Simulated Maximum Likelihood Estimation Of Dynamic Discrete Choice Statistical Models
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Simulated Maximum Likelihood Estimation Of Dynamic Discrete Choice Statistical Models
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Author : Lung-Fei Lee
language : en
Publisher:
Release Date : 1994
Simulated Maximum Likelihood Estimation Of Dynamic Discrete Choice Statistical Models written by Lung-Fei Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Monte Carlo method categories.
Estimation Of Dynamic Discrete Choice Models By Maximum Likelihood And The Simulated Method Of Moments
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Author : Phillipp Eisenhauer
language : en
Publisher:
Release Date : 2014
Estimation Of Dynamic Discrete Choice Models By Maximum Likelihood And The Simulated Method Of Moments written by Phillipp Eisenhauer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Decision making categories.
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
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Author : Kenneth Train
language : en
Publisher: Cambridge University Press
Release Date : 2009-07-06
Discrete Choice Methods With Simulation written by Kenneth Train and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-06 with Business & Economics categories.
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.
Asymptotic Bias In Maximum Simulated Likelihood Estimation Of Discrete Choice Models
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Author : Lung-Fei Lee
language : en
Publisher:
Release Date : 1992
Asymptotic Bias In Maximum Simulated Likelihood Estimation Of Discrete Choice Models written by Lung-Fei Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Estimation theory categories.
Handbook Of Choice Modelling
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Author : Stephane Hess
language : en
Publisher: Edward Elgar Publishing
Release Date : 2024-06-05
Handbook Of Choice Modelling written by Stephane Hess and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-05 with Business & Economics categories.
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.
Statistical Inference With Simulated Likelihood Functions
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Author : Lung-fei Lee
language : en
Publisher:
Release Date : 1995
Statistical Inference With Simulated Likelihood Functions written by Lung-fei Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Applied Discrete Choice Modelling
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Author : David A. Hensher
language : en
Publisher: Taylor & Francis
Release Date : 1981
Applied Discrete Choice Modelling written by David A. Hensher and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with Decision making categories.
Maximum Simulated Likelihood Methods And Applications
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Author : William Greene
language : en
Publisher: Emerald Group Publishing
Release Date : 2010-12-03
Maximum Simulated Likelihood Methods And Applications written by William Greene and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-03 with Business & Economics categories.
This collection of methodological developments and applications of simulation-based methods were presented at a workshop at Louisiana State University in November, 2009. Topics include: extensions of the GHK simulator; maximum-simulated likelihood; composite marginal likelihood; and modelling and forecasting volatility in a bayesian approach.
The Solution And Estimation Of Discrete Choice Dynamic Programming Models By Simulation And Interpolation
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Author : Michael P. Keane
language : en
Publisher:
Release Date : 1994
The Solution And Estimation Of Discrete Choice Dynamic Programming Models By Simulation And Interpolation written by Michael P. Keane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Dynamic programming categories.
Discrete Choice Analysis With R
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Author : Antonio Páez
language : en
Publisher: Springer Nature
Release Date : 2023-01-25
Discrete Choice Analysis With R written by Antonio Páez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-25 with Mathematics categories.
This book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization.