Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions

Fabian Knorre, Martin Wagner and Maximilian Grupe
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Fabian Knorre: Faculty of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227 Dortmund, Germany
Maximilian Grupe: Faculty of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227 Dortmund, Germany

Econometrics, 2021, vol. 9, issue 1, 1-35

Abstract: This paper develops residual-based monitoring procedures for cointegrating polynomial regressions (CPRs), i.e., regression models including deterministic variables and integrated processes, as well as integer powers, of integrated processes as regressors. The regressors are allowed to be endogenous, and the stationary errors are allowed to be serially correlated. We consider five variants of monitoring statistics and develop the results for three modified least squares estimators for the parameters of the CPRs. The simulations show that using the combination of self-normalization and a moving window leads to the best performance. We use the developed monitoring statistics to assess the structural stability of environmental Kuznets curves (EKCs) for both CO 2 and SO 2 emissions for twelve industrialized countries since the first oil price shock.

Keywords: cointegrating polynomial regression; environmental kuznets curve; monitoring; structural change (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions (2020)
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