A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables. / Keane, Michael P.; Sauer, Robert M.

In: International Economic Review, Vol. 51, No. 4, 11.2010, p. 925-958.

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A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables. / Keane, Michael P.; Sauer, Robert M.

In: International Economic Review, Vol. 51, No. 4, 11.2010, p. 925-958.

Research output: Contribution to journalArticlepeer-review

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@article{38e91f7dafe84080851aff11ad0da53c,
title = "A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables",
abstract = "This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband{\textquoteright}s income on female labor supply decisions.",
keywords = "Initial Conditions, Missing Data, Classification Error, Simulated Maximum Likelihood, Female Labor Supply",
author = "Keane, {Michael P.} and Sauer, {Robert M.}",
year = "2010",
month = nov,
doi = "10.1111/j.1468-2354.2010.00606.x",
language = "English",
volume = "51",
pages = "925--958",
journal = "International Economic Review",
issn = "0020-6598",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables

AU - Keane, Michael P.

AU - Sauer, Robert M.

PY - 2010/11

Y1 - 2010/11

N2 - This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband’s income on female labor supply decisions.

AB - This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband’s income on female labor supply decisions.

KW - Initial Conditions

KW - Missing Data

KW - Classification Error

KW - Simulated Maximum Likelihood

KW - Female Labor Supply

U2 - 10.1111/j.1468-2354.2010.00606.x

DO - 10.1111/j.1468-2354.2010.00606.x

M3 - Article

VL - 51

SP - 925

EP - 958

JO - International Economic Review

JF - International Economic Review

SN - 0020-6598

IS - 4

ER -