arXiv 4 Sep 2020 · Econometrics · publishedJournal of Time Series Analysis (2024) · 2 citations (OpenAlex)
arXiv:2009.02262 · PDF · DOI · OpenAlex · Extracted main text
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 verify the correct specification of the cointegrating relation. Our simulation study shows good performance of the simulated inference approach and subsampling KPSS test. We illustrate the GCPR approach using data for Austria, Belgium, Finland, the Netherlands, Switzerland, and the UK. A single global trend accurately captures all nonlinearities leading to a linear cointegrating relation between GDP and CO2 for all countries. This suggests that the environmental improvement of the last years is due to economic factors different from GDP.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wang, Q., D. Wu, and K. Zhu (2018) Model checks for nonlinear cointegrating regression | 1.000 | 7 | 3 | 100% |
| 2 | Wagner, M., P. Grabarczyk, and S. H. Hong (2020) Fully modified OLS estimation and inference for seemingly unrelated cointegrating polynomial regressions and the Environmental K… | 0.961 | 18 | 6 | 89% |
| 3 | Chan, N. and Q. Wang (2015) Nonlinear regressions with nonstationary time series | 0.950 | 7 | 4 | 86% |
| 4 | Wagner, M (2015) The Environmental Kuznets Curve, cointegration and nonlinearity | 0.874 | 5 | 2 | 100% |
| 5 | Lin, Y. and H. Reuvers (2020) Efficient estimation by fully modified GLS with an application to the Environmental Kuznets Curve self | 0.843 | 3 | 3 | 100% |
| 6 | Mazzanti, M. and A. Musolesi (2013) The heterogeneity of carbon kuznets curves for advanced countries: Comparing homogeneous, heterogeneous and shrinkage/bayesian e… | 0.811 | 4 | 2 | 100% |
| 7 | Wagner, M. and S. H. Hong (2016) Cointegrating polynomial regressions: Fully modified OLS estimation and inference | 0.811 | 4 | 2 | 100% |
| 8 | Phillips, P. C. B (2007) Regression with slowly varying regressors and nonlinear trends | 0.737 | 3 | 3 | 67% |
| 9 | Choi, I. and P. Saikkonen (2010) Tests for nonlinear cointegration | 0.737 | 3 | 2 | 100% |
| 10 | Wang, Q. and P. C. B. Phillips (2009) Structural nonparametric cointegrating regression | 0.737 | 3 | 2 | 100% |
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