This essay describes the evolution and recent convergence of two methodological approaches to causal inference. The first one, in statistics, started with the analysis and design of randomized experiments. The second, in econometrics, focused on settings with economic agents making optimal choices. I argue that the local average treatment effects framework facilitated the recent convergence by making key assumptions transparent and intelligible to scholars in many fields. Looking ahead, I discuss recent developments in causal inference that combine the same transparency and relevance.

A joint model for the Value at Risk (VaR) and expected shortfall (ES) can be estimated using a joint scoring function. Previous work has modelled the ES as the product of the VaR and a constant factor. However, this implies the same dynamics for the ES and the VaR. We propose a time-varying multiplicative factor. The ES has been expressed as the product of an expectile and a constant factor that depends on the expectile level. We rewrite this as the product of a quantile and a function of a time-varying expectile level. The expectile level is a function of the Omega ratio, which is the ratio of the expected gain to the expected loss. This leads us to model the ES as the product of the VaR and a factor that is a function of a time-varying Omega ratio. We provide empirical support using stock indices and individual stocks.

Abstract The interaction between product market competition, R&D investment, and the financing choices of R&D-intensive firms on the development of innovative products is only partially understood. We hypothesize that as competition increases, R&D-intensive firms will: i) increase R&D investment relative to existing assets in place; ii) carry more cash; and iii) maintain less net debt. Using the Hatch–Waxman Act as an exogenous shock to competition, we provide causal evidence supporting these hypotheses through a differences-in-differences analysis that exploits differences between the biopharma industry and other industries, and heterogeneity within the biopharma industry. We also explore how these changes affect innovative output.

ABSTRACT This paper examines whether and how individual auditors are disciplined for audit errors. Taking advantage of the long history of auditor identity data from China, we find that signing auditors with client restatements are likely to lose the privilege of signing the audit reports of public clients. However, auditors can avoid this consequence by issuing a modified audit opinion to warn of the potential misstatement. We show that auditors are more likely to be disciplined when their firms operate in less concentrated audit markets. Finally, we find positive outcomes from the disciplinary action of the audit firms. Firms that discipline their auditors for restatements have a larger decrease in the rate of client restatements and a larger increase in market share, compared to nondisciplining firms. Their clients have a higher earnings response coefficient after the disciplinary action. In summary, our results suggest that individual auditors in China can face career setbacks when they produce poor quality audits.

ABSTRACT This paper studies the long‐term consequences of actions induced by vesting equity, a measure of short‐term incentives. Vesting equity is positively associated with the probability of a firm repurchasing shares, the amount of shares repurchased, and the probability of the firm announcing a merger and acquisition (M&A). However, it is also associated with more negative long‐term returns over two to three years following repurchases and four years following M&A, as well as future M&A goodwill impairment. These results are inconsistent with CEOs buying underpriced stock or companies to maximize long‐run shareholder value, but consistent with these actions being used to boost the short‐term stock price and thus equity sale proceeds. CEOs sell their own stock shortly after using company money to buy the firm's stock, also inconsistent with repurchases being motivated by undervaluation.

ABSTRACT We use experimental markets to examine how pushing investment information and the value relevance of that information interact to influence investors’ value estimate accuracy and market price efficiency. Developments in technology allow information to be pushed to investors anytime and anywhere. However, in addition to value‐relevant information, pushed information often includes information that is irrelevant for assessing firm value. Drawing on psychology theory, we find that pushing information has divergent effects depending on the value relevance of the information. Pushing only value‐relevant information increases investors’ processing of the information and leads to more accurate value estimates and market prices than when not pushed. In contrast, pushing a mix of value‐relevant and value‐irrelevant information reduces investors’ processing of value‐relevant information, leading to less accurate value estimates and market prices due to poorer acquisition and integration of information than when not pushed or when only value‐relevant information is pushed. Collectively, our results reveal a dark side to push technologies, particularly with the growing presence of value‐irrelevant information.

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.

Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our equilibrium model, N assets have cash flows that are linear in J characteristics, with unknown coefficients. Risk-neutral Bayesian investors learn these coefficients and determine market prices. If J and N are comparable in size, returns are cross-sectionally predictable ex post. In-sample tests of market efficiency reject the no-predictability null with high probability, even though investors use information optimally in real time. In contrast, out-of-sample tests retain their economic meaning.

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ABSTRACT We examine how the extent and distribution of industry knowledge within an audit team affect audit outcomes. While prior research examining the role of auditors' industry knowledge focuses mainly on audit firms, audit offices, and audit partners, audits are conducted by audit teams. Using an audit framework and proprietary data from a Big 4 firm that includes audit hours for each team member, we find that Big 4 audit teams with higher average industry knowledge are associated with more audit effort. In contrast, we find mixed evidence on the relation between the average hourly internal cost rate and team knowledge. Furthermore, we find that balanced teams, which have at least one team member who qualifies as an industry specialist at both the senior rank and junior rank, produce higher‐quality audits than teams that have no specialists. In contrast, the audit quality of unbalanced teams, which have a specialist at the senior rank but not the junior rank or vice versa, is not statistically different than teams with no specialists. Overall, our evidence suggests that both the extent and distribution of industry knowledge within a team matter for audit production and that industry knowledge is utilized more effectively when it is spread throughout the team. The findings have useful implications for audit firms and regulators regarding how team composition and industry knowledge affect audit outcomes.