Regression Models with Mixed Sampling Frequencies

We study regression models that involve data sampled at different frequencies. We derive the asymptotic properties of the NLS estimators of such regression models and compare them with the LS estimators of a traditional model that involves aggregating or equally weighting data to estimate a model at the same sampling frequency. In addition we provide a new aggregation bias test. We explore the above theoretical aspects and verify them via an extensive Monte Carlo simulation study and an empirical application.