60 papers
This paper introduces a modified conditional sum-of-squares (MCSS) estimator for ARFIMA models that corrects the bias caused by estimating a constant term, demonstrating through theoretical analysis and simulations that it significantly outperforms the standard CSS estimator even in small samples, and applying this improvement to reanalyze three classical economic and hydrological datasets.
This paper characterizes the revenue-maximizing auction for an income-generating asset with costly post-allocation verification, demonstrating that the optimal mechanism combines upfront cash payments with linear royalty contracts that feature bidder-specific caps beyond which auditing ceases.
This paper provides an analytical characterization of the Synthetic Control Method's degrees of freedom to derive estimable information criteria that improve model selection over cross-validation, particularly in settings with noisy donors and numerous candidates such as the Tianjin car license rationing case.
This paper introduces Gimbal Regression, a deterministic and geometry-aware local regression framework that ensures stable, auditable estimation of spatial heterogeneity by constructing directional weights from neighborhood geometry to mitigate numerical instability caused by anisotropic or low-dimensional data structures.
This paper proposes a novel operator-level ARCH framework for data in general separable Hilbert spaces that models the full evolution of the conditional covariance operator, establishing conditions for stationarity and deriving consistent estimators, which are validated through simulations and an application to high-frequency intraday returns.
The paper argues that frontier AI safety policies currently suffer from a structural "coordination gap" by overemphasizing prevention while neglecting the institutional capacity needed to coordinate responses when failures occur, and proposes adapting mechanisms from nuclear safety and pandemic preparedness to address this underinvestment in ecosystem robustness.
This paper proposes a semi-structural DSGE model for the Israeli economy incorporating household credit frictions to demonstrate that effective monetary policy should respond to credit spreads, while macroprudential policy can mitigate debt crises and stimulate demand by reducing leverage-induced borrowing costs.
This paper proposes a modified Double Machine Learning estimator featuring "Reverse Cross-Fitting" and a specialized calibration rule to enable robust causal inference in high-dimensional macroeconomic time series, demonstrating its effectiveness through simulations and an application to the dynamic effects of Tier 1 regulatory capital.
This paper models a designer's optimal strategy for restricting an experimenter's choice of information experiments to counteract persuasive incentives, demonstrating that when preferences are S-shaped, the best approach involves a "double censorship" mechanism that outperforms full delegation by balancing information provision with persuasion constraints.
This paper proposes a shrinkage regularization-based portmanteau test to detect both linear and nonlinear serial dependence in high-dimensional non-Gaussian time series, extending the method introduced by Jasiak and Neyazi (2023) to accommodate high-dimensional settings.
This paper establishes a unified theory showing that the minimax optimal estimation rate for functionals identified by "thin sets" (submanifolds of dimension in a -dimensional space) depends critically on the intrinsic dimensionality, specifically achieving a rate of for nonparametric functions with smoothness , and provides valid inference procedures via sieve Riesz representation and Sobol points.
This paper demonstrates that to minimize justified envy in serial dictatorship mechanisms, agents should be ordered according to a Kemeny ranking of their priorities when preferences are identical and uniformly distributed, or a weighted Kemeny ranking when these assumptions are relaxed.
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 game-theoretic model demonstrating that the availability of "whataboutism" as a rhetorical deflection strategy exacerbates offensive speech and can lead to the complete breakdown of civility norms, particularly within polarized societies.
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.