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.

/pdf/robust-inference-with-multi-way-clustering-buxopcmamj.pdf

Robust Inference with Multi-way Clustering

Earnings Management and Investor Protection: An International Comparison

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

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

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1023&context=accounting_papers

The relation between equity incentives and misreporting: The role of risk-taking incentives.

https://www.uts.edu.au/sites/default/files/ADG_Cons2015_Market%2BReaction%2Bto%2BCorporate%2BGovernance%2BRegulation.pdf

Gaizka Ormazabal

Papers

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

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

The relation between equity incentives and misreporting: The role of risk-taking incentives.

The market reaction to corporate governance regulation

The Big Three and corporate carbon emissions around the world