Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models
Author: The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models
Author: Wladimir RaymondOn Maximum Likelihood Estimation of Dynamic Panel Data Models
Author: Maurice J. G. BunPublisher:
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.
Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models
Author: Kazuhiko HayakawaDynamic Panel Data Modelling Using Maximum Likelihood
Author: Enrique Moral-BenitoEstimating Dynamic Panel Data Discrete Choice Models with Fixed Effects
Author: Jesús M. CarroMaximum Likelihood Estimation for Sample Surveys
Author: Raymond L. ChambersPublisher: CRC Press
ISBN: 1420011359
Category : Mathematics
Languages : en
Pages : 374
Book Description
Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to
Transformed Maximum Likelihood Estimation of Short Dynamic Panel Data Models with Interactive Effects
Author: Kazuhiko HayakawaRobust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models with Cross-Sectional Heteroskedasticity
Author: Kazuhiko HayakawaPublisher:
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.
Maximum Likelihood and GMM Estimation of Dynamic Panel Data Models with Fixed Effects
Author: Hugo KruinigerPublisher:
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.