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.

Dynamic Models and Structural Estimation in Corporate Finance has three goals: (1) To explain the models and techniques used in this literature as simply as possible, with the intent of making the literature more accessible. (2) To introduce the reader to the main strands of this literature. This monograph can therefore be viewed in part as a literature review and in part as a tutorial. (3) To explain how dynamic models can be taken to the data and be estimated with the intent to provide a practical, hands-on guide to three specific methodologies that have been used in the literature: generalized method of moments, simulated method of moments, and maximum simulated likelihood. Dynamic Models and Structural Estimation in Corporate Finance provides a concise guide to the extant structural estimation literature in corporate finance. Following an introduction, Section 2 provides an overview of dynamic corporate finance models based on techniques developed in the continuous time contingent claims literature. Section 3 covers a separate strand of the literature that stems discrete time investment models. Section 4 reviews the relatively small number of different econometric techniques that have been used to estimate these models, as well as the studies that have used them. The authors close with a brief overview of directions for future research.