Results 1 to 10 of about 628 (47)
Characterizing correlation matrices that admit a clustered factor representation [PDF]
, 2023 The Clustered Factor (CF) model is commonly used to parametrize block correlation matrices. We show that the CF model imposes additional superfluous restrictions.
Hansen, Peter Reinhard, Tong, Chen
core +2 more sources
Network and panel quantile effects via distribution regression [PDF]
, 2018 This paper provides a method to construct simultaneous con fidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome ...
Chernozhukov, Victor +2 more
core +1 more source
Inference on a Distribution from Noisy Draws [PDF]
, 2019 We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models
Jochmans, Koen, Weidner, Martin
core +4 more sources
Treatment Choice, Mean Square Regret and Partial Identification
, 2023 We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finite-sample analysis on mean square regret, a decision criterion advocated by ...
Kitagawa, Toru, Lee, Sokbae, Qiu, Chen
core
Treatment Choice with Nonlinear Regret
, 2022 The literature on treatment choice focuses on the mean of welfare regret. Ignoring other features of the regret distribution, however, can lead to an undesirable rule that suffers from a high chance of welfare loss due to sampling uncertainty. We propose
Kitagawa, Toru, Lee, Sokbae, Qiu, Chen
core
Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!
, 2022 Vectorautogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting.
Gruber, Luis, Kastner, Gregor
core
Narrative Restrictions and Proxies: Rejoinder [PDF]
, 2022 This rejoinder addresses the discussants’ specific comments on the article “Narrative Restrictions and Proxies” (Section 2) as well as more general comments on the approach to robust Bayesian inference that we have proposed in previous work (Section 1)
Giacomini, Raffaella +2 more
core
Doubly Robust Estimators with Weak Overlap
, 2023 In this paper, we derive a new class of doubly robust estimators for treatment effect estimands that is also robust against weak covariate overlap. Our proposed estimator relies on trimming observations with extreme propensity scores and uses a bias ...
Man, Yukun +3 more
core
Bootstrap Inference on Partially Linear Binary Choice Model
, 2023 The partially linear binary choice model can be used for estimating structural equations where nonlinearity may appear due to diminishing marginal returns, different life cycle regimes, or hectic physical phenomena. The inference procedure for this model
Gao, Wenzheng, Sun, Zhenting
core
nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference [PDF]
, 2019 Nonparametric kernel density and local polynomial regression estimators are very popular in statistics, economics, and many other disciplines. They are routinely employed in applied work, either as part of the main empirical analysis or as a preliminary ...
Calonico, Sebastian +2 more
core +2 more sources