Markov-switching vector autoregressive neural networks and sensitivity analysis of environment, economic growth and petrol prices
Markov-switching vector autoregressive neural networks and sensitivity analysis of environment, economic growth and petrol prices
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DOI：
10.1007/s11356-018-3062-3
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发表时间：
2018-09
影响因子：
5.8
通讯作者：
M. Bildirici;Ö. Ersin
中科院分区：
文献类型：
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作者：
M. Bildirici;Ö. Ersin
本文旨在评估二氧化碳排放、经济发展和汽油价格之间的非线性和复杂关系，以获得关于美国和英国环境库兹涅茨曲线（EKC）形状的新见解，此外还引入了一种新提出的非线性方法。在这方面，本文有三个目的：一是将多层感知器神经网络（MLP）与马尔可夫切换向量自回归（MS-VAR）型非线性模型相结合，得到MS-VAR-MLP模型。第二种方法是利用文献中最大的数据集之一，涵盖1871-2016年期间，这是一个从18世纪后期开始的长跨度数据。由于排放、经济发展和汽油价格关系受到许多历史事件的非线性和轨迹变化的影响，MS-VAR-MLP模型的发展是必要的，以促进有关EKC曲线形状和关系稳定性的持续辩论。第三个目的是开发基于ms - var - mlp的制度依赖敏感性分析，简化对非线性因果关系的直观解释，这些因果关系允许在商业周期的扩张期和衰退期的不同阶段具有不对称相互作用。我们的结果与文献中的发现有明显的偏差：(i)不能假设EKC曲线的形状是稳定的，它受制度依赖、非线性和幅度依赖的影响；（ii）预测结果表明，与MS-VAR模型相比，结合状态切换和神经网络的预测结果有显著改善；（iii）对于美国和英国以及1871-2016年期间，在商业周期的大多数阶段，经济增长对排放的积极影响是不可否认的；然而，这种影响的程度是不同的。此外，考虑到汽油价格对排放和经济增长率的影响，汽油价格的纳入提供了重要的发现。分析表明，由于排放-经济发展关系比假设的更为复杂，因此有必要利用更复杂的经验方法来评估EKC。根据这些调查结果，提出了若干政策建议。最后，将所提出的MS-VAR- mlp方法与MS-VAR模型进行了比较，并列举了各种优缺点。
The paper aims at evaluating the nonlinear and complex relations between CO 2 emissions, economic development, and petrol prices to obtain new insights regarding the shape of the environmental Kuznets curve (EKC) in the USA and in the UK in addition to introducing a newly proposed nonlinear approach. Within this respect, the paper has three purposes: the first one is to combine the multilayer perceptron neural networks (MLP) with Markov-switching vector autoregressive (MS-VAR) type nonlinear models to obtain the MS-VAR-MLP model. The second is to utilize one of the largest datasets in the literature covering the 1871–2016 period, a long span of data starting from the late eighteenth century. Since the emission, economic development, and petrol price relation is subject to nonlinearity and trajectory changes due to many historical events, the development of the MS-VAR-MLP model is a necessity to contribute to the ongoing debate regarding the shape of the EKC curve and the stability of the relation. The third purpose is to develop the MS-VAR-MLP-based regime-dependent sensitivity analysis, which eases the visual interpretation of the nonlinear causal relationships, which are allowed to have asymmetric interactions in different phases of the expansionary and recessionary periods of the business cycles. Our results provide clear deviations from the findings in the literature:(i) the shape of the EKC curve cannot be assumed to be stable and is subject to regime dependency, nonlinearity, and magnitude dependency;(ii) the forecast results suggest that incorporation of regime switching and neural networks provide significant improvement over the MS-VAR counterpart; and (iii) for both USA and UK and for the 1871–2016 period, the positive impacts of economic growth on emissions cannot be rejected for the majority of the phases of the business cycles; however, the magnitude of this effect is at various degrees. In addition, the incorporation of petrol price provides significant findings considering its effects on emission and economic growth rates. The analysis suggest clear deviations from the expected shape of the EKC curve and puts forth the necessity to utilize more complex empirical methodologies to evaluate the EKC since the emissions-economic development relation is more complex than it was assumed. Following these findings, several policy recommendations are provided. Lastly, the proposed MS-VAR-MLP methodology is compared with the MS-VAR model and various advantages and disadvantages are enumerated.