计量经济分析 = Econometric analysis

In this article we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM. This variance estimator enables cluster-robust inference when there is two-way or multiway clustering that is nonnested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g., Liang and Zeger 1986; Arellano 1987) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state–year effects example of Bertrand, Duflo, and Mullainathan (2004) to two dimensions; and by application to studies in the empirical literature where two-way clustering is present.

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Robust Inference with Multi-way Clustering

I study whether managers use real activities manipulation and accrual-based earnings management as substitutes in managing earnings. I find that managers trade off the two earnings management methods based on their relative costs and that managers adjust the level of accrual-based earnings management according to the level of real activities manipulation realized. Using an empirical model that incorporates the costs associated with the two earnings management methods and captures managers’ sequential decisions, I document large sample evidence consistent with managers using real activities manipulation and accrual-based earnings management as substitutes.

Evidence on the Trade-Off between Real Activities Manipulation and Accrual-Based Earnings Management

This study examines whether socially responsible firms behave differently from other firms in their financial reporting. Specifically, we question whether firms that exhibit corporate social responsibility (CSR) also behave in a responsible manner to constrain earnings management, thereby delivering more transparent and reliable financial information to investors as compared to firms that do not meet the same social criteria. We find that socially responsible firms are less likely (1) to manage earnings through discretionary accruals, (2) to manipulate real operating activities, and (3) to be the subject of SEC investigations, as evidenced by Accounting and Auditing Enforcement Releases against top executives. Our results are robust to (1) controlling for various incentives for CSR and earnings management, (2) considering various CSR dimensions and components, and (3) using alternative proxies for CSR and accruals quality. To the extent that we control for the potential effects of reputation and financial performance, our findings suggest that ethical concerns are likely to drive managers to produce high-quality financial reports.

https://www.researchgate.net/profile/Benson_Wier/publication/228133769_Is_Earnings_Quality_Associated_with_Corporate_Social_Responsibility/links/0deec530caf0ff0c3a000000.pdf

Is Earnings Quality Associated with Corporate Social Responsibility

Using a large sample of U.S. firms for the period 1995-2008, we provide strong and robust evidence that corporate tax avoidance is positively associated with firm-specific stock price crash risk. This finding is consistent with the following view: Tax avoidance facilitates managerial rent extraction and bad news hoarding activities for extended periods by providing tools, masks, and justifications for these opportunistic behaviors. The hoarding and accumulation of bad news for extended periods lead to stock price crashes when the accumulated hidden bad news crosses a tipping point, and thus comes out all at once. Moreover, we show that the positive relation between tax avoidance and crash risk is attenuated when firms have strong external monitoring mechanisms such as high institutional ownership, high analyst coverage, and greater takeover threat from corporate control markets.

Corporate Tax Avoidance and Stock Price Crash Risk: Firm-Level Analysis

We review and evaluate the methods commonly used in the accounting literature to correct for cross-sectional and time-series dependence. While much of the accounting literature studies settings where variables are cross-sectionally and serially correlated, we find that the extant methods are not robust to both forms of dependence. Contrary to claims in the literature, we find that the Z2-statistic and Newey-West corrected Fama-MacBeth do not correct for both cross-sectional and time-series dependence. We show that extant methods produce misspecified test statistics in common accounting research settings, and that correcting for both forms of dependence substantially alters inferences reported in the literature. Specifically, several findings in the cost of equity capital literature, the cost of debt literature, and the conservatism literature appear not to be robust to the use of well-specified test statistics.

Correcting for Cross-Sectional and Time-Series Dependence in Accounting Research

We review and evaluate the methods commonly used in the accounting literature to correct for cross‐sectional and time‐series dependence. While much of the accounting literature studies settings in which variables are cross‐sectionally and serially correlated, we find that the extant methods are not robust to both forms of dependence. Contrary to claims in the literature, we find that the Z2 statistic and Newey‐West corrected Fama‐MacBeth standard errors do not correct for both cross‐sectional and time‐series dependence. We show that extant methods produce misspecified test statistics in common accounting research settings, and that correcting for both forms of dependence substantially alters inferences reported in the literature. Specifically, several findings in the implied cost of equity capital literature, the cost of debt literature, and the conservatism literature appear not to be robust to the use of well‐specified test statistics.

Correcting for Cross‐Sectional and Time‐Series Dependence in Accounting Research

Proxy advisory and corporate governance rating firms play an increasingly important role in U.S. public markets. Proxy advisory firms provide voting recommendations to shareholders on proxy proposals and sometimes take an active role persuading management to change governance arrangements. Corporate governance rating firms provide indices to evaluate the effectiveness of a firm's governance and claim to be able to predict future performance, risk, and undesirable outcomes such as accounting restatements and shareholder litigation. We examine these claims for the commercial corporate governance ratings produced for 2005 by Audit Integrity, RiskMetrics (previously Institutional Shareholder Services), GovernanceMetrics International, and The Corporate Library. Our results indicate that the level of predictive validity for these ratings are well below the threshold necessary to support the bold claims made for them by these commercial firms. Moreover, we find no relation between the governance ratings provided by RiskMetrics with either their voting recommendations or the actual votes by shareholders on proxy proposals.

Rating the Ratings: How Good are Commercial Governance Ratings?

This paper examines the approaches accounting researchers adopt to draw causal inferences using observational (or nonexperimental) data. The vast majority of accounting research papers draw causal inferences notwithstanding the well-known difficulties in doing so. While some recent papers seek to use quasi-experimental methods to improve causal inferences, these methods also make strong assumptions that are not always fully appreciated. We believe that accounting research would benefit from more in-depth descriptive research, including a greater focus on the study of causal mechanisms (or causal pathways) and increased emphasis on the structural modeling of the phenomena of interest. We argue these changes offer a practical path forward for rigorous accounting research.

Causal Inference in Accounting Research

Prior research generally interprets complex language in firms’ disclosures as indicative of managerial obfuscation. However, complex language can also reflect the provision of complex information, e.g., informative technical disclosure. As a consequence, linguistic complexity commingles two latent components—obfuscation and information—that are related to information asymmetry in opposite directions. We develop a novel empirical approach to estimate these two latent components within the context of quarterly earnings conference calls. We validate our estimates of these two latent components by examining their relation to information asymmetry. Consistent with our predictions, we find that our estimate of the information component is negatively associated with information asymmetry while our estimate of the obfuscation component is positively associated with information asymmetry. Our findings suggest that future research on linguistic complexity can construct more powerful tests by separately examining these two latent components of linguistic complexity.

This article is protected by copyright. All rights reserved

Ian D. Gow

Papers

Correcting for Cross-Sectional and Time-Series Dependence in Accounting Research

Correcting for Cross‐Sectional and Time‐Series Dependence in Accounting Research

Rating the Ratings: How Good are Commercial Governance Ratings?

Causal Inference in Accounting Research

Linguistic Complexity in Firm Disclosures: Obfuscation or Information?