Asymptotic Properties of the Maximum Likelihood Estimator of Random Effects Models with Serial Correlation PDF Download
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Languages : en
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The Stockholm School of Economics (SSE) Library presents the full text of the February 2001 paper entitled "Asymptotic Properties of the Maximum Likelihood Estimator of Random Effects Models with Serial Correlation," written by Jimmy Skoglund and Sune Karlsson. The text is available in PDF format and the paper is number 432 in the SEE/EFI Working Papers in Economics and Finance series. This paper discusses the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR for the time-specific error component.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The Stockholm School of Economics (SSE) Library presents the full text of the February 2001 paper entitled "Asymptotic Properties of the Maximum Likelihood Estimator of Random Effects Models with Serial Correlation," written by Jimmy Skoglund and Sune Karlsson. The text is available in PDF format and the paper is number 432 in the SEE/EFI Working Papers in Economics and Finance series. This paper discusses the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR for the time-specific error component.
Author: Jimmy Skoglund
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Category :
Languages : en
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This paper considers the large sample behavior of the maximum likelihood estimator of random effects models. Consistent estimation and asymptotic normality as N and/or T grows large is established for a comprehensive specification which allows for serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. The consistency and asymptotic normality properties of all commonly used random effects models are obtained as special cases of the comprehensive model. When N or T >infty only a subset of the parameters are consistent and asymptotic normality is established for the consistent subsets.
Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 556
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The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood. (Modified author abstract).
Author: Robert Ernest Tarone
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
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Author: Jimmy Skoglund
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Category :
Languages : en
Pages :
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The general case where the time specific effect in a two way model follows an arbitrary ARMA process has not been considered previously. We offer a straightforward maximum likelihood estimator for this case. Allowing for general ARMA processes raises the issue of model specification and we propose tests of the null hypothesis of no serial correlation as well as tests for discriminating between different specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators and test-statistics.
Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 318
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Book Description
The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.
Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
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Author: Badi Baltagi
Publisher: John Wiley & Sons
ISBN: 0470518863
Category : Business & Economics
Languages : en
Pages : 239
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Book Description
Written by one of the world's leading researchers and writers in the field, Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. Featuring the most recent empirical examples from panel data literature, data sets are also provided as well as the programs to implement the estimation and testing procedures described in the book. These programs will be made available via an accompanying website which will also contain solutions to end of chapter exercises that will appear in the book. The text has been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models.
Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
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Author: Mary Kathleen Vickers
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ISBN:
Category : Asymptotes
Languages : en
Pages : 312
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Book Description
Four theorems are proven, which simplify the application to econometric models of Weiss's theorem on asymptotic properties of maximum likelihood estimators in nonstandard cases. The theorems require, roughly: the uniform convergence in any compact sets of the unknown parameters of the expection of the Hessian matrix of the log likelihood function; and the uniform convergence to 0 in the same sense of the variance of the same quantities. The fourth theorem allows one to conclude that the optimal properties hold on an image set of the parameters when the map satisfies certain smoothness conditions, and the first three theorems are satisfied for the original parameter set. These four theorems are applied to autoregressive models, nonlinear models, systems of equations, and probit and logit models to infer optimal asymptotic properties. (Author).
