Estimating standard errors for the Parks model: Can jackknifing help?

Artikel

Non-spherical errors, namely heteroscedasticity, serial correlation and cross-sectional correlation are commonly present within panel data sets. These can cause significant problems for econometric analyses. The FGLS(Parks) estimator has been demonstrated to produce considerable efficiency gains in these settings. However, it suffers from underestimation of coefficient standard errors, oftentimes severe. Potentially, jackknifing the FGLS(Parks) estimator could allow one to maintain the efficiency advantages of FGLS(Parks) while producing more reliable estimates of coefficient standard errors. Accordingly, this study investigates the performance of the jackknife estimator of FGLS(Parks) using Monte Carlo experimentation. We find that jackknifing can - in narrowly defined situations - substantially improve the estimation of coefficient standard errors. However, its overall performance is not sufficient to make it a viable alternative to other panel data estimators.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Economics: The Open-Access, Open-Assessment E-Journal ; ISSN: 1864-6042 ; Volume: 5 ; Year: 2011 ; Issue: 2011-1 ; Pages: 1-14 ; Kiel: Kiel Institute for the World Economy (IfW)

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models ; Statistical Simulation Methods: General
• Subject: Panel data estimation ; Parks model ; cross-sectional correlation ; jackknife ; Monte Carlo ; Panel ; Schätztheorie ; Statistischer Fehler ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Reed, W. Robert ; Webb, Rachel S.
• Event: Veröffentlichung
• (who): Kiel Institute for the World Economy (IfW)
• (where): Kiel
• (when): 2011

Further information

• DOI: doi:10.5018/economics-ejournal.ja.2011-1
• Handle: http://hdl.handle.net/10419/43717
• Last update: 10.03.2025, 11:41 AM CET