Identification and Estimation of Dynamic Structural Models with Unobserved Choices

Identification and Estimation of Dynamic Structural Models with Unobserved Choices

Author: Yingyao Hu

Publisher:

Published: 2019

Total Pages:

ISBN-13:

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This paper develops identification and estimation methods for dynamic structural models when agents' actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are nonparametrically identified with a continuous state variable in a single-agent dynamic discrete choice model. Our identification results extend to (1) models with serially correlated unobserved heterogeneity and continuous choices, (2) cases in which only discrete state variables are available, and (3) dynamic discrete games. We apply our method to study moral hazard problems in US gubernatorial elections. We find that the probabilities of shirking increase as the governors approach the end of their terms.


Dynamic Discrete Choice Structural Models

Dynamic Discrete Choice Structural Models

Author: Victor Aguirregabiria

Publisher:

Published: 2016

Total Pages: 68

ISBN-13:

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This paper reviews methods for the estimation of dynamic discrete choice structural models and discusses related econometric issues. We consider single agent models, competitive equilibrium models and dynamic games. The methods are illustrated with descriptions of empirical studies which have applied these techniques to problems in different areas of economics. Programming codes for the estimation methods are available in a companion web page.


Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models

Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models

Author: Myrto Kalouptsidi

Publisher:

Published: 2018

Total Pages: 45

ISBN-13:

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In structural dynamic discrete choice models, the presence of serially correlated unobserved states and state variables that are measured with error may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address these issues, as long as measurement problems involve state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent’s entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computationally light. We provide constructive identification arguments to identify the model primitives, and establish the consistency and asymptotic normality of the estimator. A Monte Carlo study demonstrates the good finite-sample performance of the ECCP estimator in the context of a dynamic demand model for durable goods.


Discrete Choice Methods with Simulation

Discrete Choice Methods with Simulation

Author: Kenneth Train

Publisher: Cambridge University Press

Published: 2009-07-06

Total Pages: 399

ISBN-13: 0521766559

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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.


Econometric Models For Industrial Organization

Econometric Models For Industrial Organization

Author: Matthew Shum

Publisher: World Scientific

Published: 2016-12-14

Total Pages: 154

ISBN-13: 981310967X

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Economic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.


Structural Econometric Models

Structural Econometric Models

Author: Eugene Choo

Publisher: Emerald Group Publishing

Published: 2013-12-18

Total Pages: 447

ISBN-13: 1783500530

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This volume focuses on recent developments in the use of structural econometric models in empirical economics. The first part looks at recent developments in the estimation of dynamic discrete choice models. The second part looks at recent advances in the area empirical matching models.


Structural Econometric Modeling in Industrial Organization and Quantitative Marketing

Structural Econometric Modeling in Industrial Organization and Quantitative Marketing

Author: Ali Hortaçsu

Publisher: Princeton University Press

Published: 2023-10-24

Total Pages: 280

ISBN-13: 0691243468

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"Within economics a relatively new way of modeling has dominated important subfields: structural modeling. The goal of this book is to give an overview on how the various streams of literatures in empirical industrial organization and quantitative marketing use structural econometric modeling to estimate the model parameters, give the economic-model-based predictions, and conduct the policy counterfactual experiments. The traditional way of modelling, called "reduced-form" builds its models from simple relationships between variables of interests, which are mostly linear. Structural econometric models start by specifying the structure of the economic model, and the variables are calibrated from real-world data. This method enables better predictions and policy counterfactuals, and has other benefits. When considering a hypothetical policy change using the traditional modeling method ("reduced form"), researchers can often only estimate whether an effect would be positive or negative. With a structural econometric model using real-world data, a researcher can obtain the magnitude of the effects resulting from a hypothetical change. But the ability of quantifying the effects associated with a hypothetical policy change comes with its costs: the nonlinearity from explicitly specifying the possible relationships makes the structural econometric approach generally much more difficult to implement than its reduced-form counterpart. Therefore this book will provide a much-needed resource on how to use these methods effectively in the fields in which they been used the most, empirical industrial organization and quantitative marketing"--


Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models

Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models

Author: Victor Aguirregabiria

Publisher:

Published: 2018

Total Pages: 56

ISBN-13:

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We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking and the joint distribution of unobserved heterogeneity and observable state variables is nonparametric, i.e., fixed-effects model. We consider models with two endogenous state variables: the lagged decision variable, and the time duration in the last choice. This class of models includes as particular cases important economic applications such as models of market entry-exit, occupational choice, machine replacement, inventory and investment decisions, or dynamic demand of differentiated products. The identification of structural parameters requires a sufficient statistic that controls for unobserved heterogeneity not only in current utility but also in the continuation value of the forward-looking decision problem. We obtain the minimal sufficient statistic and prove identification of some structural parameters using a conditional likelihood approach. We apply this estimator to a machine replacement model.


Microeconometrics

Microeconometrics

Author: A. Colin Cameron

Publisher: Cambridge University Press

Published: 2005-05-09

Total Pages: 1058

ISBN-13: 1139444867

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This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.