A Mixed Data Sampling Approach to Accounting Research

49 Pages Posted: 10 Oct 2018

See all articles by Ryan T. Ball

Ryan T. Ball

The Stephen M. Ross School of Business at the University of Michigan

Lindsey A. Gallo

University of Michigan, Stephen M. Ross School of Business

Date Written: September 16, 2018

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

Suggested Citation

Ball, Ryan T. and Gallo, Lindsey A., A Mixed Data Sampling Approach to Accounting Research (September 16, 2018). Available at SSRN: https://ssrn.com/abstract=3250445 or http://dx.doi.org/10.2139/ssrn.3250445

Ryan T. Ball (Contact Author)

The Stephen M. Ross School of Business at the University of Michigan ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Lindsey A. Gallo

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States
7347648243 (Phone)

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