THRESHOLD REGRESSIONS WITH ENDOGENEITY
My research is mainly in threshold regressions with endogeneity. The threshold regression model is a class of models that internally sorts the data based on some threshold determinants into groups of observations, each obeying the same model. The use of threshold regression provides a parsimonious way of modeling and estimating the nonlinear economic relationship.
I started to work on this topic in 2015. At that time, I was eager to find a proper method to test the public debt overhang problem. I realized the threshold model with endogeneity could perfectly fit my needs. Methodologically, I work on proposing new models or estimation methods that deals with presence of endogeneity in a threshold regression model. I also apply the well-established models in various empirical scenarios, for example, capturing the nonlinear relationship between renewable energy and economic growth.
In the future, theoretical researchers can advance the work in associating the threshold model with high dimensional data. Empirical researchers can explore the nonlinear effect of growth on the financial or use the panel threshold approach to revisit the democracy and credit nexus.
Threshold regression is well connected to statistics. As a method working for multidiscipline, threshold models are also frequently used in fields like energy, environmental science, sociology, etc.
My research was inspired by works from some excellent scholars. To name a few here, Donald W. K. Andrews, Mehmet Caner, Oliver Linton, Jianqing Fan, Myung Hwan Seo, Thanasis Stengos, and Yiguo Sun.
Most important paper
The Democracy-Growth nexus: A Smoothed GMM Linear Index Threshold Model Approach (unpublished yet)
Course relating to my research:
Artificial intelligence and machine learning methodology (Mesterséges intelligencia és gépi tanulás módszertana)
Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 64 (2), 413-430.
Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica 68, 575-603.
Kourtellos, A., T. Stengos, and C. M. Tan (2016). Structural threshold regression. Econometric Theory 32 (4), 827-860.
Seo, M. H. and O. Linton (2007). A smoothed least squares estimator for threshold regression models. Journal of Econometrics 141 (2), 704-735.
Seo, M. H. and Y. Shin (2016). Dynamic panels with threshold effect and endogeneity. Journal of Econometrics 195 (2), 169-186.
Chen.C., M. Pinar, and T. Stengos (2021): “Determinants of Renewable Energy Consumption: Importance of Democratic Institutions”, Renewable Energy, 179, 75-83.
Chen.C., M. Pinar, and T. Stengos (2020): “Renewable Energy Consumption and Economic Growth Nexus: Evidence from a Threshold Model”, Energy Policy, 139, 111295.
Chen, C., P. Michael, and T, Stengos (2020): “Re-examining the Asymmetric Gasoline Pricing Mechanism in EU: a Note on a Panel Threshold Analysis”, Applied Economics Letters, 27:10, 778-783.
Chen, C., P. Michael, and T, Stengos (2019): “Can Exchange Rate Pass-Through Explain the Asymmetric Gasoline Puzzle? Evidence from a Pooled Panel Threshold Analysis of the EU”, Energy Economics, 81, 1-12.
Chen, C., and Y. Sun (2018): “Monte Carlo Comparison for Nonparametric Threshold Estimators”, Journal of Risk and Financial Management, 11, 49.
Chen, C., P. Michael, and T, Stengos (2018): “On the Examination of Non-linear Relationship between Market Structure and Performance in the US Manufacturing Industry”, Economics Letters, 164, 1-4.
További publikációkat ld.: