Stationary Regressors in Cointegrating Polynomial Regression
Abstract
This thesis investigates the finite sample performance of the fully modified OLS estimator
for cointegrating polynomial regressions (CPR),developed by Wagner and Hong (2016), including
stationary regressors to the model. To be precise, this thesis considers regressions including
deterministic variables, integrated processes, powers of integrated processes and stationary
variables as explanatory variables and stationary errors. The errors are allowed to be
serially correlated and the regressors are allowed to be endogenous except for the stationary
regressors where both cases, i.e. predetermined or endogenous stationary regressors, are
examined in this thesis. The basis for the finite sample performance investigation is a
simulation study which shows that the assumption of allowing endogeneity of the stationary
regressors can not be made as in this case the FM-OLS estimator seem to be not consistent
anymore and statistical inference is no longer feasible for every level of serial correlation
and endogeneity.