A Mixed Data Sampling Approach to Accounting Research

Abstract

This paper examines a mixed data sampling (MIDAS) approach to accounting research. MIDAS regression models parsimoniously incorporate variation embedded in existing economic data that are observed at much higher frequencies than accounting data. The additional source of data variation creates an opportunity to address new and important questions in accounting research. We develop and outline four new MIDAS models within the general framework that are simple to estimate and capture economic properties that are relevant to accounting research. We demonstrate the efficacy of our models with empirical applications to the January effect and to earnings response coefficients, which together illustrate the potential to expand the boundaries of accounting research by getting more out of high-frequency data.

Keywords: Mixed data sampling, ERC, January effect