Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 536-548 |
| Journal | Computational Statistics & Data Analysis |
| Volume | 57 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2013 |
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One-step robust estimation of fixed-effects panel data models. / Aquaro, M.; Cizek, P.
In: Computational Statistics & Data Analysis, Vol. 57, No. 1, 2013, p. 536-548.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - One-step robust estimation of fixed-effects panel data models
AU - Aquaro, M.
AU - Cizek, P.
N1 - Appeared earlier as CentER Discussion Paper 2010-110
PY - 2013
Y1 - 2013
N2 - The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods available for fixed-effects panel data. A new estimation approach based on two different data transformations is therefore proposed. Considering several robust estimation methods applied to the transformed data, the robust and asymptotic properties of the proposed estimators are derived, including their breakdown points and asymptotic distributions. The finite-sample performance of the existing and proposed methods is compared by means of Monte Carlo simulations.
AB - The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods available for fixed-effects panel data. A new estimation approach based on two different data transformations is therefore proposed. Considering several robust estimation methods applied to the transformed data, the robust and asymptotic properties of the proposed estimators are derived, including their breakdown points and asymptotic distributions. The finite-sample performance of the existing and proposed methods is compared by means of Monte Carlo simulations.
U2 - 10.1016/j.csda.2012.07.003
DO - 10.1016/j.csda.2012.07.003
M3 - Article
VL - 57
SP - 536
EP - 548
JO - Computational Statistics & Data Analysis
JF - Computational Statistics & Data Analysis
SN - 0167-9473
IS - 1
ER -