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

Onali, Enrico; Ginesti, Gianluca; Vasilakis, Chrysovalantis


Enrico Onali

Gianluca Ginesti

Chrysovalantis Vasilakis


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.


Onali, E., Ginesti, G., & Vasilakis, C. (2017). How should we estimate value-relevance models? Insights from European data. British Accounting Review, 49(5),

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 0890-8389
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 49
Issue 5
Keywords Value-relevance ; Linear information model ; IFRS ; Price regression model ; Return regression model ; Panel data
Public URL
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