Aggregate Cost Stickiness in GAAP Financial Statements and Future Unemployment Rate

The Accounting Review, 2017

54 Pages Posted: 11 Jan 2015 Last revised: 27 Feb 2020

See all articles by Florent Rouxelin

Florent Rouxelin

University of New South Wales (UNSW)

Wan Wongsunwai

The Chinese University of Hong Kong (CUHK)

Nir Yehuda

University of Texas at Dallas - Department of Accounting & Information Management

Date Written: August 1, 2017

Abstract

We examine whether aggregate cost stickiness predicts future macrolevel unemployment rate. We incorporate aggregate cost stickiness into three different classes of forecasting models studied in prior literature, and demonstrate an improvement in forecasting performance for all three models. For example, when adding cost stickiness to an OLS regression, which includes a battery of macroeconomic indicators and control variables, we find that a one-standard-deviation-higher cost stickiness in recent quarters is followed by a 0.23 to 0.26-percentage-point-lower unemployment rate in the current and following quarter. In out-of-sample tests, we find significant reductions in the root-mean-squared-errors upon incorporation of cost stickiness for all three models. Additional tests suggest that professional macro forecasters, particularly those employed in nonfinancial industries, do not fully incorporate the information contained in cost stickiness. Finally, we find a stronger predictive power of cost stickiness towards the end of recessionary periods; we also assess cross-sectional variation of this predictive ability.

Keywords: Cost behavior, cost stickiness, unemployment rate forecasting

JEL Classification: M41, E24, J60

Suggested Citation

Rouxelin, Florent and Wongsunwai, Wan and Yehuda, Nir, Aggregate Cost Stickiness in GAAP Financial Statements and Future Unemployment Rate (August 1, 2017). The Accounting Review, 2017. Available at SSRN: https://ssrn.com/abstract=2547789 or http://dx.doi.org/10.2139/ssrn.2547789

Florent Rouxelin

University of New South Wales (UNSW) ( email )

Kensington
High St
Sydney, NSW 2052
Australia

Wan Wongsunwai (Contact Author)

The Chinese University of Hong Kong (CUHK) ( email )

Shatin, N.T.
Hong Kong
Hong Kong

Nir Yehuda

University of Texas at Dallas - Department of Accounting & Information Management ( email )

2601 North Floyd Road
Richardson, TX 75083-0688
United States

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