Doctoral Seminar: The Asymptotic Validity of “Standard” Fully Modified OLS Estimation and Inference in Cointegrating Polynomial Regressions
Veranstaltungsort
N.2.01
Veranstalter
Institut für Statistik
Beschreibung
The paper considers estimation and inference in cointegrating polynomial regressions,
i. e., regressions that include deterministic variables, integrated processes and
their powers as explanatory variables. The stationary errors are allowed to be serially
correlated and the regressors to be endogenous. We show that estimating such
relationships using the Phillips and Hansen (1990) fully modified OLS approach developed
for linear cointegrating relationships by incorrectly considering all integrated
regressors and their powers as integrated regressors leads to the same limiting distribution
as the Wagner and Hong (2016) fully modified type estimator developed for
cointegrating polynomial regressions. The only restriction for this result to hold is
that all integrated variables themselves are included as regressors. Key ingredients
for our results are novel limit results for kernel weighted sums of properly scaled
nonstationary processes involving powers of integrated processes and a functional
central limit theorem involving polynomials of Brownian motions as both integrand
and integrator. Even though simulation results indicate performance advantages of
the Wagner and Hong (2016) estimator that are partly present even in large samples,
the results of the paper drastically enlarge the useability of the Phillips and Hansen
(1990) estimator implemented in many software packages.
Vortragende(r)
Univ.-Prof. Dr. Martin Wagner
Institut für Volkswirtschaftslehre
Universität Klagenfurt
Kontakt
Simone Gahleitner (simone [dot] gahleitner [at] aau [dot] at)
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