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A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables

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  • Robert M. Sauer
  • Michael P. Keane

Abstract

This paper develops a new simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can deal with the commonly encountered and widely discussed ``initial conditions problem,'' as well as the more general problem of missing state variables at any point during the sample period. Repeated sampling experiments on a dynamic panel data probit model with serially correlated errors indicate that the estimator has good small sample properties and is computationally practical for use with panels of the size that are likely to be encountered in practice

Suggested Citation

  • Robert M. Sauer & Michael P. Keane, 2004. "A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables," Econometric Society 2004 North American Summer Meetings 136, Econometric Society.
    • Handle: RePEc:ecm:nasm04:136
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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