The paper tests the assumptions underlying the inference-in-residuals method as an estimation framework for detecting and classifying suspects of earnings management. We derive several systematic biases that are shown to confound inference-in-residuals and, depending on the data, could render the method a futile exercise. This is not a matter of model specification, but a limitation of the statistical method. Also, it is shown that the method of using estimated residuals in a second stage regression on economic determinants of earnings management suffers considerably, especially when residuals are estimated by industry classification in the first stage.

This paper focuses on earnings conservatism, and provides new evidence based on procedures that account for variability at the firm level, drawing a comparison between the European Union and the United States. A key finding is that the estimated responsiveness of earnings to bad news is substantially higher when unobserved firm-specific effects are modelled. Furthermore, it is shown that accounting has become more conservative not only in the U.S. but also in the EU when taken as a whole, and there is little evidence of marked differences in the asymmetric timeliness of earnings between the two. Indeed, any changes in this property of earnings are likely to be attributable to a common factor that influences firms similarly in both locations, and not necessarily to the process of economic convergence that has taken place in the EU.

IntroductionThis paper proposes a structural regression system for use with financial statement variables. The key innovation of the proposed framework is the recovery of estimates that adhere to the rules of double entry bookkeeping. The need for such a framework is evident in empirical research, precisely due to the highly structured nature of financial statements and the one-for-one correspondence that is inherent in the transaction-based accounts from which financial statements are prepared (see, e.g., Ijiri 1967; Kang and Sivaramakrishnan 1995). 1 Double entry requires a change in one account to be offset by an equal change in one or more other accounts, with the codetermination of accounting variables taking place through the resolution of the multiple accounting identities that govern the articulation of financial statements, that is, the reconciliation between the balance sheet at time t, its comparatives at tÀ1, and the income and cash flow statements for the period from tÀ1 to t. We propose that no financial statement variable can be considered as strictly exogenous to the estimation of any other contemporaneous financial statement variable. Accordingly, this paper treats accounting variables as endogenous within a regression model that reflects the structure of the underlying accounting system. Furthermore, the key feature of the structural framework is the inclusion of a double entry parameter constraint to ensure that estimates converge to their theoretically expected relationships.We illustrate the efficacy of the approach by incorporating the double entry structure in regression estimation for two established applications that include variables that are governed by well known accounting identities: (1) the Penman and Yehuda (2009) equity pricing model and (2) the Zingales (1995, 1997) test of investmentcash flow sensitivity, based on the earlier work by Fazzari, Hubbard and Petersen (1988).Section 2 outlines the limitations on regression in the presence of accounting endogeneity and explains the double entry structure underlying each of the two applications. Section 3 develops a fully identified solution for the Penman and Yehuda (2009) model, followed by empirical analysis. Section 4 develops a fully identified solution for the Fazzari et al. (1988) model, again followed by empirical analysis. Section 5 concludes.

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