Author: Philipp Eisenhauer
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
Book Description
Estimation Od Dynamic Discrete Choice Models by Maximum Likelihood and the Simulated Method of Moments
Estimation of Dynamic Discrete Choice Models by Maximum Likelihood and the Simulated Method of Moments
Author: Phillipp Eisenhauer
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages : 47
Book Description
We compare the performance of maximum likelihood (ML) and simulated method of moments (SMM) estimation for dynamic discrete choice models. We construct and estimate a simplified dynamic structural model of education that captures some basic features of educational choices in the United States in the 1980s and early 1990s. We use estimates from our model to simulate a synthetic dataset and assess the ability of ML and SMM to recover the model parameters on this sample. We investigate the performance of alternative tuning parameters for SMM.
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages : 47
Book Description
We compare the performance of maximum likelihood (ML) and simulated method of moments (SMM) estimation for dynamic discrete choice models. We construct and estimate a simplified dynamic structural model of education that captures some basic features of educational choices in the United States in the 1980s and early 1990s. We use estimates from our model to simulate a synthetic dataset and assess the ability of ML and SMM to recover the model parameters on this sample. We investigate the performance of alternative tuning parameters for SMM.
Discrete Choice Methods with Simulation
Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399
Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399
Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
Simulated Maximum Likelihood Estimation of Dynamic Discrete Choice Statistical Models
Author: Lung-Fei Lee
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 0
Book Description
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 0
Book Description
Simulated Maximum Likelihood Estimation of Discrete Models with Group Data
Author: Lung-Fei Lee
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 23
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 23
Book Description
Estimating Dynamic Panel Data Discrete Choice Models with Fixed Effects
Handbook of Choice Modelling
Author: Stephane Hess
Publisher: Edward Elgar Publishing
ISBN: 1800375638
Category : Business & Economics
Languages : en
Pages : 797
Book Description
This thoroughly revised second edition Handbook provides an authoritative and in-depth overview of choice modelling, covering essential topics range from data collection through model specification and estimation to analysis and use of results. It aptly emphasises the broad relevance of choice modelling when applied to a multitude of fields, including but not limited to transport, marketing, health and environmental economics.
Publisher: Edward Elgar Publishing
ISBN: 1800375638
Category : Business & Economics
Languages : en
Pages : 797
Book Description
This thoroughly revised second edition Handbook provides an authoritative and in-depth overview of choice modelling, covering essential topics range from data collection through model specification and estimation to analysis and use of results. It aptly emphasises the broad relevance of choice modelling when applied to a multitude of fields, including but not limited to transport, marketing, health and environmental economics.
Applied Discrete-Choice Modelling
Author: David A. Hensher
Publisher: Routledge
ISBN: 1351140752
Category : Business & Economics
Languages : en
Pages : 485
Book Description
Originally published in 1981. Discrete-choice modelling is an area of econometrics where significant advances have been made at the research level. This book presents an overview of these advances, explaining the theory underlying the model, and explores its various applications. It shows how operational choice models can be used, and how they are particularly useful for a better understanding of consumer demand theory. It discusses particular problems connected with the model and its use, and reports on the authors’ own empirical research. This is a comprehensive survey of research developments in discrete choice modelling and its applications.
Publisher: Routledge
ISBN: 1351140752
Category : Business & Economics
Languages : en
Pages : 485
Book Description
Originally published in 1981. Discrete-choice modelling is an area of econometrics where significant advances have been made at the research level. This book presents an overview of these advances, explaining the theory underlying the model, and explores its various applications. It shows how operational choice models can be used, and how they are particularly useful for a better understanding of consumer demand theory. It discusses particular problems connected with the model and its use, and reports on the authors’ own empirical research. This is a comprehensive survey of research developments in discrete choice modelling and its applications.
Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood
Author: Dennis Kristensen
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
Book Description
We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated observations, we estimate the unknown density of the model nonparametrically by kernel methods, and then obtain the SMLEs of the model parameters. Our method avoids the issue of non-identification arising from poor choice of auxiliary models in simulated methods of moments (SMM) or indirect inference. More importantly, our SMLEs achieve higher efficiency under weak regularity conditions. Finally, our method allows for potentially nonstationary processes, including time-inhomogeneous dynamics.
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
Book Description
We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated observations, we estimate the unknown density of the model nonparametrically by kernel methods, and then obtain the SMLEs of the model parameters. Our method avoids the issue of non-identification arising from poor choice of auxiliary models in simulated methods of moments (SMM) or indirect inference. More importantly, our SMLEs achieve higher efficiency under weak regularity conditions. Finally, our method allows for potentially nonstationary processes, including time-inhomogeneous dynamics.
Asymptotic Bias in Maximum Simulated Likelihood Estimation of Discrete Choice Models
Author: Lung-Fei Lee
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 26
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 26
Book Description