60 papers
This paper analyzes quality-based screening in digital monopolies, demonstrating that costless replication leads to a dual inefficiency where the monopolist underinvests in the highest quality while distributing degraded versions, a dynamic where competition worsens underinvestment but improves distributional outcomes.
This paper demonstrates that in multidimensional sequential screening, a monopolist can frontload surplus extraction to transform the problem, and under specific conditions of common first-order stochastic dominance and invariant dependencies, the optimal mechanism simplifies to independently offering sequential screening for each good, thereby rationalizing membership-plus-separate-sales business models.
This paper develops estimation and inference methods for a macroeconomic model with multiple behavioural equilibria and constant-gain learning, establishing the consistency and asymptotic normality of the nonlinear least squares estimator while addressing non-standard limit distributions that arise from repeated equilibrium solutions.
This paper introduces an algorithm capable of computing all evolutionarily stable strategies within nondegenerate normal-form games involving three or more players.
This paper proposes that absentmindedness, modeled as a form of bounded rationality where players cannot perfectly track game progression, serves as a novel source of bargaining power by enabling credible refusal of last-minute ultimatums, which forces preemptive concessions but ultimately leads to inefficient delays and rejected offers.
This paper demonstrates that while Bayesian learning from public information can simultaneously generate issue alignment and persistent polarization on individual dimensions, it cannot support stronger forms of polarization requiring divergence beyond marginal beliefs, with aggregate divergence ultimately depending on cross-issue separability.
This paper argues that while many common causal estimands in network settings fail to simultaneously offer interpretable summaries of unit-level effects and support optimal policy choice due to the mismatch between homogeneous exposure assumptions and heterogeneous realities, the expected average outcome serves as a superior estimand that fulfills both criteria.
This study develops a sequential team production model to demonstrate that optimal AI deployment involves stochastically replacing workers at the beginning and end of a workflow while preserving middle workers to maintain peer monitoring, a strategy that may intentionally underutilize AI capacity but ultimately increases average wages and reduces intra-team inequality.
This paper critiques the over-reliance on and misinterpretation of statistical significance in choice modelling, advocating for more precise reporting of uncertainty measures and a greater emphasis on behavioural and policy significance alongside statistical findings.
This paper demonstrates that random utility rationality with aggregation is substantially weaker than standard aggregated random utility models (ARUM), identifying specific conditions for their equivalence and showing that violating these conditions leads to significant estimation bias.
This paper addresses the challenges of Structural Vector Autoregressions (SVARs) with locally but not globally identified parameters by characterizing the resulting observationally equivalent points and proposing novel algorithms for exhaustive parameter computation and robust Bayesian and frequentist inference on multi-modal impulse responses.
This paper introduces a notion of robustness for belief-invariant Bayes correlated equilibria in incomplete information games and establishes that a generalized potential function provides a sufficient condition for such robustness, implying that in supermodular potential games, robust equilibria coincide with Bayes Nash equilibria.
This paper develops a theoretical framework demonstrating that optimal pricing for Large Language Models reduces to one-dimensional screening via committed-spend token budgets priced at marginal cost, a mechanism that aligns with observed industry practices and adapts to competitive dynamics between proprietary and open-source models.
This study introduces DisSim-FinBERT, a novel framework that combines discourse simplification with aspect-based sentiment analysis to improve the extraction of core messages and sentiment accuracy from complex financial documents like FOMC minutes.
This paper proposes a class of structural vector autoregressions with breaks (SVAR-WB) that incorporates cross-regime constraints to improve identification, derives conditions for point and set identification, and develops robust Bayesian and frequentist inference methods to address the unreliability of standard approaches caused by multiple observationally equivalent structural parameters.
This paper addresses the failure of point identification in heteroskedastic Structural Vector Autoregressions (SVARs) caused by eigenvalue multiplicity during variance breaks by deriving identified sets for impulse responses using zero or sign restrictions and applying robust Bayesian inference, illustrated through an empirical analysis of the global crude oil market.
This paper analyzes a public good game involving N citizens and a Governor who balances material gains from embezzlement against belief-dependent reputational concerns, fully characterizing the resulting equilibria to demonstrate how penalties, audits, and psychological incentives jointly determine the levels of citizen contribution and public good provision.
This paper refutes the sufficiency of the rank condition proposed by Rubio-Ramirez, Waggoner, and Zha (2010) for global identification in structural vector autoregressions by demonstrating that it fails to account for redundant restrictions, and subsequently derives a corrected necessary and sufficient condition.
This paper introduces a unified Bayesian framework using indicator-saturated regression and a spike-and-slab prior with an inverse-moment density to consistently detect structural breaks in longitudinal data, demonstrating superior performance over frequentist methods and successfully applying the approach to evaluate climate policies in the European road transport sector.
This paper addresses the ambiguity surrounding the existence of maximum likelihood estimates in generalized linear models by establishing conditions for their validity, demonstrating that consistent estimates may still be obtained for certain parameters even when these conditions fail, and providing methods to verify these conditions in high-dimensional settings.