,

  Specification testing and quasi-maximum likelihood estimation by Jeffrey M. Wooldridge 1 edition - first published in Accessible book, Econometrics, Protected DAISY, Asymptotic theory, Asimptotik teori, Asymptotische Methode. Econometric Modelling with Time Series This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maxi-mum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation File Size: KB. “ Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks,” Journal of the American Statistical Association , Su, L., Y. Zhang, and J. Wei, , “ A Practical Test for Strict Exogeneity in Linear Panel Data Models with Fixed Effects,” Economics Letters , Hypothesis Testing Specification Testing Partial (or Pooled) Likelihood Methods for Panel Data Panel Data Models with Unobserved E¤ects Two-Step Estimators Involving Maximum Likelihood Quasi-Maximum Likelihood Estimation Problems 14 Generalized Method of Moments and Minimum Distance.

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"Specification Testing and Quasi-Maximum Likelihood Estimation," Working papersMassachusetts Institute of Specification testing and quasi-maximum likelihood estimation book (MIT), Department of Economics.

More. Jeffrey M. Wooldridge, "Specification Testing and Quasi-Maximum Likelihood Estimation," Working papersMassachusetts Institute of Technology (MIT), Department of Economics. Handle: RePEc:mit:worpap Get this from a library.

Econometric modelling with time series: specification, estimation and testing. [Vance Martin; Stan Hurn; David Harris] -- "Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data.

The principle of maximum likelihood plays a central role in the exposition. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models, and gives the conditions under which parameters of interest can be consistently estimated despite misspecification, and the consequences of Cited by: This book provides a general framework for specifying, estimating, and testing time series econometric models.

Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation.

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This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for.

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation.5/5(10).

This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference.

Professor White first explores the underlying motivation for Price: $ In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable.

The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood. It is various with the online book Econometric Modelling With Time Series: Specification, Estimation And Testing (Themes In Modern Econometrics), By Vance Martin, Stan Hurn, where you can get a book then the seller will certainly send out the published book for you.

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method Price: $   This book provides a general framework for specifying, estimating, and testing time series econometric models.

Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by. PDF – ISBN: – Econometric Modelling with Time Series: Specification, Estimation and Testing by Vance Martin, Stan Hurn and David Harris # (Themes in Modern Econometrics) English | |, | pages | PDF | 7,5 MB This book provides.

"Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework.

Examples include ordinary least squares, generalized least squares and full 4/5(1). Estimation, Inference, and Specification Analysis by Halbert White of first derivatives; that these two expressions coincide is the "information matrix equality".

In such a setting, testing (smooth, finite-dimensional) restrictions on the parameters can be done in any of a number of straightforward ways — likelihood ratios, Lagrange. This book provides a general framework for specifying, estimating, and testing time series econometric models.

Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by Pages: on the relationship between the statistics and econometrics literatures on testing in quasi-likelihood frameworks, this comparison reveals some important lim-itations of GLM as a general framework for devising specification tests.

INTRODUCTION In the field of generalized linear models/quasi-maximum likelihood estima. Specification, Estimation and Testing. Author: Vance Martin,Stan Hurn,David Harris; Publisher: Cambridge University Press ISBN: Category: Business & Economics Page: View: DOWNLOAD NOW» "Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data.

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation.

An important. This volume is the result of an "Advances in Econometrics" conference held in November of at Louisiana State University in recognition of Halbert White's pioneering work published in Econometrica in and on robust variance-covariance estimation and quasi-maximum likelihood estimation.

Econometric modelling with time series: Speci Þ cation, Estimation, and Testing is a graduate textbook covering a broad range of topics in time series econometrics. The book is unique and valuable in three aspects. First, the book tries to bridge the gap between the purely theoretical view of time series analy.

Abstract. This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by : Vance Martin, Stan Hurn and David Harris.

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation.3/5(1).

In this case maximum likelihood estimation based on () is quasi-maximum likelihood estimation, which, as pointed out in Campbell (), is inconsistent for regime-switching models in. This paper investigates asymptotic properties of the maximum likelihood estimator and the quasi‐maximum likelihood estimator for the spatial autoregressive model.

The rates of cCited by: 1 The Maximum Likelihood Principle Introduction Maximum likelihood estimation is a general method for estimating the para-meters of econometric models from observed data.

The principle of maximum likelihood plays a central role in the exposition of this book, since a num-ber of estimators used in econometrics can be derived within this. estimation, inference, and specification testing for possibly misspecified quantile regression; quasi–maximum likelihood estimation with bounded symmetric errors; consistent quasi-maximum likelihood estimation with limited information; an examination of the sign and volatility switching arch models under alternative distributional assumptions.

"Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in. The alternative approach could be full information maximum likelihood (FIML) estimation or approximated multivariate likelihood function with the sequence of Author: Steven T.

Yen.This book provides a general framework for specifying, estimating and testing time series econometric models. Contents. Machine generated contents note: Part I.

Maximum Likelihood: 1. The maximum likelihood principle; 2. Properties of maximum likelihood estimators; 3. Numerical estimation methods; 4. Hypothesis testing; Part II. Regression.The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on.