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

Enrico Onali*, Gianluca Ginesti, Chrisovalantis Vasilakis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)460-473
JournalBritish Accounting Review
Volume49
Issue number5
Early online date27 May 2017
DOIs
Publication statusPublished - 1 Sep 2017

Bibliographical note

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • value-relevance
  • linear information model
  • IFRS
  • price regression model
  • return regression model
  • panel data

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