每日简报

空间动态面板的再中心化矩估计量AI 解读

This paper proposes a recentered method of moments (RMM) approach for estimating spatial dynamic panel data (SDPD) models with both individual and time fixed effects. This approach directly exploits the correlation of regressors and disturbances, where heteroskedasticity may exist across units and/or over time. It is computationally tractable and can handle situations where other methods are not applicable. We show that the proposed estimator is consistent and asymptotically…

用户指定异方差参数模型下的回归有效估计AI 解读

We show that it is possible to estimate regression coefficients at least as precisely as Ordinary Least Squares (OLS), Weighted Least Squares (WLS) and its recently proposed refinements, regardless of the user’s parametric model for heteroskedasticity. The key is to modify WLS depending on scalar target parameters, e.g., coefficients, marginal effects, predictions, etc. to obtain a targeted WLS that maximizes precision of estimation of the target given the user’s model for…

高维异质数据的无模型分布稳健特征筛选与错误发现控制AI 解读

In this paper, we propose a model-free feature screening framework tailored for high-dimensional and heterogeneous datasets, based on a novel distributionally robust dependence measure termed Copula Divergence. The proposed screening method, named CD-Screen, addresses critical limitations of existing feature screening methods, such as restrictive modeling assumptions and sensitivity to heterogeneous feature distributions. CD-Screen ranks features according to their Copula…

线性因子模型的 Alpha 检验：投资组合方法AI 解读

This paper develops a test for evaluating linear factor models in high-dimensional settings. The proposed Maximum Random Sparse Portfolio (MRSP) test generates L random sparse portfolios, each obtained through K random draws, and then selects the hardest-to-price portfolio to assess factor model performance. By evaluating multiple randomly generated sparse portfolios, the MRSP test retains a portfolio-based economic interpretation while avoiding reliance on a single…

基于正态混合准极大似然的双重自回归模型稳健估计AI 解读

This paper investigates the estimation of the double autoregressive (DAR) model in the presence of skewed and heavy-tailed innovations. We propose a novel Normal Mixture Quasi-Maximum Likelihood Estimation (NM-QMLE) method to address the limitations of conventional quasi-maximum likelihood estimation (QMLE) under non-Gaussian conditions. By incorporating a normal mixture distribution into the quasi-likelihood framework, NM-QMLE effectively captures both heavy-tailed behavior…

多重门槛回归的 Lasso 后推断及收益可预测性应用AI 解读

This article revisits the multiple threshold regression model, wherein coefficient parameters shift across regimes in accordance with the value of a threshold variable. We focus on developing a post-Lasso inference framework to resolve the selection uncertainty—particularly pronounced when threshold effects are weak—intrinsic to the group Lasso-based procedure that jointly estimates the number and locations of multiple thresholds. This objective is accomplished by…

存在少报的有序选择模型中的变化中的变化AI 解读

We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for…

函数型时间序列的近似因子模型AI 解读

We propose a novel approximate factor model tailored for analyzing time-dependent curve data. Our model decomposes such data into two distinct components: a low-dimensional predictable factor component and an unpredictable error term. These components are identified through the autocovariance structure of the underlying functional time series. The model parameters are consistently estimated using the eigencomponents of a cumulative autocovariance operator and an information…

高维干扰参数下分位数回归高维效应的去相关检验AI 解读

Quantile regression provides a flexible framework in econometrics for modeling the relationship between a response variable and multivariate predictors, particularly when heterogeneous effects are present. This paper addresses the challenge of testing high-dimensional coefficients in quantile regression in the presence of high-dimensional nuisance parameters. We first extend a recently proposed score test by incorporating a convolution-type smoothed quantile loss, which…

一族相干风险度量：在险价值的无限加权平均AI 解读

Value-at-Risk (VaR) remains one of the most widely used risk measures in finance due to its simplicity, interpretability, and regulatory prominence, particularly within the Basel framework. However, VaR is not a coherent risk measure, as it generally fails to satisfy the subadditivity property. This paper demonstrates that, although finite weighted combinations of VaR-type functionals do not necessarily yield coherent risk measures, appropriately constructed infinite…

利用大时间维度面板数据估计未观测的横截面网络连接AI 解读

We propose a simple three-step method to estimate the cross-sectional interdependence in a customary network econometric model with an unobserved network and a large T. First, we estimate the reduced-form model to construct instruments for the endogenous peer outcomes. Second, we use two-stage least squares to estimate the direct and contextual effects of the exogenous regressors, as well as the endogenous peer effect. Third, we estimate the network weight matrix. Monte…

面板MIDAS模型非线性最小二乘估计量的渐近分布AI 解读

The Mixed Data Sampling (MIDAS) approach provides a flexible framework for incorporating mixed-frequency data into econometric models. In this paper, we introduce the MIDAS framework into a panel data model and derive the asymptotic properties of the NLS estimator under a joint asymptotic framework where both N and T tend to infinity, allowing for heteroskedasticity and arbitrary correlation within each individual. To facilitate valid inference, we construct robust…

从旋转不变性到标量不变性：提升得分驱动因子模型的可识别性AI 解读

We show that, for a certain class of scaling matrices including the inverse square-root of the conditional Fisher information, score-driven factor models are identifiable up to a multiplicative scalar constant under very mild restrictions. This result has no analogue in parameter-driven models, as it exploits the different structure of the score-driven factor dynamics. Consequently, score-driven factor models overcome the issue of rotational invariance that typically affects…

非线性模型中缺失协变量的插补AI 解读

ABSTRACT I propose a new imputation estimator for missing covariate values in nonlinear models. The estimator provides efficiency gains relative to just using the complete cases, and is consistent for any model that falls under M‐estimation. This is unlike the commonly used dummy variable method and regression imputation, which I show to be generally inconsistent in nonlinear models. The proposed estimator is straightforward to implement, and relies only on the commonly used…

断点差分设计中的分位数处理效应AI 解读

Summary This paper studies identification and doubly robust (DR) estimation of quantile treatment effects (QTEs) in difference-in-discontinuities (diff-in-disc) designs when a new treatment is introduced in the post period and a confounding discontinuous policy is present in both periods. We show that QTEs are point identified under a conditional stable-distributional-effect assumption for the confounding treatment, a restriction that is analogous to the distributional…

Summary We consider likelihood-ratio-type statistics for testing restrictions on parameters built from a convex M-estimation criterion. In general, these statistics are not asymptotically pivotal. We propose a general simple non-parametric bootstrap procedure that yields asymptotically valid critical values and p-values. The method modifies the bootstrap objective function to mimic what happens under the null hypothesis. A Monte Carlo study illustrates that our method…

存在干扰参数时矩约束的统一推断AI 解读

This article proposes a simple unified inference approach on moment restrictions in the presence of nuisance parameters. The proposed test is constructed based on a new characterization that avoids the estimation of nuisance parameters and can be broadly applied across diverse settings. Under suitable conditions, the test is shown to be asymptotically size controlled and consistent for both independent and dependent samples. Monte Carlo simulations show that the test…

时变向量自回归的模型平均AI 解读

This paper proposes a novel time-varying model averaging (TVMA) approach to enhancing forecast accuracy for multivariate time series subject to structural changes. The TVMA method averages predictions from a set of time-varying vector autoregressive models using optimal time-varying combination weights selected by minimizing a penalized local criterion. This allows the relative importance of different models to adaptively evolve over time in response to structural shifts. We…

基于图的多样本比较及多类别响应的特征选择AI 解读

This article proposes a graph-based maximum pairwise difference (MPD) test to compare K-sample distributions. This test can be utilized to identify the most informative variables contributing to the heterogeneity of multiple multivariate distributions. To construct the test, we first pool observations from multiple samples to create a similarity graph based on the distances between any two observations. For each observation, we next evaluate its connection probability to…

高维协变量下局部平均处理效应的识别稳健推断AI 解读

This paper presents an inference method for the local average treatment effect (LATE) in the presence of high-dimensional covariates, regardless of the strength of identification. We propose an orthogonalized Anderson-Rubin test statistic that maintains uniformly valid asymptotic size. We provide an easy-to-implement algorithm for inferring the high-dimensional LATE by inverting our test statistic and employing the double/debiased machine learning method. Simulation results…

检验文献集合中的统计功效不足AI 解读

How many experimental studies would have come to different conclusions had they been run on larger samples? I show how to estimate the expected number of statistically significant results that a set of experiments would have reported had their sample sizes all been counterfactually increased. The proposed deconvolution estimator is asymptotically normal and adjusts for publication bias. Unlike related methods, this approach requires no assumptions of any kind about the…

空间回归中的系数稳定性检验AI 解读

This paper develops a test for coefficient variability in spatial regressions. The test is designed to have good power for a wide range of persistent patterns of coefficient variation, be applicable in a wide range of spatial designs, and accommodate spatial correlation in regressors and regression errors. The test approximates the locally best invariant test for coefficient stability in a Gaussian regression model with martingale-like coefficient variation under the…

稀疏网络的拟合优度检验AI 解读

The stochastic block model (SBM) has been widely used to analyze network data. Various goodness-of-fit tests have been proposed to assess the adequacy of model structures. To the best of our knowledge, however, none of the existing approaches are applicable for sparse networks in which the connection probability of any two communities is of order n−1logn, and the number of communities is divergent. To fill this gap, we propose a novel goodness-of-fit test for the stochastic…

识别失败与模型设定正确性的协调检验AI 解读

We develop a specification test for GMM and Minimum Distance (MD) inference that is: (i) robust to weak identification; (ii) easy to implement and broadly applicable; and (iii) has meaningful power. Our specification test is based on a conditional inference approach: information about the existence of strongly identified directions in the parameter space is exploited to define a data-dependent critical value, which, in turn, increases the power of the resulting specification…