论文标题
通过电力法趋势共同集成多项式回归：环境库兹尼茨曲线还是省略时间影响？
Cointegrating Polynomial Regressions with Power Law Trends: Environmental Kuznets Curve or Omitted Time Effects?
论文作者
论文摘要
环境Kuznets曲线预测了环境污染与经济增长之间的倒U形关系。当前的分析经常采用限制数据中非线性的模型，仅由经济增长变量来解释。我们提出了广泛的共同点多项式回归（GCPR），以允许替代非线性来源。更具体地说，GCPR是一种看似无关的回归，具有（1）单个单元的确定性和随机趋势的整数功能，以及（2）常见的灵活全球趋势。我们通过非线性最小二乘正方形估算此GCPR，并得出其渐近分布。回归器的内生性将将滋扰参数引入限制分布中，但是基于模拟的方法仍然使我们能够进行有效的推理。提出了多元子采样KPSS测试，以验证协调关系的正确规范。我们的模拟研究显示了模拟推理方法和亚采样KPSS测试的良好性能。我们使用有关奥地利，比利时，芬兰，荷兰，瑞士和英国的数据来说明GCPR方法。一个全球趋势准确地捕获了所有非线性，从而导致所有国家的GDP和CO2之间的线性协整关系。这表明过去几年的环境改善是由于经济因素与GDP不同。
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.
