Abstract

Researchers regularly use instrumental variables to resolve concerns about regressor endogeneity. The existing literature has correctly emphasized that the choice of instrumental variables matter for the resulting estimates. This paper shows that researchers should also be concerned that the functional form of the instrument matters as well for the resulting estimates. For example, simply changing an instrumental variable from the level to the logarithm can change estimates directly. This article documents the problem, suggests why the problem occurs and suggests different approaches to the problem.

Causal Inferences in Capital Markets Research

This article is included as a chapter in Causal Inferences in Capital Markets Research.

Causal Inferences in Capital Markets Research is an attempt to promote a broad interdisciplinary debate about the notion of causality and the role of causal inference in the social sciences.

At the risk of oversimplifying, the issue of causality divides the accounting research community in two polar views: the view that causality is an unattainable ideal for the social sciences and must be given up as a standard, and the view that, on one hand, causality should be the ultimate goal of all scientific endeavors and, on the other hand, theory and causal inference are inextricable. Reflecting and discussing these views was the main motivation for this volume. This volume contains eight articles on three topics: I) Econometrics; III) Accounting, and III) Finance.

First, Nancy Cartwright addresses the problem of external validity and the reliability of scientific claims that generalize individual cases. Then, John Rust discusses the role of assumptions in empirical research and the possibility of assumption-free inference. Peter Reiss considers the question how sensitive are instrumental variables to functional form transformations. Finally, Charles Manski studies the logical issues that affect the interpretation of point predictions.

Second, Jeremy Bertomeu, Anne Beyer and Daniel Taylor provide a critical overview of empirical accounting research focusing on the benefits of theory-based estimation, while Qi Chen and Katherine Schipper consider the question whether all research should be causal and assess the existing gap between theory and empirical research in accounting.

Third, R. Jay Kahn and Toni Whited clarifies and contrasts the notions of identification and causality, whereas Ivo Welch adopts a sociology of science approach to understand the consequences of the researchers’ race for discovering novel and surprising results.

This volume allows researchers and Ph.D students in accounting, and the social sciences in general, to acquire a deeper understanding of the notion of causality and the nature, limits, and scope of empirical research in the social sciences.