How useful is yet another data-driven bandwidth in long-run variance estimation?: A simulation study on cointegrating regressions

How useful is yet another data-driven bandwidth in long-run variance estimation?: A simulation study on cointegrating regressions

This paper investigates how bandwidth choice rules in long-run variance estimation affect finite-sample performance of efficient estimators for cointegrating regression models. Monte Carlo results indicate that Hirukawa's (2010) bandwidth choice rule contributes bias reduction in the estimators...

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Veröffentlicht in:Economics letters 2011-05, Vol.111 (2), p.170-172
1. Verfasser: Hirukawa, Masayuki
Format: Artikel
Sprache:eng
Schlagworte:
Bandwidth
Bandwidth Cointegration Kernel Long-run variance Simulation
Bandwidths
Cointegration
Estimating techniques
Estimation
Estimation bias
Kernel
Long-run variance
Monte Carlo simulation
Regression analysis
Simulation
Studies
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Beschreibung
Zusammenfassung:This paper investigates how bandwidth choice rules in long-run variance estimation affect finite-sample performance of efficient estimators for cointegrating regression models. Monte Carlo results indicate that Hirukawa's (2010) bandwidth choice rule contributes bias reduction in the estimators. ► Effects of different bandwidth formulae in HAC estimation are investigated. ► Simulations on efficient estimation in cointegrating regressions are conducted. ► Hirukawa's (2010) formula contributes bias reduction in the efficient estimators.
ISSN:0165-1765
1873-7374
DOI:10.1016/j.econlet.2011.02.006