Please use this identifier to cite or link to this item:
Stypka, Oliver
Wagner, Martin
Grabarczyk, Peter
Kawka, Rafael
Year of Publication: 
Series/Report no.: 
IHS Economics Series 333
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 are allowed to be endogenous. The main result shows 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 theWagner and Hong (2016) fully modified type estimator developed for cointegrating polynomial regressions. A key ingredient for the main result are novel limit results for kernel weighted sums of properly scaled nonstationary processes involving scaled powers of integrated processes. Even though the 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 as implemented in many software packages.
Cointegrating Polynomial Regression
Cointegration Test
EnvironmentalKuznets Curve
Fully Modified OLS Estimation
Integrated Process
Document Type: 
Working Paper
Social Media Mentions:

Files in This Item:
572.63 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.