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Author: Maurice J. G. Bun Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We analyse the finite sample properties of maximum likelihood estimators for dynamic panel data models. In particular, we consider transformed maximum likelihood (TML) and random effects maximum likelihood (RML) estimation. We show that TML and RML estimators are solutions to a cubic first-order condition in the autoregressive parameter. Furthermore, in finite samples both likelihood estimators might lead to a negative estimate of the variance of the individual-specific effects. We consider different approaches taking into account the non-negativity restriction for the variance. We show that these approaches may lead to a solution different from the unique global unconstrained maximum. In an extensive Monte Carlo study we find that this issue is non-negligible for small values of T and that different approaches might lead to different finite sample properties. Furthermore, we find that the Likelihood Ratio statistic provides size control in small samples, albeit with low power due to the flatness of the log-likelihood function. We illustrate these issues modelling US state level unemployment dynamics.
Author: Maurice J. G. Bun Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We analyse the finite sample properties of maximum likelihood estimators for dynamic panel data models. In particular, we consider transformed maximum likelihood (TML) and random effects maximum likelihood (RML) estimation. We show that TML and RML estimators are solutions to a cubic first-order condition in the autoregressive parameter. Furthermore, in finite samples both likelihood estimators might lead to a negative estimate of the variance of the individual-specific effects. We consider different approaches taking into account the non-negativity restriction for the variance. We show that these approaches may lead to a solution different from the unique global unconstrained maximum. In an extensive Monte Carlo study we find that this issue is non-negligible for small values of T and that different approaches might lead to different finite sample properties. Furthermore, we find that the Likelihood Ratio statistic provides size control in small samples, albeit with low power due to the flatness of the log-likelihood function. We illustrate these issues modelling US state level unemployment dynamics.
Author: Hugo Kruiniger Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This paper considers inference procedures for two types of dynamic linear panel data models with fixed effects (FE). First, it shows that the closures of stationary ARMAFE models can be consistently estimated by Conditional Maximum Likelihood Estimators and it derives their asymptotic distributions. Then it presents an asymptotically equivalent Minimum Distance Estimator which permits an analytic comparison between the CMLE for the ARFE (1) model and the GMM estimators that have been considered in the literature. The CMLE is shown to be asymptotically less efficient than the most efficient GMM estimator when N approaches the limit infinity but T is fixed. Under normality some of the moment conditions become asymptotically redundant and the CMLE attains the Cramer-Rao lowerbound when T approaches the limit infinity as well. The paper also presents likelihood based unit root tests. Finally, the properties of CML, GMM, and Modified ML estimators for dynamic panel data models that condition on the initial observations are studied and compared. It is shown that for finite T the MMLE is less efficient than the most efficient GMM estimator.
Author: William Gould Publisher: Stata Press ISBN: 9781597180788 Category : Mathematics Languages : en Pages : 352
Book Description
Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.
Author: Kazuhiko Hayakawa Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran and Tahmiscioglu (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem that arises, and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model. It is shown that the transformed maximum likelihood estimator continues to be consistent even in the presence of cross-sectional heteroskedasticity. We also obtain standard errors that are robust to cross-sectional heteroskedasticity of unknown form. By means of Monte Carlo simulation, we investigate the finite sample behavior of the transformed maximum likelihood estimator and compare it with various GMM estimators proposed in the literature. Simulation results reveal that, in terms of median absolute errors and accuracy of inference, the transformed likelihood estimator outperforms the GMM estimators in almost all cases.
Author: Maurice J. G. Bun
Author: Maurice J. G. Bun
Author: Hugo Kruiniger
Author: Cheng Hsiao
Author: Hugo Kruiniger
Author: Kazuhiko Hayakawa
Author: Kazuhiko Hayakawa
Author: Hugo Kruiniger
Author: William Gould
Author: Kazuhiko Hayakawa
Author: Gareth Thomas