Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/213848 
Erscheinungsjahr: 
2020
Quellenangabe: 
[Journal:] Economics: The Open-Access, Open-Assessment E-Journal [ISSN:] 1864-6042 [Volume:] 14 [Issue:] 2020-4 [Publisher:] Kiel Institute for the World Economy (IfW) [Place:] Kiel [Year:] 2020 [Pages:] 1-18
Verlag: 
Kiel Institute for the World Economy (IfW), Kiel
Zusammenfassung: 
This paper shows how to bootstrap hypothesis tests in the context of the Parks's (1967) Feasible Generalized Least Squares estimator. It then demonstrates that the bootstrap outperforms FGLS(Parks)'s top competitor. The FGLS(Parks) estimator has been a workhorse for the analysis of panel data and seemingly unrelated regression equation systems because it allows the incorporation of cross-sectional correlation together with heteroskedasticity and serial correlation. Unfortunately, the associated, asymptotic standard error estimates are biased downward, often severely. To address this problem, Beck and Katz (1995) developed an approach that uses the Prais-Winsten estimator together with 'panel corrected standard errors' (PCSE). While PCSE produces standard error estimates that are less biased than FGLS(Parks), it forces the user to sacrifice efficiency for accuracy in hypothesis testing. The PCSE approach has been, and continues to be, widely used. This paper develops an alternative: a nonparametric bootstrapping procedure to be used in conjunction with the FGLS(Parks) estimator. We demonstrate its effectiveness using an experimental approach that creates artificial panel datasets modelled after actual panel datasets. Our approach provides a superior alternative to existing estimation options by allowing researchers to retain the efficiency of the FGLS(Parks) estimator while producing more accurate hypothesis test results than the PCSE.
Schlagwörter: 
Parks model
FGLS
PCSE
SUR
panel data
cross-sectional correlation
bootstrap
Monte Carlo
simulation
JEL: 
C13
C15
C23
C33
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
347.65 kB





Publikationen in EconStor sind urheberrechtlich geschützt.