Journal of Econometrics最新文献

英文中文

Difference-in-Differences with compositional changes 差异中的差异与成分的变化

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-19 DOI: 10.1016/j.jeconom.2025.106147

Pedro H.C. Sant’Anna , Qi Xu

This paper studies Difference-in-Differences (DiD) setups with repeated cross-sectional data and potential compositional changes across time periods. We begin our analysis by deriving the efficient influence function and the semiparametric efficiency bound for the average treatment effect on the treated (ATT). We introduce nonparametric estimators that attain the semiparametric efficiency bound under mild rate conditions on the estimators of the nuisance functions, exhibiting a type of rate doubly robust (DR) property. Additionally, we document a trade-off related to compositional changes: We derive the asymptotic bias of DR DiD estimators that erroneously exclude compositional changes and the efficiency loss when one fails to correctly rule out compositional changes. We propose a nonparametric Hausman-type test for compositional changes based on these trade-offs. The finite sample performance of the proposed DiD tools is evaluated through Monte Carlo experiments and an empirical application. We consider extensions of our framework that accommodate double machine learning procedures with cross-fitting, and setups when some units are observed in both pre- and post-treatment periods. As a by-product of our analysis, we present a new uniform stochastic expansion of the local polynomial multinomial logit estimator, which may be of independent interest.

本文研究了具有重复横截面数据和跨时间段潜在成分变化的差异中的差异（DiD）设置。我们通过推导平均处理效应对被处理（ATT）的有效影响函数和半参数效率界开始分析。我们引入了非参数估计量，在温和速率条件下，在扰值函数的估计量上得到半参数效率界，并表现出一种速率双鲁棒性。此外，我们记录了与组成变化相关的权衡：我们推导了DR DiD估计器的渐近偏差，该估计器错误地排除了组成变化，并且当一个人未能正确排除组成变化时，效率损失。我们提出了一个基于这些权衡的成分变化的非参数hausman型检验。通过蒙特卡罗实验和经验应用评估了所提出的DiD工具的有限样本性能。我们考虑扩展我们的框架，以适应交叉拟合的双重机器学习过程，并在处理前后观察到一些单元时进行设置。作为我们分析的副产品，我们给出了局部多项式多项式对数估计量的一个新的一致随机展开式，它可能具有独立的意义。

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引用次数: 0

A jackknife bias correction for nonlinear network data models with fixed effects 具有固定效应的非线性网络数据模型的折刀偏差校正

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-19 DOI: 10.1016/j.jeconom.2025.106130

David W. Hughes

I introduce a new method for bias correction of dyadic models with agent-specific fixed effects, including the dyadic link formation model with homophily and degree heterogeneity. The proposed approach uses a jackknife procedure to deal with the incidental parameters problem. The method can be applied to both directed and undirected networks, allows for non-binary outcome variables, and can be used to bias correct estimates of average effects and counterfactual outcomes. I also show how the jackknife can be used to bias correct fixed-effect averages over functions that depend on multiple nodes, e.g. triads or tetrads in the network. As an example, I implement specification tests for dependence across dyads, such as reciprocity or transitivity. Finally, I demonstrate the usefulness of the estimator in an application to a gravity model for import/export relationships across countries.

本文介绍了一种新的具有主体特异性固定效应的二元模型的偏差校正方法，包括具有同质性和程度异质性的二元链接形成模型。该方法采用折刀法处理附带参数问题。该方法可应用于有向和无向网络，允许非二元结果变量，并可用于偏差正确估计平均效果和反事实结果。我还展示了如何使用jackknife来偏差校正依赖于多个节点的函数的固定效应平均值，例如网络中的三元组或四元组。作为一个例子，我实现了对二元依赖性的规范测试，比如互易性或传递性。最后，我将演示估计器在一个应用程序中对跨国家进出口关系的引力模型的有用性。

引用次数: 0

Functional semiparametric modeling for nonstationary and periodic time series data 非平稳周期时间序列数据的泛函半参数建模

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-17 DOI: 10.1016/j.jeconom.2025.106149

Shouxia Wang , Hua Liu , Jinhong You , Tao Huang

Inspired by a real data example illustrating the periodicity in hog price data, this study aims to analyze time series that exhibit an unknown period alongside complex covariate effects. To address these complexities and effectively handle the data structures, we incorporate the partial functional varying-coefficient single-index model into the classical time series decomposition model. We propose a two-stage estimation procedure designed to accurately estimate the unknown periodic component and the associated covariate functions. In the first stage, the unknown period is estimated using a penalized least squares approach, where the covariate functions are approximated via B-splines rather than being ignored. In the second stage, given the estimated period, we employ B-splines to estimate key components, including the amplitude of the periodic component, the varying-coefficient functions, the single-index link function, and the functional slope function. Asymptotic results for the proposed estimators are derived, encompassing the consistency of the period estimator as well as the asymptotic properties of the estimated periodic sequence and covariate functions. Furthermore, we conduct simulations to validate the superior performance of the proposed method and demonstrate its practical applicability through the aforementioned empirical example.

受一个说明生猪价格数据周期性的真实数据示例的启发，本研究旨在分析具有未知周期和复杂协变量效应的时间序列。为了解决这些复杂性并有效地处理数据结构，我们将偏函数变系数单指标模型纳入经典的时间序列分解模型。我们提出了一个两阶段的估计程序，旨在准确地估计未知的周期分量和相关的协变量函数。在第一阶段，使用惩罚最小二乘方法估计未知周期，其中协变量函数通过b样条近似而不是被忽略。在第二阶段，给定估计周期，我们使用b样条来估计关键分量，包括周期分量的振幅、变系数函数、单指标链接函数和函数斜率函数。给出了所提估计量的渐近结果，包括周期估计量的相合性以及估计的周期序列和协变量函数的渐近性质。并通过仿真验证了所提方法的优越性能，并通过上述实例验证了所提方法的实用性。

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引用次数: 0

Large-scale model comparison with fast model confidence sets 快速模型置信集的大尺度模型比较

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-14 DOI: 10.1016/j.jeconom.2025.106123

Sylvain Barde

The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations use an elimination approach, where one starts with the full collection of models and successively eliminates the worst performing until the null of equal predictive ability is no longer rejected at a given confidence level. The intuition behind the proposed implementation lies in reversing the process, i.e. starting with a collection of two models and updating both the model rankings and p-values as models are successively added to the collection. The first benefit of this approach is a reduction of one polynomial order in both the time complexity and memory cost of finding the confidence set of a collection of

M

models using the

R

rule, falling respectively from

O (M^{3})

O (M^{2})

and from

O (M^{2})

O (M)

. The second key benefit is that it allows for further models to be added at a later point in time, thus enabling collaborative efforts using the model confidence set procedure. The paper proves that this implementation is equivalent to the elimination approach, demonstrates the improved performance on a multivariate GARCH collection consisting of 4800 models, and discusses possible use-cases where this improved performance could prove useful.

本文提出了一种寻找预测集合或预测模型置信集的新算法。现有的数值实现使用一种消除方法，即从模型的完整集合开始，逐步消除表现最差的模型，直到在给定的置信度水平上不再拒绝相同预测能力的零值。提出的实现背后的直觉在于反转过程，即从两个模型的集合开始，并在模型陆续添加到集合时更新模型排名和p值。这种方法的第一个好处是，使用R规则查找M个模型集合的置信集的时间复杂度和内存成本降低了一个多项式阶，分别从OM3降至OM2和从OM2降至OM。第二个关键的好处是，它允许在稍后的时间点添加进一步的模型，从而支持使用模型置信度集过程的协作工作。本文证明了这种实现等同于消除方法，演示了在包含4800个模型的多元GARCH集合上的性能改进，并讨论了这种改进的性能可能被证明有用的用例。

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引用次数: 0

Testing for peer effects without specifying the network structure 在不指定网络结构的情况下测试对等效应

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-14 DOI: 10.1016/j.jeconom.2025.106124

Hyunseok Jung , Xiaodong Liu

This paper proposes an Anderson–Rubin (AR) test for the presence of peer effects in panel data without the need to specify the network structure. The unrestricted model of our test is a linear panel data model of social interactions with dyad-specific peer effect coefficients for all potential peers. The proposed AR test evaluates if these peer effect coefficients are all zero. As the number of peer effect coefficients increases with the sample size, so does the number of instrumental variables (IVs) employed to test the restrictions under the null, rendering a many-IV environment of Bekker (1994). By extending existing many-IV asymptotic results to panel data, we establish the asymptotic validity of the proposed AR test. Our Monte Carlo simulations show the robustness and improved performance of the proposed test compared to some existing tests with misspecified networks. We provide two applications to demonstrate its empirical relevance.

本文在不指定网络结构的情况下，提出了面板数据中对等效应存在的安德森-鲁宾（AR）检验方法。我们测试的无限制模型是一个线性面板数据模型，具有所有潜在同伴特定的同伴效应系数的社会互动。提出的AR检验评估这些对等效应系数是否都为零。随着样本量的增加，同伴效应系数的数量也会增加，用于检验null下限制的工具变量（IVs）的数量也会增加，从而呈现出Bekker（1994）的多iv环境。通过将现有的许多- iv渐近结果扩展到面板数据，我们建立了所提出的AR检验的渐近有效性。我们的蒙特卡罗模拟表明，与现有的一些错误指定网络的测试相比，所提出的测试具有鲁棒性和改进的性能。我们提供了两个应用程序来证明其经验相关性。

引用次数: 0

Five lessons for applied researchers from twenty years of common correlated effects estimation 二十年常见相关效应估计给应用研究人员的五点启示

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-13 DOI: 10.1016/j.jeconom.2025.106120

Artūras Juodis , Simon Reese

This article distills the vast literature on Common Correlated Effects (CCE), initiated by the seminal contribution of Pesaran (2006), into five practical lessons. We provide a concise overview of the CCE framework and describe the reasons for its popularity in empirical (macro-) panel data research. The lessons we draw focus on aspects that have received substantial methodological attention, but remain underappreciated in empirical work.

本文将由Pesaran（2006）的开创性贡献发起的关于共同相关效应（CCE）的大量文献提炼成五个实践教训。我们简要概述了CCE框架，并描述了其在实证（宏观）面板数据研究中受欢迎的原因。我们吸取的教训集中在已经得到大量方法论关注的方面，但在实证工作中仍未得到充分重视。

引用次数: 0

Enhancements of communication-efficient distributed statistical inference and its privacy preservation 提高通信效率的分布式统计推断及其隐私保护

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-13 DOI: 10.1016/j.jeconom.2025.106125

Miaomiao Yu , Jiaxuan Li , Yong Zhou

In the modern era of big data, the vast amount of available data has brought more ways to analyze important economic and financial issues. For example, predicting the probability of individual default has become more accurate, as the number of defaulted individuals has increased year-on-year with the increase in data volume, leading to a more detailed characterization of the defaulted population. However, it presents new challenges and one of them is that all samples are separately stored in different machines and cannot be transferred directly for privacy considerations and limited data storage capacity. This paper develops an improved communication-efficient distributed algorithm in which more local summarized information is used to estimate the high-order derivatives of the loss function with lower communication cost. Furthermore, to protect the privacy in the interacted vector, we design a privacy-preserving algorithm based on the differential privacy constraint by adding a Laplace-distributed noise term in the parameters that can be extended to other cases beyond distributed architectures. Both non-private and private schemes, in which only local estimators are passed from the local machine to the central machine, are more theoretically and practically accurate and efficient than their counterparts. Then we suggest a bootstrap scheme to estimate the covariance matrix of the parametric estimators that is beneficial to effective inference. Finally, we find that the proposed method can effectively handle the practical activities that are, accurate probabilistic predictions of default risk and climate activity.

在现代大数据时代，大量的可用数据为分析重要的经济和金融问题带来了更多的方法。例如，预测个人违约的概率变得更加准确，因为随着数据量的增加，违约个人的数量逐年增加，从而可以更详细地描述违约人群。然而，这也带来了新的挑战，其中之一就是所有的样本都是单独存储在不同的机器上，由于隐私的考虑和数据存储容量的限制，不能直接传输。本文提出了一种改进的通信高效分布式算法，该算法利用更多的局部汇总信息来估计损失函数的高阶导数，并且通信成本较低。此外，为了保护交互向量中的隐私，我们设计了一种基于差分隐私约束的隐私保护算法，该算法在参数中添加了拉普拉斯分布噪声项，可扩展到分布式架构以外的其他情况。非私有方案和私有方案，其中只有局部估计量从本地机器传递到中央机器，在理论上和实践中都比它们的对应方案更准确和有效。然后，我们提出了一种自举方案来估计参数估计量的协方差矩阵，这有利于有效的推理。最后，我们发现该方法可以有效地处理实际活动，即对违约风险和气候活动进行准确的概率预测。

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引用次数: 0

Quasi-Bayesian estimation and inference with control functions 带控制函数的拟贝叶斯估计与推理

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-13 DOI: 10.1016/j.jeconom.2025.106126

Ruixuan Liu , Zhengfei Yu

This paper introduces a quasi-Bayesian method that integrates frequentist nonparametric estimation with Bayesian inference in a two-stage process. Applied to an endogenous discrete choice model, the approach first uses kernel or sieve estimators to estimate the control function nonparametrically, followed by Bayesian methods to estimate the structural parameters. This combination leverages the advantages of both frequentist tractability for nonparametric estimation and Bayesian computational efficiency for complicated structural models. We analyze the asymptotic properties of the resulting quasi-posterior distribution, finding that its mean provides a consistent estimator for the parameters of interest, although its quantiles do not yield valid confidence intervals. However, bootstrapping the quasi-posterior mean accounts for the estimation uncertainty from the first stage, thereby producing asymptotically valid confidence intervals

本文介绍了一种拟贝叶斯方法，该方法将频率非参数估计与贝叶斯推理集成为两阶段过程。该方法应用于内源性离散选择模型，首先使用核或筛估计器非参数估计控制函数，然后使用贝叶斯方法估计结构参数。这种组合利用了非参数估计的频率可追溯性和复杂结构模型的贝叶斯计算效率的优点。我们分析了所得到的准后验分布的渐近性质，发现它的平均值为感兴趣的参数提供了一致的估计量，尽管它的分位数没有产生有效的置信区间。然而，自举准后验均值解释了第一阶段的估计不确定性，从而产生渐近有效的置信区间

引用次数: 0

Jump detection in high-frequency order prices 高频订单价格的跳跃检测

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-12 DOI: 10.1016/j.jeconom.2025.106133

Markus Bibinger , Nikolaus Hautsch , Alexander Ristig

We propose methods to infer jumps of a semi-martingale, which describes long-term price dynamics, based on discrete, noisy, high-frequency observations. Different to the classical model of additive, centered market microstructure noise, we consider one-sided microstructure noise for order prices in a limit order book.

We develop methods to estimate, locate and test for jumps using local minima of best ask quotes. We provide a local jump test and show that we can consistently estimate jump sizes and jump times. One main contribution is a global test for jumps. We establish the asymptotic properties and optimality of this test. We derive the asymptotic distribution of a maximum statistic under the null hypothesis of no jumps based on extreme value theory. We prove consistency under the alternative hypothesis. The rate of convergence for local alternatives is determined and shown to be much faster than optimal rates for the standard market microstructure noise model. This allows the identification of smaller jumps. In the process, we establish uniform consistency for spot volatility estimation under one-sided noise. Online jump detection based on the new approach is shown to achieve a speed advantage compared to standard methods applied to mid quotes.

A simulation study sheds light on the finite-sample implementation and properties of the new approach and draws a comparison to a popular method for market microstructure noise. We showcase how our new approach helps to improve jump detection in an empirical analysis of intra-daily limit order book data.

我们提出的方法来推断跳跃的半鞅，描述长期价格动态，基于离散的，有噪声的，高频的观察。不同于经典的附加的、中心的市场微观结构噪声模型，我们考虑了限价订单中订单价格的片面微观结构噪声。我们开发了使用最佳报价的局部最小值来估计、定位和测试跳跃的方法。我们提供了一个局部跳跃测试，并证明我们可以一致地估计跳跃大小和跳跃时间。一个主要贡献是对跳跃的全球测试。我们建立了这个检验的渐近性质和最优性。基于极值理论，导出了无跳跃零假设下最大统计量的渐近分布。我们在备择假设下证明了一致性。确定并证明了局部替代方案的收敛速度比标准市场微观结构噪声模型的最佳速度快得多。这允许识别较小的跳跃。在此过程中，我们建立了单边噪声下的点波动率估计的一致一致性。与应用于中引号的标准方法相比，基于新方法的在线跳转检测显示具有速度优势。仿真研究揭示了新方法的有限样本实现和特性，并与市场微观结构噪声的流行方法进行了比较。我们展示了我们的新方法如何帮助改善跳跃检测在每日限价订单数据的实证分析。

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引用次数: 0

Identification and estimation of partial effects in nonlinear semiparametric panel models 非线性半参数面板模型部分效应的辨识与估计

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105860

Laura Liu , Alexandre Poirier , Ji-Liang Shiu

Average partial effects (APEs) are often not point identified in panel models with unrestricted unobserved individual heterogeneity, as in binary response panel models with fixed effects and logistic errors for example. This lack of point identification occurs despite the identification of these models’ common coefficients. We provide a unified framework to establish the point identification of various partial effects in a wide class of nonlinear semiparametric models under an index sufficiency assumption on the unobserved heterogeneity, even when the error distribution is unspecified and non-stationary. This assumption does not impose parametric restrictions on the unobserved heterogeneity and idiosyncratic errors. We also present partial identification results when the support condition fails. We then propose three-step semiparametric estimators for APEs, average structural functions, and average marginal effects, and show their consistency and asymptotic normality. Finally, we illustrate our approach in a study of determinants of married women’s labor supply.

在具有不受限制的未观察到的个体异质性的面板模型中，平均部分效应（ape）通常不能被点识别，例如在具有固定效应和逻辑误差的二元响应面板模型中。尽管识别了这些模型的公共系数，但仍然会出现缺乏点识别的情况。我们提供了一个统一的框架，在未观测到异质性的指标充分性假设下，即使误差分布未指定和非平稳，也可以在一类广泛的非线性半参数模型中建立各种局部效应的点识别。这一假设没有对未观察到的异质性和特质误差施加参数限制。给出了支撑条件失效时的部分辨识结果。然后，我们提出了类似性、平均结构函数和平均边际效应的三步半参数估计，并证明了它们的一致性和渐近正态性。最后，我们在已婚妇女劳动力供给决定因素的研究中说明了我们的方法。

引用次数: 0

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