This paper analyzes the properties of standard estimators, tests, and confidence sets (CS's) for parameters that are unidentified or weakly identified in some parts of the parameter space. The paper also introduces methods to make the tests and CS's robust to such identification problems. The results apply to a class of extremum estimators and corresponding tests and CS's that are based on criterion functions that satisfy certain asymptotic stochastic quadratic expansions and that depend on the parameter that determines the strength of identification. This covers a class of models estimated using maximum likelihood (ML), least squares (LS), quantile, generalized method of moments, generalized empirical likelihood, minimum distance, and semi-parametric estimators. The consistency/lack-of-consistency and asymptotic distributions of the estimators are established under a full range of drifting sequences of true distributions. The asymptotic sizes (in a uniform sense) of standard and identification-robust tests and CS's are established. The results are applied to the ARMA(1, 1) time series model estimated by ML and to the nonlinear regression model estimated by LS. In companion papers, the results are applied to a number of other models.

In large-scale panel data models with latent factors the number of factors and their loadings may change over time. Treating the break date as unknown, this article proposes an adaptive group-LASSO estimator that consistently determines the numbers of pre- and post-break factors and the stability of factor loadings if the number of factors is constant. We develop a cross-validation procedure to fine-tune the data-dependent LASSO penalties and show that after the number of factors has been determined, a conventional least-squares approach can be used to estimate the break date consistently. The method performs well in Monte Carlo simulations. In an empirical application, we study the change in factor loadings and the emergence of new factors in a panel of U.S. macroeconomic and financial time series during the Great Recession.

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including (time‐varying) rare‐disaster risk models and long‐run risk models. Building on recent developments in the conditional inference literature, we provide a novel conditional specification test by simulating the critical value conditional on a sufficient statistic. This sufficient statistic can be intuitively interpreted as a measure capturing the macroeconomic information decoupled from the underlying content of asset pricing theories. Macro‐finance decoupling is an effective way to improve the power of the specification test when asset pricing theories are difficult to refute because of a severe imbalance in the information content about the key model parameters between macroeconomic moment restrictions and asset pricing cross‐equation restrictions. We apply the proposed conditional specification test to the evaluation of a time‐varying rare‐disaster risk model and the construction of robust model uncertainty sets.

This paper examines the role of restrictive covenants in convertible bonds. After controlling for standard covenant intensity determinants, an average convertible bond offering has 3.21 fewer covenants than an average straight bond offering. While covenants negatively affect straight bond yields, there is no negative association between covenants and convertible bond yields. Moreover, contrary to straight bond covenants, convertible bond covenants are set largely independently of issuer characteristics. Overall, our findings suggest that the conversion option and certain covenants are substitutes for addressing debt-related financing costs. The few covenants included in convertibles represent irrelevant boilerplate clauses.

This study examines the relationship between government subsidies and income smoothing using a sample of US‐listed firms. We find that subsidized firms smooth their earnings more aggressively than their unsubsidized peers. This finding is consistent with the reasoning that subsidized firms bear higher political costs and have more incentives to smooth earnings to avoid public attention. In addition, smoothing by subsidized firms is more pronounced when the subsidies are granted through non‐tax‐related channels than through tax‐based channels, and the positive association between government subsidies and income smoothing is stronger for firms under higher public scrutiny and with less transparent information environments. Further analysis shows that smoothing by subsidized firms serves mainly to obfuscate earnings and that subsidized firms that smooth earnings tend to continue receiving subsidies in the future. Overall, our results help explain the role of government subsidies in shaping firms' accounting and disclosure choices.

The study investigates the impact of collective empathy on corporate green innovation from the perspective of war heritage. Drawing on stakeholder theory and empathy theory, we argue that collective empathy fosters corporate green innovation by generating public emotional empathy toward local descendants of war victims and enhance cognitive empathy within firms regarding the environmental needs of local communities. Analyzing data from Chinese-listed firms in heavily polluting industries between 2010 and 2019, we find that collective empathy significantly encourages green innovation efforts. Mechanism analyses indicate that collective emotional and cognitive empathy serve as key pathways in this relationship. Furthermore, state-owned ownership and formal institutions reinforce and complement the positive effect of collective empathy on corporate green innovation.

In this study, we explore the impact of the establishment of environmental courts, an environmental justice system, on the cost of equity capital. Based on a quasi-natural experiment of establishing environmental courts in China, we find that they have a deterrent effect and reduce the cost of equity capital for heavily polluting firms in localities. We also find that a low proportion of managerial ownership, a low level of analyst attention, and high environmental uncertainty induce this deterrent effect. Furthermore, mechanism tests indicate that environmental courts enhance corporate environmental engagement and corporate ESG ratings, increase long-term institutional investor ownership, and reduce urban environmental violations to achieve a deterrent effect. The findings are more pronounced for the trial court sample, firms in cities with lower public participation in environmental protection, and firms with lower environmental information transparency. We also suggest the impacts and underlying mechanisms of the environmental justice system on the equity capital market, which is conducive to long-term planning by firm managers.