How should we estimate value-relevance models? Insights from European data

Onali, Enrico; Ginesti, Gianluca; Vasilakis, Chrysovalantis

Abstract

We study the consequences of unobserved heterogeneity when employing different econometric methods in the estimation of two major value-relevance models: the Price Regression Model (PRM) and the Return Regression Model (RRM). Leveraging a large panel data set of European listed companies, we first demonstrate that robust Hausman tests and Breusch-Pagan Lagrange Multiplier tests are of fundamental importance to choose correctly among a fixed-effects model, a random-effects model, or a pooled OLS model. Second, we provide evidence that replacing firm fixed-effects with country and industry fixed-effects can lead to large differences in the magnitude of the key coefficients, with serious consequences for the interpretation of the effect of changes in earnings and book values per share on firm value. Finally, we offer recommendations to applied researchers aiming to improve the robustness of their econometric strategy.

Citation

Onali, E., Ginesti, G., & Vasilakis, C. (2017). How should we estimate value-relevance models? Insights from European data. British Accounting Review, 49(5), https://doi.org/10.1016/j.bar.2017.05.006

• Journal Article Type: Article
• Acceptance Date: May 22, 2017
• Online Publication Date: May 27, 2017
• Publication Date: Sep 30, 2017
• Deposit Date: Jun 18, 2018
• Publicly Available Date: May 28, 2019
• Journal: British Accounting Review
• Print ISSN: 0890-8389
• Electronic ISSN: 1095-8347
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 5
• DOI: https://doi.org/10.1016/j.bar.2017.05.006
• Keywords: Value-relevance ; Linear information model ; IFRS ; Price regression model ; Return regression model ; Panel data
• Public URL: https://nottingham-repository.worktribe.com/output/885745
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0890838917300306
• Contract Date: Jun 18, 2018