Is Adaptive Estimation Useful for Panel Models With Heteroskedasticity in the Individual Specific Error Component? Some Monte Carlo Evidence

Abstract

This paper first derives an adaptive estimator when heteroskedasticity is present in the individual specific error in an error component model and then compares the finite sample performance of the proposed estimator with various other estimators. While the Monte Carlo results show that the proposed estimator performs adequately in terms of relative efficiency, its performance on the basis of empirical size is quite similar to the other estimators considered.Heteroskedasticity, Kernel estimation, Error component model, JEL Classification , C14, C23,

Testing for heteroskedasticity and spatial correlation in a random effects panel data model

“…The experimental design for the Monte Carlo simulations is based on the format extensively used in earlier studies in the spatial regression model by Anselin and Rey (1991) and Anselin and Florax (1995), and in the heteroskedastic panel data model by Roy (2002).…”

“…The standard error components model has been extended to take into account spatial correlation by Anselin (1988), Baltagi, Song and Koh (2003), and Kapoor, Kelejian and Prucha (2007), to mention a few. This model has also been generalized to take into account heteroskedasticity by Mazodier and Trognon (1978), Baltagi and Griffin (1988), Li and Stengos (1994), Lejeune (1996), Holly and Gardiol (2000), Roy (2002) and Baltagi, Bresson and Pirotte (2006) to mention a few. For a review of these papers, see Baltagi (2005).…”

Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure

“…[18] also patterned the sensitivity of these results to the choice of the smoothing parameters, the sample size, and the degree of heteroscedasticity. They found that the [10] estimator performs better under this type of misspecification than the corresponding estimator of [19]. They, however, suggested that the former estimator is sensitive to the choice of the bandwidth.…”

“…Their paper was extended by [15], [16] and [17]. [18] tried to check the sensitivity of two adaptive heteroscedastic estimators suggested by [10] and [19] for an error component regression model to misspecification of the form of heteroscedasticity. In particular, they run Monte Carlo experiments using the heteroscedasticity set up by [10] to see how the misspecified [19] estimator performs.…”

“…[18] tried to check the sensitivity of two adaptive heteroscedastic estimators suggested by [10] and [19] for an error component regression model to misspecification of the form of heteroscedasticity. In particular, they run Monte Carlo experiments using the heteroscedasticity set up by [10] to see how the misspecified [19] estimator performs. [18] also patterned the sensitivity of these results to the choice of the smoothing parameters, the sample size, and the degree of heteroscedasticity.…”