Author: Dennis Kristensen
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
Book Description
We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed-form representation of the likelihood function. Using the simulated observations, we nonparametrically estimate the density - which is unknown in closed form - by kernel methods, and then construct a likelihood function that can be maximized. We prove for dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. NPSML is applicable to general classes of models and is easy to implement in practice.
Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood
Author: Dennis Kristensen
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
Book Description
We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed-form representation of the likelihood function. Using the simulated observations, we nonparametrically estimate the density - which is unknown in closed form - by kernel methods, and then construct a likelihood function that can be maximized. We prove for dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. NPSML is applicable to general classes of models and is easy to implement in practice.
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
Book Description
We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed-form representation of the likelihood function. Using the simulated observations, we nonparametrically estimate the density - which is unknown in closed form - by kernel methods, and then construct a likelihood function that can be maximized. We prove for dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. NPSML is applicable to general classes of models and is easy to implement in practice.
Exact Maximum Likelihood Estimation of Observation-driven Econometric Models
Author: Francis X. Diebold
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 38
Book Description
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 38
Book Description
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.
A Nonparametric Simulated Maximum Likelihood Estimation Method
Nonparametric Function Estimation, Modeling, and Simulation
Author: James R. Thompson
Publisher: SIAM
ISBN: 0898712610
Category : Mathematics
Languages : en
Pages : 317
Book Description
Topics emphasized in this book include nonparametric density estimation, multi-dimensional data analysis, cancer progression, chaos theory, and parallel based algorithms.
Publisher: SIAM
ISBN: 0898712610
Category : Mathematics
Languages : en
Pages : 317
Book Description
Topics emphasized in this book include nonparametric density estimation, multi-dimensional data analysis, cancer progression, chaos theory, and parallel based algorithms.
Maximum Likelihood Estimation of Dynamic Models with Unobserved Variables
Author: Damayanti Ghosh
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 498
Book Description
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 498
Book Description
Quasi-maximum Likelihood Estimation of Dynamic Models with Time Varying Covariances
QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF DYNAMIC MODELS WITH TIME VARYNG COVARIANCES
Estimation of Dynamic Models with Error Components
Author: Theodore Wilbur Anderson
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 52
Book Description
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 52
Book Description
Unconditional Maximum Likelihood Estimation of Dynamic Models for Spatial Panels
Simulated Maximum Likelihood Estimation of Discrete Models with Group Data
Author: Lung-Fei Lee
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 0
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 0
Book Description