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具有动态异质溢出的矩阵值空间自回归AI 解读

We introduce a new time-varying parameter spatial matrix autoregressive model that integrates matrix-valued time series, heterogeneous spillover effects, outlier robustness, and time-varying parameters in one unified framework. The model allows for separate dynamic spatial spillover effects across both the row and column dimensions of the matrix-valued observations. Robustness can be introduced via a matrix Student’s t distribution. The model can still be estimated…

基于离散傅里叶变换检验交互固定效应面板模型的结构变化AI 解读

We propose a discrete Fourier transform (DFT)-based test for detecting structural changes in panel data models with interactive fixed effects (IFE). When structural changes are present but ignored, the conventional asymptotic distribution of the least squares (LS) estimator of Bai (2009) may no longer provide reliable inference, and the resulting residuals contain signals of parameter instability. Our test exploits this feature by comparing the DFT of weighted residuals to…

通过模型平均实现多层网络迁移学习的框架AI 解读

Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting their practicality. To address these issues, we propose a novel transfer learning framework for multilayer networks using a bi-level model averaging method. A $K$-fold cross-validation…

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异质性面板中的个体平均估计AI 解读

In this work, we introduce a unit averaging procedure to efficiently recover unit-specific parameters in a heterogeneous panel model. The procedure consists in estimating the parameter of a given unit using a weighted average of all the unit-specific parameter estimators in the panel. The weights of the average are determined by minimizing an MSE criterion we derive. We analyze the properties of the resulting minimum MSE unit averaging estimator in a local heterogeneity…

排序数据的静态与动态贝叶斯加性回归树模型AI 解读

Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with latent scores defined as…

半参数广义帕累托模型中的尾部期望分位数估计AI 解读

Expectiles have received increasing attention as a market risk measure that is both coherent and elicitable. They are defined as a least squares analog to quantiles. Their estimation from heavy-tailed loss data in an extreme value framework is an important and difficult problem, especially when the target tail expectile is beyond the range of the data. This problem has been studied only fairly recently, using solely the Weissman extrapolation method. The competing…

一个精确 t 检验AI 解读

Multivariate linear regression and randomization-based inference are two essential methods in statistics and econometrics. Nevertheless, the problem of producing a randomized test for the value of a single regression coefficient that is exactly valid when errors are exchangeable, and which is asymptotically valid for the best linear predictor, has remained elusive. In this paper, we produce a test that is exactly valid with exchangeable errors and which allows for general…

分解加成率的上升AI 解读

A recent literature argues that the rise in aggregate markups is due to economic activity reallocating toward high-markup firms rather than an increase in firmlevel markups. I show that standard decomposition methods used to analyze this phenomenon can estimate a non-zero reallocation effect even when the joint distribution of markups and market shares is time-invariant. This is due to churning in the firm-level distribution of markups and market shares. Empirically, I find…

有限状态马尔可夫链近似：一种隐马尔可夫方法AI 解读

This paper proposes a novel finite-state Markov chain approximation method for Markov processes with continuous support, providing both an optimal grid and a transition probability matrix. The method can be used for multivariate processes and processes with time-varying components. It is based on minimizing the information loss between a Hidden Markov Model and the true data-generating process. We provide sufficient conditions under which this information loss can be made…

ARMA-Design：部分可观测实验中 A/B 测试的最优处理分配AI 解读

Online experiments are frequently employed in technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. In many applications, the experimental units receive a sequence of treatments over time. To handle these time-dependent settings, existing A/B testing solutions typically assume a fully observable experimental environment that satisfies the Markov condition. However, this assumption often does not…

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…

分位数网络向量自回归中的双向同质性分组AI 解读

We propose a two-way grouped network quantile (TGNQ) autoregression model for time series data observed on networks with substantial heterogeneity and complex directional interactions. Motivated by prior studies on network effects in directed networks, our model assigns each node two latent group memberships to flexibly capture asymmetric and heterogeneous interactions among users. These memberships, along with model parameters, can be consistently estimated using the…

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…

混合采样频率异质面板模型的估计AI 解读

ABSTRACT This paper studies heterogeneous mixed‐frequency panel data models, focusing on linear specifications that allow heterogeneity in both aggregation weights and slope coefficients. To address potential correlations between the heterogeneity and the covariates, we first show that, under additional normalization conditions, the mean‐group estimator delivers consistent and asymptotically normal slope estimates but biased weights estimators. As an alternative, we propose…

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…

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…

这个冲击不同：误设双向固定效应面板回归的估计与推断AI 解读

We investigate the properties of the linear two-way fixed effects (FE) estimator for panel data when the underlying data generating process (DGP) does not have a linear parametric structure. The FE estimator is consistent for some pseudo-true value and we characterize the corresponding asymptotic distribution. We show that the rate of convergence is determined by the degree of model misspecification, and that the asymptotic distribution can be non-normal. We propose a novel…

以倾向得分校准改善双重／去偏机器学习的平均处理效应有限样本估计AI 解读

ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates. Estimators relying on double‐robust score functions are highly sensitive to errors in propensity score predictions. Machine learning algorithms have been found to produce models that often…

Abstract Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. In this work, we focus on estimating the causal effect of a multivariate time series on a univariate time series when a third (possibly multivariate) time series confounds the relationship but remains unobserved. By assuming spectral sparsity of the confounder, we show how this problem can be framed as an adversarial outlier problem in the frequency…

动态空间自回归模型的推断与变点检测AI 解读

We analyze a varying-coefficient spatial autoregressive model with spatial fixed effects. One salient feature of the model is the incorporation of multiple spatial weight matrices through their linear combinations with varying coefficients, which help solve the problem of choosing the most “correct” one for applied econometricians who often face the availability of multiple expert spatial weight matrices. We estimate and make inferences on the model coefficients and…

稳健且可复现的网络探索AI 解读

We propose a novel methodology for discovering the presence of relationships realized as binary time series between variables in high dimension. To make it visually intuitive, we regard the existence of a relationship as an edge connection, and call a collection of such edges a network. Our objective is thus rephrased as uncovering the network by selecting relevant edges, referred to as the network exploration. Our methodology is based on multiple testing for the presence or…

含影响节点与社群的高维分位数向量自回归AI 解读

Quantile vector autoregression (QVAR) models offer enhanced capabilities over vector autoregression (VAR) models in analyzing asymmetric interactions within multiple time series and are widely used in many areas, including finance and economics. However, in scenarios involving high-dimensional data where the number of time series N exceeds the length of the time series T, both parameter estimation and the theoretical establishment present significant challenges. Notably,…

集中化 MCMC 估计AI 解读

This paper studies a concentrated Markov Chain Monte Carlo (MCMC) estimation method that offers significant computational advantages while delivering valid inference for parameters of interest. By reducing the dimension of parameters to be sampled, this method substantially decreases computational time and memory usage relative to both traditional and some state-of-the-art Bayesian algorithms. Its applicability extends to correctly specified or misspecified likelihood…

含潜变量的马尔可夫过程学习与识别AI 解读

We consider the problem of identifying the parameters of a time-homogeneous bivariate Markov chain when only one of the two variables is observable. We show that, subject to conditions that we spell out, the transition kernel and the distribution of the initial condition are uniquely recoverable (up to an arbitrary relabelling of the state space of the latent variable) from the joint distribution of four (or more) consecutive time-series observations. The result is,…