47 papers
This paper develops a nonlinear theoretical framework to analyze public expenditure reform in Uruguay, demonstrating that structural adjustments involving institutional frictions and transition costs initially increase total spending before converging to a more efficient long-run allocation, thereby producing a J-shaped expenditure trajectory.
This paper proposes a tractable identification approach for strategic network formation models with unobserved heterogeneity by employing a "bounding-by-" technique that utilizes monotonicity restrictions on subnetwork configurations to derive identifying restrictions and informative bounds on structural parameters without requiring a closed-form equilibrium solution.
This paper proposes a novel two-phase online learning framework for semiparametric monotone index models that enables real-time, rate-optimal estimation and inference without storing historical data, addressing the computational limitations of traditional batch methods in large-scale streaming economic applications.
This paper proposes a new identification strategy for average treatment effects on the treated in panel data with discrete outcomes, utilizing a transition independence assumption and a latent-type Markov structure to overcome the limitations of standard difference-in-differences methods while delivering distinct estimates in empirical applications.
This paper proposes an "at-risk" transformation that binarizes macroeconomic predictors to capture the discrete nature of recessions, demonstrating that this simple approach significantly improves out-of-sample forecasting performance and makes linear models competitive with machine learning methods, particularly around the onset of U.S. recessions.
This paper establishes a unified framework for counterfactual analysis in incomplete models by proving the isomorphism between identification and counterfactual tasks, extending support-function methods to handle moment closures under minimal conditions, and demonstrating that irreducible models render identified sets and their moment closures statistically indistinguishable.
This paper introduces ForeComp, an R package designed to compare predictive accuracy using Diebold-Mariano tests with both standard and fixed-smoothing asymptotics, featuring visual diagnostics for bandwidth sensitivity and demonstrating its utility through professional forecasting applications and Monte Carlo simulations.
This paper establishes general and sharp convergence rates for induced order statistics under weak smoothness assumptions, enabling their application to both interior and boundary points in settings like regression discontinuity designs while clarifying the trade-offs between smoothness and convergence speed.
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 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 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 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 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 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.
This paper develops a non-parametric sequential framework using e-values and e-processes to perform anytime-valid comparative backtests for general elicitable risk measures, offering robust performance under dependence and misspecification while introducing a modified three-zone approach for more informative model evaluation.
This paper introduces Temporal Hierarchy Forecasting (THieF) as a computationally efficient method that significantly improves day-ahead electricity price prediction accuracy across diverse models and markets by reconciling forecasts for hourly and block products.