A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables
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
Year of publication: |
2004-08-11
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Authors: | Sauer, Robert M. ; Keane, Michael P. |
Institutions: | Econometric Society |
Subject: | Initial Conditions | Missing Data | Simulation Estimation |
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Type of publication: | Book / Working Paper |
Notes: | The text is part of a series Econometric Society North American Summer Meetings 2004 Number 136 |
Classification: | C15 - Statistical Simulation Methods; Monte Carlo Methods ; C23 - Models with Panel Data ; C25 - Discrete Regression and Qualitative Choice Models |
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Persistent link: https://www.econbiz.de/10005342235