Knorre, Fabian2025-06-182025-06-182025http://hdl.handle.net/2003/4375710.17877/DE290R-25531This cumulative dissertation develops and examines cointegration regression techniques and their application to nonlinear cointegrating relationships between integrated time series, with a particular focus on the Environmental Kuznets Curve (EKC). The EKC hypothesis postulates an inverted U-shaped relationship between economic development and pollution. Chapter 1 introduces residual-based monitoring procedures for cointegrating polynomial regressions (CPRs) to detect and date structural changes, i.e. whether a relationship may turn into a spurious relationship or whether the parameters of the relationship change. CPRs capture nonlinear dynamics by incorporating integer powers of integrated regressors. Chapter 2 evaluates estimators for nonparametric cointegrating regressions and specification tests for nonlinear cointegrating relationships, comparing their performance through a large scale simulation study. Chapter 3 extends estimators for seemingly unrelated cointegrating polynomial regressions to accommodate common integrated regressors across equations. It provides limiting distributions for these estimators, along with Wald-type hypothesis tests, RESET-type specification tests, and methods for group-wise pooled estimation, enhancing flexibility and precision in modeling complex systems.enNonlinear cointegrationCointegrating polynomial regressionEnvironmental Kuznets curve310PhDThesisKuznets-KurvePolynomiale RegressionKointegration