Application of Panel Data Model to Economic Effects of High-Speed Railway

2020 ◽  
Vol 16 (7) ◽  
pp. 1130 ◽  
Author(s):  
Wang Tiantian ◽  
Li Baiji ◽  
Zhang Gongpeng
2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.


2021 ◽  
Vol 40 (7) ◽  
pp. 688-707
Author(s):  
Yan Meng ◽  
Jiti Gao ◽  
Xibin Zhang ◽  
Xueyan Zhao

Kybernetes ◽  
2020 ◽  
Vol 49 (11) ◽  
pp. 2713-2735 ◽  
Author(s):  
Xiaomin Fan ◽  
Yingzhi Xu ◽  
Yongqing Nan ◽  
Baoli Li ◽  
Haiya Cai

Purpose The purpose of this paper is to analyse the impact of high-speed railway (HSR) on industrial pollution emissions using the data for 285 prefecture-level cities in China from 2004 to 2016. Design/methodology/approach The research method used in this paper is the multi-period difference-in-differences (DID) model, which is an effective policy effect assessment method. To further address the issue of endogeneity, the DID integrated with the propensity score matching (PSM-DID) approach is employed to eliminate the potential self-selection bias. Findings The results show that the HSR has significantly reduced industrial pollution emissions, which is validated by several robustness tests. Compared with peripheral cities, HSR exerts a greater impact on industrial pollution emissions in central cities. In addition, the mechanism test reveals that the optimised allocation of inter-city industries is an important channel for HSR to mitigate industrial pollution emissions, and this is closely related to the location of HSR stations. Originality/value Previous studies have paid more attention to evaluating the economic effects of HSR, however, most of these studies overlook its environmental effects. Consequently, the impact of HSR on industrial pollution emissions is led by using multi-period DID models in this paper, in which the environmental effects are measured. The results of this paper can provide a reference for the pollution reduction policies and also the coordinated development of economic growth and environmental quality.


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