New method automatically selects significant variables in dynamic panel data models.
The article focuses on developing a method to efficiently estimate and select important variables in a complex statistical model. The researchers use a combination of parametric and nonparametric techniques to estimate different components of the model. They also introduce a method to automatically identify significant variables and determine the correct lagged order. The results show that their approach achieves accurate estimation even when the true variables are unknown, demonstrating its effectiveness in practical applications.
