This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under classical assumptions on the data, provided that the controlled lag order diverges. Thus, the commonly perceived efficiency loss of LPs can become asymptotically negligible with many controls. Second, we propose LP-based inference procedures for (level and cumulated) impulse responses that possess robustness properties not shared by existing methods. Inference methods using two distinct standard errors are considered. The uniform validity for the first method depends on a zero fourth-order cumulant condition on shocks, while that of the second holds more generally for conditionally heteroskedastic martingale-difference shocks. We propose a bootstrap procedure that improves finite-sample performance and extend the standard error construction to structural responses.

Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium.

Firms’ organizational structures impose constraints on their ability to use promotion-based incentives. We develop a framework for identifying these constraints and exploring their consequences. We show that firms manage workers’ careers by choosing personnel policies that resemble an internal labor market. Firms may adopt forced turnover policies to keep lines of advancement open, and they may alter their organizational structures to relax these constraints. This gives rise to a trade-off between incentive provision at the worker level and productive efficiency at the firm level. Our framework generates novel testable implications that connect firm-level characteristics with workers’ careers.

A major threat to the development of financial markets in emerging markets is “tunneling.” In China, this took on the form of controlling shareholders diverting assets from listed firms or coercing firms to serve as guarantors on questionable loans. A new set of rules enacted in 2005 prohibited asset diversion for “non-operational” purposes. Firms complying with these rules have experienced a reduction in related party transactions, an increase in investment, and better performance. In contrast, another set of contemporary rules, which aimed to standardize the practice of firms providing loan guarantees, has had very little impact. We attribute the contrasting design, implementation, and effectiveness of these two sets of rules to the difference in enforcement costs of the two types of tunneling activities. Relative to loan guarantees, it is much easier for a third party to determine (ex ante) whether a particular form of diversion destroys firm value, and to verify (ex post) that the losses to the firm resulted from the diversion. Our results highlight the importance of enforceability—laws and regulations that can be enforced at lower costs are more likely to succeed, especially in countries with underdeveloped formal institutions.

Boards of directors play their role in corporate governance by advising and/or monitoring managers. In the corporate disclosure literature, prior research has documented directors' monitoring role, yet empirical evidence on directors' advising role is limited. Since the advising role often entails information transfer, we examine directors who concurrently serve as directors or executives in the firms' related industries (DRIs) and hence possess valuable information about the firms' external operating environment. We hypothesize and find that more DRIs on boards are associated with more accurate management forecasts. This association is stronger when firms face greater uncertainty, and holds in settings where DRIs are unlikely to monitor managers, suggesting a distinct advising role of DRIs. Our study highlights directors' role as information suppliers and advisors who help shape corporate voluntary disclosure.

We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory‐motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support‐vector‐machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.