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The environmental Kuznets curve predicts an inverted U-shaped relationshipbetween environmental pollution and economic growth. Current analysesfrequently employ models which restrict nonlinearities in the data to beexplained by the economic growth variable only. We propose a GeneralizedCointegrating Polynomial Regression (GCPR) to allow for an alternative sourceof nonlinearity. More specifically, the GCPR is a seemingly unrelatedregression with (1) integer powers of deterministic and stochastic trends forthe individual units, and (2) a common flexible global trend. We estimate thisGCPR by nonlinear least squares and derive its asymptotic distribution.Endogeneity of the regressors will introduce nuisance parameters into thelimiting distribution but a simulation-based approach nevertheless enables usto conduct valid inference. A multivariate subsampling KPSS test is proposed toverify the correct specification of the cointegrating relation. Our simulationstudy shows good performance of the simulated inference approach andsubsampling KPSS test. We illustrate the GCPR approach using data for Austria,Belgium, Finland, the Netherlands, Switzerland, and the UK. A single globaltrend accurately captures all nonlinearities leading to a linear cointegratingrelation between GDP and CO2 for all countries. This suggests that theenvironmental improvement of the last years is due to economic factorsdifferent from GDP.

Cointegrating Polynomial Regressions with Power Law Trends: Environmental Kuznets Curve or Omitted Time Effects?