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

Michael P Keane () and Robert M. Sauer ()

Discussion Paper Series In Economics And Econometrics from University of Southampton, Economics Division, School of Social Sciences

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.
Keywords; Initial Conditions, Missing Data, Simulation Estimation
JEL Classification: C15, C23, C25

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Related works:
Working Paper: A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables (2004)
Working Paper: A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables (2009)
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Persistent link: http://EconPapers.repec.org/RePEc:stn:sotoec:0705

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