On Parameter Estimation in Cointegrating Polynomial Regressions: Single Equation, Seemingly Unrelated and Panel Estimators
Abstract
This thesis studies parameter estimation and inference in systems of
seemingly unrelated cointegrating polynomial regressions as
introduced in Hong and Wagner (2011). These are equation systems with deterministic regressors
and integrated processes and their integer powers as stochastic
regressors. The stochastic regressors are allowed to be endogenous and the
errors are allowed to be dynamically correlated, both over time and
across equations. In particular we consider a setting relevant for the
analysis of the environmental Kuznets curve (EKC) hypothesis, including only one
integrated regressor and its second and third power. In this setting we
compare three estimation approaches by means of a simulaton study.
These are the FM-OLS type estimator for (single equation) cointegrating
polynomial regressions of Wagner and Hong (2015), the SUR estimators of
Hong and Wagner (2011) and panel type estimators advocated by de Jong
and Wagner (2016). This thesis investigates under which conditions on
sample sizes (N and T), level of endogeneity and extent of serial and
cross-sectional correlation, one of the three above mentioned estimators,
as well as hypothesis tests, based upon it, performs relatively best.
Not forgetting the fact that the results are based on a
simulation study only, they provide some guidance with
respect to estimator choice in cointegrating polynomial regression
settings with panel-type data.