Journal of Econometrics最新文献

英文中文

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2026-01-01 DOI: 10.1016/j.jeconom.2026.106183

Xun Lu , Liangjun Su , Yinglong Ba

The widely-used common correlated effects (CCE) estimator, pioneered by Pesaran (2006), is computed using least squares applied to auxiliary regressions where the observed regressors are augmented with cross-sectional averages of the dependent variable and regressors. However, the CCE estimator requires a crucial rank condition and becomes inconsistent when this condition is violated and the factor loadings of the x- and y -equations are correlated, causing an endogeneity issue. This paper proposes a generalized CCE (GCCE) estimator by augmenting the regression with both cross-sectional and time-series averages of the regressors. We argue that the time-series average can serve as “control variables” to address the endogeneity issue. We show that the GCCE and CCE estimators are asymptotically equivalent when the rank condition holds, and the GCCE estimator remains consistent even when the rank condition is violated under our “control variable” condition. Therefore, our GCCE estimator is doubly robust, achieving consistency under either the rank condition or the “control variable” condition. Furthermore, we propose a leave-one-out jackknife method to conduct valid inferences regardless of whether the rank condition holds. Monte Carlo simulations demonstrate excellent performance of our estimators and inference methods in finite samples. We apply our new methods to two datasets to estimate the production function and gravity equation.

由Pesaran（2006）首创的广泛使用的共同相关效应（CCE）估计量是使用应用于辅助回归的最小二乘来计算的，其中观察到的回归量与因变量和回归量的横截面平均值相增强。然而，CCE估计器需要一个关键的秩条件，当这个条件被违反并且x-和y -方程的因子负载是相关的时，CCE估计器就会变得不一致，从而导致内质性问题。本文提出了一种广义CCE （GCCE）估计量，通过对回归量的横截面平均值和时间序列平均值进行扩充。我们认为时间序列平均值可以作为“控制变量”来解决内生性问题。我们证明了当秩条件成立时，GCCE和CCE估计量是渐近等价的，并且在我们的“控制变量”条件下，即使违反秩条件，GCCE估计量也保持一致。因此，我们的GCCE估计器是双鲁棒的，无论是在秩条件下还是在“控制变量”条件下都实现了一致性。此外，我们提出了一种不考虑秩条件是否成立的留一折刀方法来进行有效的推理。蒙特卡罗模拟证明了我们的估计器和推理方法在有限样本下的优异性能。我们将新方法应用于两个数据集来估计生产函数和重力方程。

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引用次数: 0

Estimation and inference for large-dimensional generalized matrix factor models 大维广义矩阵因子模型的估计与推理

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2026-01-01 DOI: 10.1016/j.jeconom.2025.106179

Xinbing Kong, Tong Zhang

This article introduces a nonlinear generalized matrix factor model, moving beyond the linear-Gaussian framework to accommodate a broader class of response models typically handled via logit, probit, Poisson, or Tobit structures. We introduce a novel Lagrange multiplier method carefully tailored to ensure that the penalized likelihood function is locally concave around the true factor and loading parameters. This leads to central limit theorems of the estimated factors and loadings which is nontrivial for nonlinear matrix factor modeling. We establish the convergence rates of the estimated factor and loading matrices for the generalized matrix factor model under general conditions that allow for correlations across samples, rows, and columns. We provide a model selection criterion to determine the numbers of row and column factors. Extensive simulation studies demonstrate the superiority in handling discrete and mixed-type variables of the generalized matrix factor model. An empirical data analysis of the company’s operating performance shows that the generalized matrix factor model does clustering and reconstruction well in the presence of discontinuous entries in the data matrix.

本文介绍了一个非线性广义矩阵因子模型，超越了线性-高斯框架，以适应通常通过logit、probit、泊松或Tobit结构处理的更广泛的响应模型。我们引入了一种新颖的拉格朗日乘子方法，以确保惩罚似然函数在真实因子和加载参数周围局部凹。这导致了估计因子和负荷的中心极限定理，这对于非线性矩阵因子建模是非平凡的。我们为广义矩阵因子模型在允许跨样本、行和列的相关性的一般条件下建立了估计因子和加载矩阵的收敛率。我们提供了一个模型选择标准来确定行和列因素的数量。大量的仿真研究证明了广义矩阵因子模型在处理离散变量和混合变量方面的优越性。对该公司经营业绩的实证数据分析表明，广义矩阵因子模型在数据矩阵中存在不连续条目的情况下，能够很好地进行聚类和重构。

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引用次数: 0

Identification in nonlinear dynamic panel models under partial stationarity 部分平稳下非线性动态面板模型的辨识

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2026-01-01 DOI: 10.1016/j.jeconom.2026.106185

Wayne Yuan Gao , Rui Wang

This paper provides a general identification approach for a wide range of nonlinear panel data models, including binary choice, ordered response, and other types of limited dependent variable models. Our approach accommodates dynamic models with any number of lagged dependent variables as well as other types of endogenous covariates. Our identification strategy relies on a partial stationarity condition, which allows for not only an unknown distribution of errors, but also temporal dependencies in errors. We derive partial identification results under flexible model specifications and establish sharpness of our identified set in the binary choice setting. We demonstrate the robust finite-sample performance of our approach using Monte Carlo simulations, and apply the approach to the empirical analysis of income categories using various ordered choice models.

本文为各种非线性面板数据模型提供了一种通用的识别方法，包括二元选择、有序响应和其他类型的有限因变量模型。我们的方法适应动态模型与任何数量的滞后因变量以及其他类型的内生协变量。我们的识别策略依赖于部分平稳条件，这不仅允许误差的未知分布，而且允许误差的时间依赖性。我们在灵活的模型规范下得到了部分识别结果，并在二元选择设置下建立了识别集的清晰度。我们使用蒙特卡罗模拟证明了我们方法的强大有限样本性能，并使用各种有序选择模型将该方法应用于收入类别的实证分析。

引用次数: 0

Uncovering mild drift in asset prices with intraday high-frequency data 利用日内高频数据揭示资产价格的温和波动

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2026-01-01 DOI: 10.1016/j.jeconom.2025.106177

Shuping Shi , Peter C.B. Phillips

Asset prices are commonly represented as a drift-diffusion process, wherein the drift component denotes the anticipated return of the asset within some time frame, while the diffusion component accommodates random shocks. The drift component has substantial practical significance but accurate estimation is typically challenging and has met with limited success in the existing literature except over large time spans. This paper explores a comprehensive range of drift-diffusion models that include constant, linear, trending, and bursting drift. Conditions are identified under which realized squared drift

RSD

is a reliable tool for gauging integrated squared drift when the time span T_n is large enough. The recently introduced drift-robust quarticity estimator

RiceQ

is found to retain consistency under twin asymptotics with T_n → ∞ and infill Δ_n → 0, subject to some constraints on the divergence rate of T_n across different drift specifications. An inferential method of detecting nonzero drift using

RSD

and

RiceQ

is proposed and the drift tests are shown to be consistent under different data generating processes with various conditions on T_n. Simulation studies reveal excellent performance of the realized squared drift measure and the drift test in finite samples. The drift test is demonstrated empirically in real-time surveillance of market abnormalities in the Nasdaq Composite Index over two notable sample periods: the dotcom bubble (1996–2003) and the artificial intelligence boom (2016–2024), using intraday data.

资产价格通常被表示为一个漂移-扩散过程，其中漂移分量表示资产在某个时间框架内的预期收益，而扩散分量则适应随机冲击。漂移分量具有重要的实际意义，但准确的估计通常是具有挑战性的，并且在现有文献中除了在大的时间跨度之外取得了有限的成功。本文探讨了广泛的漂移-扩散模型，包括常数，线性，趋势和爆发漂移。确定了当时间跨度Tn足够大时，实现平方漂移RSD是测量积分平方漂移的可靠工具的条件。最近引入的漂移鲁棒量估计器RiceQ在Tn → ∞和填充Δn → 0的双渐近下保持一致性，但Tn在不同漂移规范上的发散率受到一定的约束。提出了一种基于RSD和RiceQ的非零漂移的推理检测方法，在Tn上不同条件下的不同数据生成过程中，漂移测试结果是一致的。仿真研究表明，所实现的平方漂移测量和有限样本漂移测试具有良好的性能。利用日内数据，在纳斯达克综合指数两个显著样本时期(互联网泡沫时期（1996-2003年）和人工智能繁荣时期（2016-2024年）的市场异常实时监测中，实证证明了漂移测试。

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引用次数: 0

Exogenous consideration and extended random utility 外生考虑与扩展随机效用

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2026-01-01 DOI: 10.1016/j.jeconom.2025.106166

Roy Allen

In a consideration set model, an individual maximizes utility among the considered alternatives. I relate an exogenous consideration set additive random utility model to classic discrete choice and the extended additive random utility model, in which utility can be

- \infty

for infeasible alternatives. When observable utility shifters are bounded, all three models are observationally equivalent. Moreover, they have the same counterfactual bounds and welfare formulas given variation in price-like utility indices. For attention interventions, welfare cannot change in the full consideration model but is completely unbounded in the limited consideration model. The identified set for consideration set probabilities has a minimal width for any bounded support of shifters, but with unbounded support it is a point: identification “towards” infinity does not resemble identification “at” infinity.

在考虑集模型中，个人在考虑的备选方案中实现效用最大化。我将外生考虑集加性随机效用模型与经典离散选择和扩展加性随机效用模型联系起来，其中对于不可行的替代方案，效用可以是−∞。当可观测的效用位移有界时，所有三个模型在观测上是等效的。此外，它们具有相同的反事实界限和给定价格类效用指数变化的福利公式。对于注意干预，在充分考虑模型下，福利不会发生变化，而在有限考虑模型下，福利是完全无界的。对于移位器的任何有界支持，考虑集合概率的识别集具有最小宽度，但对于无界支持，它是一个点：“趋向”无穷远的识别与“趋向”无穷远的识别不同。

引用次数: 0

Robustness to missing data: breakdown point analysis 对缺失数据的稳健性：分解点分析

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-12-26 DOI: 10.1016/j.jeconom.2025.106151

Daniel Ober-Reynolds

Missing data is pervasive in econometric applications, and rarely is it plausible that the data are missing (completely) at random. This paper proposes a methodology for studying the robustness of results drawn from incomplete datasets. Selection is measured as the divergence from the distribution of complete observations to the distribution of incomplete observations. The breakdown point is defined as the minimal amount of selection needed to overturn a given result. Reporting point estimates and lower confidence intervals of the breakdown point is a simple, concise way to communicate the robustness of a result. An estimator of the breakdown point is proposed and shown

\sqrt{n}

-consistent and asymptotically normal. This estimator can be applied directly to conclusions drawn from any model identified with the generalized method of moments (GMM) that satisfies mild assumptions. Simulations demonstrate the finite sample performance of the breakdown point estimator on averages, linear regression, and logistic regression. The methodology is illustrated by estimating the breakdown point of conclusions drawn from several randomized controlled trails suffering from missing data due to attrition.

数据丢失在计量经济学应用中是普遍存在的，而且数据（完全）随机丢失的情况很少是可信的。本文提出了一种研究不完整数据集结果鲁棒性的方法。选择是用从完全观测分布到不完全观测分布的发散度来衡量的。分解点被定义为推翻给定结果所需的最小选择量。报告点估计和故障点的较低置信区间是传达结果稳健性的一种简单、简明的方法。给出了击穿点的估计量，并证明了其n一致和渐近正态性。该估计量可以直接应用于由广义矩法（GMM）识别的任何模型得出的结论，该模型满足温和的假设。仿真结果表明，该故障点估计器在平均、线性回归和逻辑回归上具有有限样本的性能。该方法是通过估计从几个随机对照试验中得出的结论的崩溃点来说明的，这些试验由于磨损而丢失了数据。

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引用次数: 0

Data-driven policy learning for continuous treatments 数据驱动的连续治疗策略学习

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-12-25 DOI: 10.1016/j.jeconom.2025.106170

Chunrong Ai , Yue Fang , Haitian Xie

This paper studies policy learning for continuous treatments from observational data. Continuous treatments present more significant challenges than discrete ones because population welfare may need nonparametric estimation, and policy space may be infinite-dimensional and may satisfy shape restrictions. We propose to approximate the policy space with a sequence of finite-dimensional spaces and, for any given policy, obtain the empirical welfare by applying the kernel method. We consider two cases: known and unknown propensity scores. In the latter case, we allow for machine learning of the propensity score and modify the empirical welfare to account for the effect of machine learning. The learned policy maximizes the empirical welfare or the modified empirical welfare over the approximating space. In both cases, we modify the penalty algorithm proposed in Mbakop and Tabord-Meehan (2021) to data-automate the tuning parameters (i.e., bandwidth and dimension of the approximating space) and establish an oracle inequality for the welfare regret.

本文研究了基于观测数据的连续治疗策略学习。由于人口福利可能需要非参数估计，并且政策空间可能是无限维的，并且可能满足形状限制，因此连续处理比离散处理面临更大的挑战。我们提出用有限维空间序列来近似策略空间，并对任意给定的策略，应用核方法获得经验福利。我们考虑两种情况：已知和未知的倾向得分。在后一种情况下，我们允许倾向得分的机器学习，并修改经验福利来解释机器学习的影响。学习策略使经验福利或修正经验福利在近似空间上最大化。在这两种情况下，我们修改了Mbakop和Tabord-Meehan（2021）提出的惩罚算法，使调优参数（即近似空间的带宽和维度）数据自动化，并建立了福利后悔的oracle不等式。

引用次数: 0

Empirical welfare maximization with constraints 有约束的经验福利最大化

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-12-24 DOI: 10.1016/j.jeconom.2025.106169

Liyang Sun

Empirical Welfare Maximization (EWM) is a framework that can be used to select welfare program eligibility policies based on data. This paper extends EWM by allowing for uncertainty in estimating the budget needed to implement the selected policy, in addition to its welfare. Due to the additional estimation error, I show there exist no rules that achieve the highest welfare possible while satisfying a budget constraint uniformly over a wide range of DGPs. This differs from the setting without a budget constraint where uniformity is achievable. I propose an alternative trade-off rule and illustrate it with Medicaid expansion, a setting with imperfect take-up and varying program costs.

实证福利最大化（Empirical Welfare Maximization， EWM）是一个基于数据选择福利项目资格政策的框架。本文通过考虑实施所选政策所需预算的不确定性以及其福利来扩展EWM。由于额外的估计误差，我表明不存在能够在满足预算约束的同时，在广泛的dpp范围内实现最高福利的规则。这与没有预算限制的情况不同，在这种情况下，一致性是可以实现的。我提出了另一种权衡规则，并以医疗补助计划的扩张为例进行了说明，这是一个不完美的占用和不同项目成本的设置。

引用次数: 0

Multivariate kernel regression in vector and product metric spaces 向量和积度量空间中的多元核回归

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-12-22 DOI: 10.1016/j.jeconom.2025.106168

Marcia Schafgans , Victoria Zinde-Walsh

This paper derives limit properties of nonparametric kernel regression estimators without requiring existence of density for regressors in

R^{q}

. In functional regression limit properties are established for multivariate functional regression. The rate and asymptotic normality for the Nadaraya–Watson (NW) estimator is established for distributions of regressors in

R^{q}

that allow for mass points, factor structure, multicollinearity and nonlinear dependence, as well as fractal distribution; when bounded density exists we provide statistical guarantees for the standard rate and the asymptotic normality without requiring smoothness. We demonstrate faster convergence associated with dimension reducing types of singularity, such as a fractal distribution or a factor structure in the regressors. The paper extends asymptotic normality of kernel functional regression to multivariate regression over a product of any number of metric spaces. Finite sample evidence confirms rate improvement due to singularity in regression over

R^{q}

. For functional regression the simulations underline the importance of accounting for multiple functional regressors. We demonstrate the applicability and advantages of the NW estimator in our empirical study, which reexamines the job training program evaluation based on the LaLonde data.

本文导出了非参数核回归估计量的极限性质，而不要求回归量在Rq中存在密度。在泛函回归中，建立了多元泛函回归的极限性质。对于Rq中考虑质量点、因子结构、多重共线性和非线性依赖以及分形分布的回归量分布，建立了Nadaraya-Watson （NW）估计量的速率和渐近正态性；当有界密度存在时，我们提供了标准率和渐近正态性的统计保证，而不要求平滑性。我们展示了与降维奇点类型相关的更快收敛，例如回归量中的分形分布或因子结构。本文将核泛函回归的渐近正态性推广到任意数量度量空间积上的多元回归。有限样本证据证实了由于Rq上回归的奇异性而导致的速率提高。对于函数回归，模拟强调了考虑多个函数回归量的重要性。通过对LaLonde数据的实证研究，验证了NW估计器的适用性和优越性。

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Estimation and inference for CP tensor factor models CP张量因子模型的估计与推理

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-12-18 DOI: 10.1016/j.jeconom.2025.106167

Bin Chen , Yuefeng Han , Qiyang Yu

High-dimensional tensor-valued data have recently gained attention from researchers in economics and finance. We consider the estimation and inference of high-dimensional tensor factor models, where each dimension of the tensor diverges. Our focus is on a factor model that admits CP-type tensor decomposition, which allows for non-orthogonal loading vectors. Based on the contemporary covariance matrix, we propose an iterative simultaneous projection estimation method. Our estimator is robust to weak dependence among factors and weak correlation across different dimensions in the idiosyncratic shocks. We establish an inferential theory, demonstrating both consistency and asymptotic normality under relaxed assumptions. Within a unified framework, we consider two eigenvalue ratio-based estimators for the number of factors in a tensor factor model and justify their consistency. Simulation studies confirm the theoretical results and an empirical application to sorted portfolios reveals three important factors: a market factor, a long-short factor, and a volatility factor.

高维张量值数据近年来受到了经济学和金融学研究者的关注。我们考虑高维张量因子模型的估计和推理，其中张量的每个维度都是发散的。我们的重点是一个允许cp型张量分解的因子模型，它允许非正交加载向量。基于当代协方差矩阵，提出了一种迭代同步投影估计方法。我们的估计器对特殊冲击中因素之间的弱依赖性和不同维度之间的弱相关性具有鲁棒性。我们建立了一个推理理论，证明了在宽松假设下的一致性和渐近正态性。在一个统一的框架内，我们考虑了张量因子模型中两个基于特征值比率的估计量，并证明了它们的一致性。模拟研究证实了理论结果，并通过对组合排序的实证应用揭示了三个重要因素：市场因素、多空因素和波动因素。

引用次数: 0

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