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A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables

A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables
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.
initial conditions, missing data, discrete choice, simulation estimation
0966-4246
705
University of Southampton
Sauer, Robert
11c87254-ac8e-4f4e-aad9-b0f3ab1aeabb
Keane, Michael P.
2ddb0fc0-2616-462a-b400-bdb827df10b6
Sauer, Robert
11c87254-ac8e-4f4e-aad9-b0f3ab1aeabb
Keane, Michael P.
2ddb0fc0-2616-462a-b400-bdb827df10b6

Sauer, Robert and Keane, Michael P. (2007) A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables (Discussion Papers in Economics and Econometrics, 705) Southampton, UK. University of Southampton 62pp.

Record type: Monograph (Discussion Paper)

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.

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More information

Published date: 1 August 2007
Keywords: initial conditions, missing data, discrete choice, simulation estimation

Identifiers

Local EPrints ID: 35074
URI: http://eprints.soton.ac.uk/id/eprint/35074
ISSN: 0966-4246
PURE UUID: 6be36d74-0ccb-447e-a68b-027004b4256d

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Date deposited: 19 May 2006
Last modified: 15 Mar 2024 07:50

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Contributors

Author: Robert Sauer
Author: Michael P. Keane

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