Research
“Starry Night Over the Rhône ,” Vincent van Gogh, 1888.
“Starry Night Over the Rhône ,” Vincent van Gogh, 1888.
1). Dai, S., Hong, Y., Li, H., & Zheng, C. (2025) Shrinkage Estimation of Spatial Panel Data Models with Multiple Structural Breaks and a Multifactor Error Structure. Journal of Econometrics, Accepted.
2).Wang, W., Wooldridge, J. M., Xu, M., Lu, C., & Zheng, C. (2025). Using generalized estimating equations to estimate nonlinear models with spatial data. Econometric Reviews, 44(2), 214-242.
3). Xu, X., Wang, W., Shin, Y., & Zheng, C. (2024) Dynamic network quantile regression model. Journal of Business & Economic Statistics, 42,407-421
4). Chen, J., Shin, Y., & Zheng, C. (2022) Estimation and inference in heterogeneous spatial panels with a multifactor error structure. Journal of Econometrics, 229, 55-79.
1). Estimation of dynamic quantile panel data with interactive effects. (Job Market Paper)
Abstract: Although the Brexit is done, it has provoked a renewed interest in studying the effect of a EU membership (EUM), or a more general free trade agreement (FTA), on bilateral trade. While researchers have not reached a consensus by utilising different econometric techniques, they generally assume the effect to be the same at different stages of a Business Cycle. The existing results are therefore unlikely to tell a complete story. In this paper, I developed a quantile panel model to characterise the distributional effects of EMU/FTA on bilateral trade, while admitting the high-persistence and strong cross-section dependence properties of the trade data. By estimating an international trade gravity model for 380 countries pairs, I discovered that the long run effect of EMU on bilateral trade is much higher at lower quantiles (118.72%) than at higher quantiles (27.40%), implying that the boosting effect of EMU is more significant during economic recession.
2). A Spatio-temporal autoregressive factor model of the global business cycle. (joint with Tomohiro Ando, Matthew Greenwood-Nimmo and Yongcheol Shin)
Abstract: To study the synchronicity of national business cycles, we propose a new heterogeneous-parameter approach in which the global business cycle is modelled as a spatio-temporal autoregressive process with a common factor error structure. To achieve consistent estimation in the presence of parameter heterogeneity and endogeneity, we develop a modified quasi maximum likelihood estimation approach. We show that the resulting estimators are consistent and asymptotically normally distributed. We employ Monte Carlo simulations to demonstrate that their finite sample performance is satisfactory. Based on the proposed estimator, we further develop network analysis tools at both individual and regional level using diffusion FEVDs and multipliers. These tools are then applied to analyse the business cycle synchronisation covering 79 countries in the world over the period 1970-2019 (50 years).
1). A unified approach for panel data models with latent group structure. (joint with Jia Chen)
Research Assistant for the ESRC project “New Cross-Sectionally Dependent Panel Data Methods for the Analysis of Macroeconomic and Financial Networks” (grant number ES/T01573X/1, in progress)