B1006
Title: Panel nonparametric MIDAS model: A clustering approach
Authors: Yun Liu - Michigan Technological University (United States)
Yeonwoo Rho - Michigan Technological University (United States) [presenting]
Abstract: The mixed data sampling regression (MIDAS) models are developed to handle different sampling frequencies in one regression model, preserving information in the higher sampling frequency. While a parametric MIDAS model provides a parsimonious way to summarize information in high frequency data, one parametric form may not necessarily be appropriate for all cross-sectional subjects. In the effort to identify groups in a panel data setting involving mixed frequencies, a flexible MIDAS model is proposed using a nonparametric approach. This nonparametric MIDAS model is further extended to a panel setting using a penalized regression idea. The estimated parameters can then be clustered using traditional clustering methods. The proposed clustering algorithm delivers reasonable clustering results both in theory and in simulations, without requiring prior knowledge about the true group membership information.
