Author: Lung-Fei LeePublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 26
Book Description
Asymptotic Bias in Maximum Simulated Likelihood Estimation of Discrete Choice Models
Author: Lung-Fei LeePublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 26
Book Description
Estimating Dynamic Panel Data Discrete Choice Models with Fixed Effects
Author: Jesús M. CarroStatistical Inference with Simulated Likelihood Functions
Author: Lung-fei LeeEssays in Econometrics
Author: Xueyuan LiuPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
The dissertation consists of three chapters on different econometric topics. The first chapter studies jackknife bias reduction for simulated maximum likelihood estimator of discrete choice models. We propose to reduce asymptotic biases of simulated maximum likelihood estimators (SMLE) by using a jackknife method similar to Dhaene and Jochmans (2015), which was originally proposed to reduce bias in nonlinear panel models. Lee (1995) investigates the asymptotic bias of the SMLE, and derives the analytical formula of higher order bias due to simulation. However, implementation of Lee (1995)'s method requires analytical characterization of the higher order bias, which may not be convenient for practice. Because the jackknife method does not require an explicit characterization of the bias, it may be a practically attractive alternative to Lee (1995)'s estimator. The second chapter studies estimation of average treatment effects for massively unbalanced binary outcomes. The maximum likelihood estimator (MLE) of the average treatment effects (ATE) in the logit model for binary outcomes may have a significant second order bias if the event has a low probability. The analysis of rare events is relevant for economics because some of the big data sets are collected from online sources where the number of events (such as " clicks" and " purchases") is much smaller than the number of nonevents. The literature about rare events (King and Zeng, 2001; Chen and Giles, 2012; Rilstone, 1996; Wang, 2020) does not shed light on the finite sample behavior of logit MLE and ATE if events are rare. In this chapter, we also derive the second order bias of the logit ATE estimator and propose bias-corrected estimators of the ATE. We also propose a variation on the logit model with parameters that are elasticities. Finally, we propose a computational trick that avoids numerical instability in the case of estimation for rare events. The third chapter studies a Vuong test (Vuong, 1989) for panel data models with fixed effects. This chapter generalizes the Vuong test to nonlinear panel models where the dimension of incidental parameters grows with the sample size. The incidental parameters (Neyman and Scott, 1948) that affect the unbiasedness of the parameters of interest are also important for panel data models as they capture unobserved heterogeneity. The discrepancy in incidental parameters plays an important role in model selection; for example, as noted by MacKinnon et al. (2020), there is a vast literature on the cluster-robust inference that assumes the structure of the clusters is correctly specified, which is often violated. In the presence of incidental parameters, we cannot easily apply the classical Vuong test to select a panel data model. This chapter proposes a new model selection test for panel data models by extending the classical Vuong test, which selects from two parametric likelihood models based on their Kullback-Leibler information criterion (KLIC). This chapter proposes three different test statistics for researchers who need to deal with all possible relationships between candidate models: overlapping models, nested models, and strictly nonnested models. These three model relationships are classified according to the structure of low-dimensional parameter of interest and high-dimensional incidental parameters. We allow for disagreements about incidental parameters and obtain specification tests based on a modified likelihood function.
Discrete Choice Methods with Simulation
Author: Kenneth TrainPublisher: 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.
Nonlinear Statistical Modeling
Author: Takeshi AmemiyaPublisher: Cambridge University Press
ISBN: 9780521662468
Category : Business & Economics
Languages : en
Pages : 472
Book Description
This collection investigates parametric, semiparametric, nonparametric, and nonlinear estimation techniques in statistical modeling.
Handbook of Econometrics
Author: J.J. HeckmanPublisher: Elsevier
ISBN: 0080524796
Category : Business & Economics
Languages : en
Pages : 737
Book Description
The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses. For more information on the Handbooks in Economics series, please see our home page on http://www.elsevier.nl/locate/hes
Discrete Choice Methods with Simulation
Author: Kenneth E. TrainPublisher: Cambridge University Press
ISBN: 1139480375
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. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This 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.
Maximum Simulated Likelihood Estimation of Consumer Demand Systems with Binding Non-negativity Constraints
Author: Chihwa KaoThe Econometrics of Panel Data
Author: Lászlo MátyásPublisher: Springer Science & Business Media
ISBN: 3540758925
Category : Business & Economics
Languages : en
Pages : 966
Book Description
This restructured, updated Third Edition provides a general overview of the econometrics of panel data, from both theoretical and applied viewpoints. Readers discover how econometric tools are used to study organizational and household behaviors as well as other macroeconomic phenomena such as economic growth. The book contains sixteen entirely new chapters; all other chapters have been revised to account for recent developments. With contributions from well known specialists in the field, this handbook is a standard reference for all those involved in the use of panel data in econometrics.