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非协整函数系数回归的极限理论与推断AI 解读

Functional coefficient (FC) cointegrating regressions offer empirical investigators flexibility in modeling economic relationships by introducing covariates that influence the direction and intensity of comovement among nonstationary time series. FC regression models are also useful when formal cointegration is absent, in the sense that the equation errors may themselves be nonstationary, but where the nonstationary series display well-defined FC linkages that can be…

协变量自适应随机化下多回归量的调整AI 解读

Our paper discovers a new trade-off of using regression adjustments (RAs) in causal inference under covariate-adaptive randomizations (CARs). On one hand, RAs can improve the efficiency of causal estimators by incorporating information from covariates that are not used in the randomization. On the other hand, RAs can degrade estimation efficiency due to their estimation errors, which are not asymptotically negligible when the number of regressors is of the same order as the…

稀疏高维加性模型的估计与一致推断AI 解读

We develop a novel method to construct uniformly valid confidence bands for a nonparametric component f1 in the sparse additive model Y=f1(X1)+…+fp(Xp)+ɛ in a high-dimensional setting. Our method integrates sieve estimation into a high-dimensional Z-estimation framework, facilitating the construction of uniformly valid confidence bands for the target component f1. To form these confidence bands, we employ a multiplier bootstrap procedure. Additionally, we provide rates for…

高维非线性模型基于自助法的渐近改进AI 解读

We consider penalized extremum estimation of a high-dimensional, possibly nonlinear model that is sparse in the sense that most of its parameters are zero but some are not. We use the SCAD penalty function, which provides model selection consistent and oracle efficient estimates under suitable conditions. However, asymptotic approximations based on the oracle model can be inaccurate with the sample sizes found in many applications. This paper gives conditions under which the…

跨时区股票收益的大型验证性动态因子模型AI 解读

We propose a confirmatory dynamic factor model for a large number of stocks whose returns are observed daily across multiple time zones. The model has a global factor and a continental factor that both drive the individual stock return series. We propose two estimators of the model: a quasi-maximum likelihood estimator (QML-just-identified), and an improved estimator based on an Expectation Maximization (EM) algorithm (QML-all-res). Our estimators are consistent and…

通过张量因子建模进行张量时间序列缺失值填补AI 解读

We propose tensor time series imputation when the missing pattern in the tensor data can be general, as long as any two data positions along a tensor fibre are both observed for enough time points. The method is based on a tensor time series factor model with Tucker decomposition of the common component. One distinguished feature of the tensor time series factor model used is that there can be weak factors in the factor loading matrix for each mode. This reflects reality…

仿射模型中尖锐识别集的简易子向量推断AI 解读

This paper studies a regularized support function estimator for bounds on components of the parameter vector in the case in which the identified set is a polygon. The proposed regularized estimator has three important properties: (i) it has a uniform asymptotic Gaussian limit in the presence of flat faces in the absence of redundant (or overidentifying) constraints (or vice versa); (ii) the bias from regularization does not enter the first-order limiting distribution; (iii)…

具有时变交互固定效应的时变面板数据模型AI 解读

This paper introduces a time-varying (TV) panel data model with interactive fixed effects where both the coefficients and factor loadings are allowed to change smoothly over time. We propose a local version of the least squares and principal component method to estimate the TV coefficients, TV factor loadings, and common factors simultaneously. We provide a bias-corrected local least squares estimator for the TV coefficients and establish the limiting distributions and…

具有多层交互固定效应的三维异质面板数据模型AI 解读

We consider a three-dimensional (3D) panel data model with heterogeneous slope coefficients and multi-level interactive fixed effects consisting of latent global factors and two types of local factors. Our model nests many commonly used 3D panel data models. We propose an iterative estimation procedure that relies on initial consistent estimators obtained through a novel defactored approach. We study the asymptotic properties of our estimators and show that our iterative…

流式数据平滑分位数回归的统计推断AI 解读

In this paper, we tackle the problem of conducting valid statistical inference for quantile regression with streaming data. The main difficulties are that the quantile regression loss function is non-smooth and it is often infeasible to store the entire dataset in memory, rendering traditional methodologies ineffective. We introduce a fully online updating method for statistical inference in smoothed quantile regression with streaming data to overcome these issues. Our main…

区间分位数相关及高维分位数效应检验AI 解读

In this article, we propose interval quantile correlation and interval quantile partial correlation to measure the association between two random variables over an interval of quantile levels. We construct efficient estimators for the proposed correlations, and establish their asymptotic properties under the null and alternative hypotheses. We further use the interval quantile partial correlation to test for the significance of covariate effects in high-dimensional quantile…

使用非线性筛方法推断时间序列非参数条件矩限制AI 解读

This paper studies estimation of and inference on dynamic nonparametric conditional moment restrictions of high dimensional variables for weakly dependent data, where the unknown functions of endogenous variables can be approximated via nonlinear sieves such as neural networks and Gaussian radial bases. The true unknown functions and their sieve approximations are allowed to be in general weighted function spaces with unbounded supports, which is important for time series…

多维索引数据的经验似然AI 解读

This paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likelihood statistic that converges to a chi-square distribution under the non-degenerate case, where corresponding Hoeffding type decomposition is dominated by linear terms. Our methodology is related to the notion of jackknife empirical likelihood but the leave-out pseudo values are constructed by leaving…

协变量自适应随机化下的分位数异质处理效应检验AI 解读

In economic studies and clinical trials , it is prevalent to observe heterogeneous treatment effects that vary depending on the relative locations of units in the distribution of responses. In this study, we propose using quantile regression to estimate and conduct inference for conditional quantile treatment effects (cQTEs) in covariate-adaptive randomized experiments. First, we present sufficient conditions for consistently estimating the cQTEs, concerning the bias due to…

基于随机森林的分位数控制法AI 解读

This paper studies robust inference procedure for treatment effects in panel data with flexible relationship across units via the random forest method. The key contribution of this paper is twofold. First, we propose a direct construction of prediction intervals for the treatment effect by exploiting the information of the joint distribution of the cross-sectional units using random forest. In particular, we propose a Quantile Control Method (QCM) using the Quantile Random…

通过最小二乘实现流数据序贯分位数回归AI 解读

Massive stream data are common in modern economics applications, such as e-commerce and finance. They cannot be permanently stored due to storage limitation, and real-time analysis needs to be updated frequently as new data become available. In this paper, we develop a sequential algorithm, SQR, to support efficient quantile regression (QR) analysis for stream data. Due to the non-smoothness of the check loss, popular gradient-based methods do not directly apply. Our…

千万级观测下分位数回归的快速推断AI 解读

Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometric methods based on extreme estimators require large amounts of computing resources and memory, which are often not readily available. In this paper, we focus on linear quantile regression applied to “ultra-large” datasets, such as U.S. decennial censuses. A fast inference framework is presented,…

有限聚类数量下分位数过程的推断AI 解读

I introduce a generic method for inference on entire quantile and regression quantile processes in the presence of a finite number of large and arbitrarily heterogeneous clusters. The method asymptotically controls size by generating statistics that exhibit enough distributional symmetry such that randomization tests can be applied. The randomization test does not require ex-ante matching of clusters, is free of user-chosen parameters, and performs well at conventional…

通过条件分位数回归估计无条件分位数偏效应AI 解读

This paper develops a semi-parametric procedure for estimation of unconditional quantile partial effects using quantile regression coefficients. The estimator is based on an identification result showing that, for continuous covariates, unconditional quantile effects are a weighted average of conditional ones at particular quantile levels that depend on the covariates. We propose a two-step estimator for the unconditional effects where in the first step one estimates a…

高维环境下的分布反事实分析AI 解读

In the context of treatment effect estimation, this paper proposes a new methodology to recover the counterfactual distribution when there is a single (or a few) treated unit and possibly a high-dimensional number of potential controls observed in a panel structure. The methodology accommodates, albeit does not require, the number of units to be larger than the number of time periods (high-dimensional setup). As opposed to model only the conditional mean, we propose to model…

异质节点网络向量自回归的联合估计与分组识别AI 解读

Individuals or companies in a large social or financial network often display rather heterogeneous behaviors for various reasons. In this work, we propose a network vector autoregressive model with a latent group structure to model heterogeneous dynamic patterns observed from network nodes, for which group-wise network effects and time-invariant fixed-effects can be naturally incorporated. In our framework, the model parameters and network node memberships can be…

边际均值效应与边际分位数效应估计的半参数方法AI 解读

We consider a semiparametric generalized linear model and study estimation of both marginal mean effects and marginal quantile effects in this model. We propose an approximate maximum likelihood estimator, and rigorously establish the consistency, the asymptotic normality, and the semiparametric efficiency of our method in both the marginal mean effect and the marginal quantile effect estimation. Simulation studies are conducted to illustrate the finite sample performance,…

最低工资与贫困：动态双重差分估计的新证据AI 解读

Abstract This study re-examines Dube (2019), which finds large and statistically significant poverty-reducing effects of the minimum wage. We show that his estimated elasticities are fragile and sensitive to (1) time period under study, (2) choice of macroeconomic controls, (3) limiting counterfactuals to geographically proximate states (“close controls”), which poorly match treatment states' pre-treatment poverty trends, and (4) accounting for potential bias caused by…

非参数时变工具变量结构 VAR：估计与推断AI 解读

Abstract This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even…