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矩阵分位数因子模型AI 解读

This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function with orthogonal rotation constraints. We show that the estimates converge at rate (min{p1p2,p2T,p1T})−1/2 in the average Frobenius norm, where p1, p2 and T are the row dimensionality, column dimensionality and length of the matrix sequence, respectively. This rate is faster than that…

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…

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…

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…

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…

STRAP：利用随机投影的多元同质性平滑检验AI 解读

In this paper, we propose STRAP (Smooth Tests using RAdom Projections), projected smooth tests for the equality of two multivariate distributions. We employ random projections to map the multivariate data into one-dimensional data and transform the projected data via the probability integral transformation. Under the null hypothesis of homogeneity, the transformed data follow the standard uniform distribution, reducing the problem to testing for uniformity. Building on…

近历元依赖重尾时间序列的极端局部线性分位数回归AI 解读

This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic properties of the local-linear…

动态网络形成的非参数推断：对“动态网络形成中的同质性与传递性”的评论AI 解读

1 IntroductionDano et al. (2026) develop a novel conditional logit approach to identify the structural parameters of a model of dynamic network formation. They propose a conditional maximum likelih...

面板双重差分估计的简单变换方法AI 解读

In the case of panel data with staggered interventions, we propose a simple time-series transformation that can be combined with various treatment effect estimators. The transformation is at the unit level, and simply requires computing the average outcome prior to an intervention, subtracting it from a post-treatment outcome, and then carefully selecting the control units in each time period. We show that under no anticipation and parallel trends assumptions, the cohort…

具有潜在分组结构的面板随机前沿模型AI 解读

Stochastic frontier models have attracted considerable attention due to the incorporation of an inefficiency term in addition to the conventional error term. In this paper, we propose a general estimation framework for panel stochastic frontier models that accommodates potential heterogeneity through latent group structures. The framework is tailored to the distinctive features of stochastic frontier models and is paired with a practical hybrid estimation procedure that…

一类半参数多项选择模型的闭式估计量AI 解读

We consider a semiparametric multinomial choice model that allows for an arbitrary joint distribution of choice specific unobservables that are independent of explanatory variables. This model permits relatively flexible substitution patterns between choices. To minimize computational difficulties, we restrict attention on estimators of the model that can be expressed in closed form. We combine and extend various results from the existing literature to enforce economic…

多层网络社区检测的矩整合与偏差调整谱方法AI 解读

To detect the global community structure of multi-layer networks, individual layers might not provide sufficient information. It is of vital importance to effectively complement the information from the network data. In this paper, under the framework of multi-layer stochastic block model, a Spectral method with Moments integration and Bias Adjustment (SpecMBA) is proposed for community detection. The key distinguishing feature of SpecMBA is the adaptive integration of both…

通过干扰平衡设计网络实验AI 解读

Randomized experiments are widely used to estimate the causal effects of treatment or intervention across various scientific fields. Nowadays, numerous experiments are conducted in network settings where the outcome of one unit depends not only on its own treatment but also on the treatments of other units within the network. This phenomenon, known as network interference, poses significant challenges for experimental design. Existing randomization methods often neglect…

时间维度较小时异质性面板分位数回归的 CCE 估计AI 解读

We consider a common correlated effects estimator for panel quantile regression models featuring heterogeneous slope coefficients and interactive fixed effects. Using convolution smoothing to facilitate the derivation of asymptotic properties, our method allows the number of time periods (T) to grow at a much slower rate than the number of cross-sectional units (N). We establish that the mean group estimator converges to a limiting normal distribution, which may have a…

利用特殊回归变量识别第一类与第二类错误概率：司法误判应用AI 解读

We nonparametrically identify Type I and Type II error rates when mistakes are unobserved. Our strategy reframes the setting as a misclassified binary choice model, using a special regressor to identify the misclassification probabilities. Identification requires an exclusion restriction and a large support condition. When the exclusion restriction is in doubt, we show how our estimands can be interpreted as bounds under a weaker monotonicity condition. Bounds are also…

利用双重机器学习估计直接与间接分位数处理效应AI 解读

We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of…

条件平均处理效应的估计AI 解读

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE parameter is nonparametrically…