Adaptive estimation of heteroskedastic error component model
AbstractThis paper checks the sensitivity of two adaptive heteroskedastic estimators suggested by Li and Stengos (1994) and Roy (2002) for an error component regression model to misspecification of the form of heteroskedasticity. In particular, we run Monte Carlo experiments using the heteroskedasticity setup by Li and Stengos (1994) to see how the misspecified Roy (2002) estimator performs. Next, we use the heteroskedasticity setup by Roy (2002) to see how the misspecified Li and Stengos (1994) estimator performs. We also check the sensitivity of these results to the choice of the smoothing parameters, the sample size, and the degree of heteroskedasticity. We find that the Li and Stengos (1994) estimator performs better under this type of misspecification than the corresponding estimator of Roy (2002). However, the former estimator is sensitive to the choice of the bandwidth.
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Bibliographic InfoPaper provided by ERMES, University Paris 2 in its series Working Papers ERMES with number 0402.
Date of creation: 2004
Date of revision:
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- Badi Baltagi & Georges Bresson & Alain Pirotte, 2005. "Adaptive Estimation Of Heteroskedastic Error Component Models," Econometric Reviews, Taylor & Francis Journals, vol. 24(1), pages 39-58.
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- Juhl, Ted & Sosa-Escudero, Walter, 2014. "Testing for heteroskedasticity in fixed effects models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 484-494.
- Bresson G. & Hsiao C. & Pirotte A., 2007.
"Assessing the Contribution of R&D to Total Factor Productivity – a Bayesian Approach to Account for Heterogeneity And Heteroscedasticity,"
Working Papers ERMES
0708, ERMES, University Paris 2.
- Georges Bresson & Cheng Hsiao & Alain Pirotte, 2011. "Assessing the contribution of R&D to total factor productivity—a Bayesian approach to account for heterogeneity and heteroskedasticity," AStA Advances in Statistical Analysis, Springer, vol. 95(4), pages 435-452, December.
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