Mixed data sampling (MIDAS) regression models

Abstract

Abstract Mixed data sampling (MIDAS) regressions are now commonly used to deal with time series data sampled at different frequencies. This chapter focuses on single-equation MIDAS regression models involving stationary processes with the dependent variable observed at a lower frequency than the explanatory ones. We discuss in detail nonlinear and semiparametric MIDAS regression models, topics not covered in prior work. Moreover, fitting the theme of the handbook, we also elaborate on the R package midasr associated with the regression models using simulated and empirical examples. In the theory part, a stylized model is introduced in order to discuss specific issues relevant to the construction of MIDAS models, such as the use or nonuse of functional constraints on parameters, the types of constraints and their choice, and the selection of the lag order. We introduce various new MIDAS regression models, including quasi-linear MIDAS, models with nonparametric smoothing of weights, logistic smooth transition and min–mean–max effects MIDAS, and semiparametric specifications.