團隊

In this paper, we characterize a forecasting model where forecasters cannot perfectly distinguish between the two persistent components (trends and cycles) in a dynamic setting. In this model, forecasters jointly update their beliefs about the two components: noisy information about one component is used to update beliefs about the other component. We present diagnostic empirical facts on forecasting behaviors and show that these facts are consistent with our model’s predictions while contradicting those of existing models in the expectation formation literature. To validate our model, we exploit the Federal Reserve’s 2012 adoption of explicit inflation targeting as a policy shock. Structural estimation reveals that this policy change altered the underlying data-generation process, and the corresponding changes in forecasting behavior indeed align with our model’s predictions. Finally, we revisit the standard Forecast Error-Forecast Revision regression approach in this literature. We examine its robustness within our enriched framework and reveal that trend-cycle confusion can interact with behavioral bias and generate horizon-dependent overreaction patterns documented in empirical studies.

Utilizing 1.54 million judicial judgments from enterprise-to-enterprise litigation between 2014 and 2019 in China, we provide evidence of municipal leaders exerting influence over the courts to favor enterprises connected to them. By leveraging variations in enterprise connections resulting from official turnover, we show that enterprises with connections to party leaders have higher chances of winning in business litigation than unconnected enterprises. We also examine the impact of the staggered roll-out of circuit courts, a top-down institutional reform, on cronyism in the courtroom. Our findings show that this reform has effectively reduced the judicial advantage enjoyed by connected enterprises by two-thirds. By contrast, the trial live-broadcasting reform increases visibility but is not associated with a reduction in the effect of political connections, suggesting that different forms of judicial bias require different monitoring approaches.

Our study analyses 6 million civil judgments in China from 2014 to 2018, documenting gender disparities that disfavour female litigants. We investigate the impact of an open justice reform that mandated courts to broadcast legal proceedings live on a centralized online platform. By exploiting variations in its implementation across courts and over time and employing both difference-in-differences and Bartik IV approaches, we find that gender disparities in chances of winning decrease as broadcast intensity increases. Analysis of the textual content of judicial decisions provides further evidence that these changes in judicial outcomes stem from altered judge behaviours (i.e. attention and effort) under enhanced judicial transparency. Our results demonstrate how information technology shapes judges’ conduct, underscoring its broader potential to improve accountability in public institutions.

Using firm-level earnings forecasts and managerial guidance data, we construct guidance surprises for analysts, i.e., differences between managerial guidance and analysts' initial forecasts. We document new evidence on expectation formation: (i) analysts overreact to managerial guidance and the overreaction is state-dependent, i.e., it is stronger for negative guidance surprises but weaker for surprises that are larger in size; and (ii) forecast revisions are neither symmetric in guidance surprises nor monotonic. We organize these facts with a model where analysts are uncertain about the quality of managerial guidance. We show that a reasonable degree of ambiguity aversion is necessary to account for the documented heterogeneous overreaction pattern.