Li Tao, Yuanjie Zhang, Maozai Tian

【Abstract】

The study of dynamic panel data is mainly based on the conditional mean regression methods, but constrained by classical assumptions. The quantile regression model for dynamic panel data considered, not only solves the problem with constraint, but also fully describes the conditional distribution of the response variables. This paper presents a method to study dynamic panel data with instrument variables based on adaptive penalty quantile regression, and proves that the proposed estimator has large sample properties. Monte Carlo simulation study shows that the proposed method is better than traditional methods. In the end, the paper analyzes the case study on the relationship between the selling price of commercial houses and per capita GDP in large and medium cities of China, and founds that there is a positive feedback mechanism between them.

【Keywords】

Dynamic panel data, quantile regression, adaptive penalty function, fixed effexts, real estate prices