Cointegrating Polynomial Regressions with Power Law Trends: Environmental Kuznets Curve or Omitted Time Effects?
Yicong Lin, Hanno Reuvers

TL;DR
This paper introduces a Generalized Cointegrating Polynomial Regression model that captures nonlinearities in environmental-economic data, revealing that global trends explain pollution reductions better than GDP alone.
Contribution
It develops a new GCPR model allowing for multiple sources of nonlinearity and proposes a novel inference method and specification test for environmental and economic data.
Findings
A single global trend explains nonlinearities in pollution and GDP data.
Environmental improvements are linked to factors other than GDP.
The proposed methods perform well in simulations and real data analysis.
Abstract
The environmental Kuznets curve predicts an inverted U-shaped relationship between environmental pollution and economic growth. Current analyses frequently employ models which restrict nonlinearities in the data to be explained by the economic growth variable only. We propose a Generalized Cointegrating Polynomial Regression (GCPR) to allow for an alternative source of nonlinearity. More specifically, the GCPR is a seemingly unrelated regression with (1) integer powers of deterministic and stochastic trends for the individual units, and (2) a common flexible global trend. We estimate this GCPR by nonlinear least squares and derive its asymptotic distribution. Endogeneity of the regressors will introduce nuisance parameters into the limiting distribution but a simulation-based approach nevertheless enables us to conduct valid inference. A multivariate subsampling KPSS test is proposed to…
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Taxonomy
TopicsEnergy, Environment, Economic Growth · Environmental Impact and Sustainability · Climate Change Policy and Economics
