论文检索

矩不等式模型推断的实用指南AI 解读

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex, including multiple survey papers that document the non-standard features these models possess, the main novel concepts behind inference in these models, and the most recent developments…

区间识别集参数推断的功效性质AI 解读

This article studies the power properties of confidence intervals (CIs) for a partially-identified parameter of interest with an interval identified set. We assume that the researcher has bounds estimators needed to construct the CIs proposed by Imbens and Manski (2004), Stoye (2009), and Stoye (2020), denoted by C I α 1 $CI_{\alpha }^{1}$ upper C upper I Subscript alpha Superscript 1 , C I α 2 $CI_{\alpha }^{2}$ upper C upper I Subscript alpha Superscript 2 , C I α 3 $CI_{\alpha }^{3}$…

LASSO 及相关估计量的分布鲁棒预测误差AI 解读

We study the classical problem of predicting an outcome variable, Y, using a linear combination of a d-dimensional covariate vector, X. We are interested in linear predictors whose coefficients solve infβ∈Rd(EPn[|Y−X⊤β|r])1/r+δρ(β), where δ>0 is a regularization parameter, ρ:Rd→R+ is a convex penalty function, Pn is the empirical distribution of the data and r≥1. Our main contribution is a new bound on the out-of-distribution prediction error of such estimators. The new…

AR(1) 模型的局部投影残差自助法AI 解读

This article proposes a local projection (LP) residual bootstrap method to construct confidence intervals for impulse response coefficients of AR(1) models. Our bootstrap method is based on the LP approach and involves a residual bootstrap procedure applied to AR(1) models. We present theoretical results for our bootstrap method and proposed confidence intervals. First, we prove the uniform consistency of the LP-residual bootstrap over a large class of AR(1) models that…

不可微函数的后验分布AI 解读

This paper examines the asymptotic behavior of the posterior distribution of a possibly nondifferentiable function g(θ), where θ is a finite-dimensional parameter of either a parametric or semiparametric model. The main assumption is that the distribution of a suitable estimator θ̂n, its bootstrap approximation, and the Bayesian posterior for θ all agree asymptotically.