On the Choice of Instruments in Mixed Frequency Specification Tests

TL;DR

This paper introduces a new specification test to decide between traditional time averaging and MIDAS models for mixed frequency data, leveraging instrumental variables to improve power and accuracy.

Contribution

It proposes a Durbin-Wu-Hausman based test with validated instruments for large frequency ratios, enhancing model selection in mixed frequency data analysis.

Findings

The proposed test outperforms existing methods in simulations.

Instrumental variables are effective for large frequency ratios.

The method provides a more powerful model selection criterion.

Abstract

Time averaging has been the traditional approach to handle mixed sampling frequencies. However, it ignores information possibly embedded in high frequency. Mixed data sampling (MIDAS) regression models provide a concise way to utilize the additional information in high-frequency variables. In this paper, we propose a specification test to choose between time averaging and MIDAS models, based on a Durbin-Wu-Hausman test. In particular, a set of instrumental variables is proposed and theoretically validated when the frequency ratio is large. As a result, our method tends to be more powerful than existing methods, as reconfirmed through the simulations.