Dynamic Discrete Choice and Dynamic Treatment

This paper considers semiparametric identiÞcation of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic treatment model using the structural model as a benchmark. For the structural model we show the gains from using cross equation restrictions connecting choices to associated measurements and outcomes. In the dynamic discrete choice model, we identify both subjective and objective outcomes, distinguishing ex post and ex ante outcomes. We show how to identify agent information

[1]  V. J. Hotz,et al.  Conditional Choice Probabilities and the Estimation of Dynamic Models , 1993 .

[2]  John Rust.  Structural estimation of markov decision processes , 1986 .

[3]  J. Heckman.  Shadow prices, market wages, and labor supply , 1974 .

[4]  L L THURSTONE,et al.  The measurement of values. , 1960, Psychological review.

[5]  M. Keane,et al.  The Career Decisions of Young Men , 1997, Journal of Political Economy.

[6]  K. Wolpin.  An Estimable Dynamic Stochastic Model of Fertility and Child Mortality , 1984, Journal of Political Economy.

[7]  J. Heckman,et al.  Removing the Veil of Ignorance in Assessing the Distributional Impacts of Social Policies , 2002, SSRN Electronic Journal.

[8]  J. Heckman,et al.  Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College , 2003 .

[9]  V. Joseph Hotz,et al.  An Empirical Analysis of Life Cycle Fertility and Female Labor Supply. , 1988 .

[10]  James M. Robins,et al.  Causal Inference from Complex Longitudinal Data , 1997 .

[11]  H. James.  VARIETIES OF SELECTION BIAS , 1990 .

[12]  James J. Heckman,et al.  Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males , 1998, Journal of Political Economy.

[13]  J. Heckman,et al.  The Economics and Econometrics of Active Labor Market Programs , 1999 .

[14]  Christopher R. Taber.  Semiparametric identification and heterogeneity in discrete choice dynamic programming models , 2000 .

[15]  Rosa L. Matzkin.  Nonparametric Estimation of Nonadditive Random Functions , 2003 .

[16]  Jeffrey A. Smith,et al.  Evaluating the Welfare State , 1998 .

[17]  M. Lechner,et al.  Identification of the effects of dynamic treatments by sequential conditional independence assumptions , 2005 .

[18]  James M. Robins,et al.  Unified Methods for Censored Longitudinal Data and Causality , 2003 .

[19]  James J. Heckman,et al.  Four Parameters of Interest in the Evaluation of Social Programs , 2001 .

[20]  Ariel Pakes,et al.  Patent Renewal Data , 1989 .

[21]  Herman Rubin,et al.  Statistical Inference in Factor Analysis , 1956 .

[22]  J. Mincer.  Schooling, Experience, and Earnings , 1976 .

[23]  James J. Heckman,et al.  A Life Cycle Model of Female Labour Supply , 1980 .

[24]  Kenneth I. Wolpin,et al.  The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpol , 1994 .

[25]  Tatyana Ryutov,et al.  Dynamic Policy Evaluation for Containing Network Attacks (DEFCN) , 2005 .

[26]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[27]  J. Heckman.  Heterogeneity and State Dependence , 1981 .

[28]  James J Heckman,et al.  Understanding Instrumental Variables in Models with Essential Heterogeneity , 2006, The Review of Economics and Statistics.

[29]  J. Heckman,et al.  Does Unemployment Cause Future Unemployment? Definitions, Questions and Answers from a Continuous Time Model of Heterogeneity and State Dependence. , 1980 .

[30]  James J. Heckman,et al.  Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation , 2007 .

[31]  J J Heckman,et al.  Local instrumental variables and latent variable models for identifying and bounding treatment effects. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Rosa L. Matzkin.  Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models , 1992 .

[33]  Lance Lochner,et al.  Explaining Rising Wage Inequality: Explorations with a Dynamic General Equilibrium Model of Labor Earnings with Heterogeneous Agents , 1998 .

[34]  James J. Heckman,et al.  Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs , 2005 .

[35]  James J. Heckman,et al.  The identifiability of the competing risks model , 1989 .

[36]  J. Heckman,et al.  A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data , 1984 .

[37]  Unobserved Ability and the Return to Schooling , 2002 .