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Author: T. Fomby
Publisher: Elsevier
ISBN: 9780762310753
Category : Business & Economics
Languages : en
Pages : 280
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Book Description
Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
Author: T. Fomby
Publisher: Elsevier
ISBN: 0762310758
Category : Business & Economics
Languages : en
Pages : 266
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Book Description
Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
Author: Gregory C. Chow
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
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Book Description
Author: Scott R. Eliason
Publisher: SAGE
ISBN: 9780803941076
Category : Mathematics
Languages : en
Pages : 100
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Book Description
This is a short introduction to Maximum Likelihood (ML) Estimation. It provides a general modeling framework that utilizes the tools of ML methods to outline a flexible modeling strategy that accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models linking endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, the author discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
Author: Demian Pouzo
Publisher:
ISBN:
Category :
Languages : en
Pages : 53
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Book Description
This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency and local asymptotic normality of the ML estimator under general conditions which allow for autoregressive dynamics in the observable process, time-inhomogeneous Markov regime sequences, and possible model misspecification. A Monte Carlo study examines the finite-sample properties of the ML estimator. An empirical application is also discussed.
Author: T.K. Dijkstra
Publisher: Springer
ISBN: 9783540138938
Category : Business & Economics
Languages : en
Pages : 134
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Book Description
This volume collects papers prepared for the workshop '~isspecification Ana lysis" held in Groningen. The Netherlands. December 15th and 16th. 1983. The papers. which cover a wide range of problems. contain a number of interesting and fruitful ideas. A bird's-eye view of the contents of the papers is as follows: White describes in a very general setting properties of classical statistical procedures when models are misspecified. Verbeek provides a lucid analysis of the statistical complexity of the model selection process. Dijkstra indicates how to construct good estimators. taking the possibility of misspecification explicitly into account. Taking the view that measured variables are never continuous. De Leeuw dis cusses in an illuminating way whether. and how. the discreteness of the data should and can be taken more seriously. Starting from the thesis that models are at best useful approximations of reality. Van ~ag and Koster derive properties of estimators determining best fitting hyperplanes. Within the context of the errors-in-variables problem. Bekker, Xapteyn and Wansbeek provide an elegant description of the variation in estimation re sults due to part of the modelspecification varying, while estimators are .adjusted so as to keep them consistent. Bierens develops consistent tests of the hypothesis of parameter constancy against extremely diffuse alternatives.
Author: Kanchana Nadarajah
Publisher:
ISBN:
Category :
Languages : en
Pages : 330
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Book Description
Misspecification of the short memory dynamics in a long memory model has serious repercussions for the asymptotic properties of any estimator of the long memory parameter. Under misspecification, the estimator converges in probability to a value called the pseudo-true value, which is different from the true value of the parameter. Intuitively, of all the family of spectral densities, the spectral density with the pseudo-true value is the closest spectral density to the true spectral density. Further consequences of misspecification are associated with the rate of convergence and the asymptotic distribution of the estimator of the parameter of the misspecified model. Both the rate of convergence and the asymptotic distribution of the parametric estimator of the misspecified model depends, in turn, on the difference between the true and pseudo-true values. We prove that under misspecification, frequency domain maximum likelihood estimation, Whittle estimation, time domain maximum likelihood estimation and conditional sum of squares estimation are asymptotically equivalent. However, our simulation study demonstrates that in small and medium sized samples, the performance of the parametric estimators of the misspecified model, in terms of bias, mean squared error and the form of the sampling distribution, differs across estimators. Overall, under misspecification, the conditional sum of squares estimator outperforms the other parametric estimators in small and medium sized samples. Further, the approximate frequency domain maximum likelihood estimator is the least efficient of all parametric estimators of the misspecified model, overall. In certain circumstances, where the difference between the true and the pseudo-true value of the long memory parameter is sufficiently large, a clear distinction between the frequency domain and time domain estimators can be observed in small samples. However, as the sample size increases, the behaviour of all of the parametric estimators of the misspecified model is consistent with the theoretical asymptotic results. Whilst misspecified parametric estimators of the long memory parameter are inconsistent for its true value, any semi-parametric estimator is consistent, although very biased in small samples. Thus, we compare the parametric estimators of the long memory parameter in the misspecified model with the semi-parametric Geweke and Porter-Hudak (GPH) estimator, to investigate whether any misspecified parametric estimator is less biased, or more efficient, than this particular semi-parametric estimator to measure the true value of the long memory parameter in finite samples. The CSS estimator under the misspecified model outperforms the GPH estimator in large finite samples in terms of bias and mean squared error, when the misspecified model is close to the true model. If the misspecified model is substantially different from the true model, then the GPH estimator is preferred over the four parametric estimators of the misspecified model in finite samples.
Author: Russell B. Millar
Publisher: John Wiley & Sons
ISBN: 1119977711
Category : Mathematics
Languages : en
Pages : 286
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Book Description
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
Author: Diane E. Duffy
Publisher:
ISBN:
Category :
Languages : en
Pages : 94
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Book Description
Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
Author: Jian Yang
Publisher: London : Department of Economics, University of Western Ontario
ISBN:
Category : Mathematics
Languages : en
Pages : 68
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Book Description
