Author: Francesco Bartolucci
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
Book Description
We show how the dynamic logit model for binary panel data may be approximated by a quadratic exponential model. Under the approximating model, simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects. The latter must be distinguished from the state dependence which is accounted for by including the lagged response variable among the regressors. By conditioning on the sufficient statistics, we derive a pseudo conditional likelihood estimator for the structural parameters of the dynamic logit model which is very simple to compute. Asymptotic properties of this estimator are derived. Simulation results show that the estimator is competitive in terms of efficiency with estimators very recently proposed in the econometric literature. We also show how the approach may be exploited to construct a Wald-type test for state dependence.
Pseudo Conditional Maximum Likelihood Estimation of the Dynamic Logit Model for Binary Panel Data
Author: Francesco Bartolucci
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
Book Description
We show how the dynamic logit model for binary panel data may be approximated by a quadratic exponential model. Under the approximating model, simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects. The latter must be distinguished from the state dependence which is accounted for by including the lagged response variable among the regressors. By conditioning on the sufficient statistics, we derive a pseudo conditional likelihood estimator for the structural parameters of the dynamic logit model which is very simple to compute. Asymptotic properties of this estimator are derived. Simulation results show that the estimator is competitive in terms of efficiency with estimators very recently proposed in the econometric literature. We also show how the approach may be exploited to construct a Wald-type test for state dependence.
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
Book Description
We show how the dynamic logit model for binary panel data may be approximated by a quadratic exponential model. Under the approximating model, simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects. The latter must be distinguished from the state dependence which is accounted for by including the lagged response variable among the regressors. By conditioning on the sufficient statistics, we derive a pseudo conditional likelihood estimator for the structural parameters of the dynamic logit model which is very simple to compute. Asymptotic properties of this estimator are derived. Simulation results show that the estimator is competitive in terms of efficiency with estimators very recently proposed in the econometric literature. We also show how the approach may be exploited to construct a Wald-type test for state dependence.
Conditional Maximum Likelihood Estimation of Dynamic Panel Data Models
Maximum Likelihood Estimation for Sample Surveys
Author: Raymond L. Chambers
Publisher: 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
Publisher: 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
On Maximum Likelihood Estimation of Dynamic Panel Data Models
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.
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.
Maximum Likelihood Estimation
Author: Scott R. Eliason
Publisher: SAGE
ISBN: 9780803941076
Category : Mathematics
Languages : en
Pages : 100
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.
Publisher: SAGE
ISBN: 9780803941076
Category : Mathematics
Languages : en
Pages : 100
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.
Pseudolikelihood Estimation, Markov Graphs and Social Networks
Author: Michael Miyoshi Ikeda
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 234
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 234
Book Description
Maximum Likelihood Estimation of Dynamic Linear Panel Data Models with Fixed Effect
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian Or Binary Data
Conditional Maximum Likelihood Estimation for a Dynamic Test Model
Author: Wilhelm F. Kempf
Publisher:
ISBN:
Category : Psychometrics
Languages : en
Pages : 146
Book Description
Publisher:
ISBN:
Category : Psychometrics
Languages : en
Pages : 146
Book Description
Maximum Likelihood Estimation in Binary Data Models Using Panel Data Under Alternative Distributional Assumptions
Author: Chris Orme
Publisher:
ISBN: 9780732603847
Category : Econometric models
Languages : en
Pages : 14
Book Description
Publisher:
ISBN: 9780732603847
Category : Econometric models
Languages : en
Pages : 14
Book Description