Econometrics

• New submissions
• Cross-lists
• Replacements

See recent articles

Showing new listings for Thursday, 25 September 2025

New submissions (showing 2 of 2 entries )

[1] arXiv:2509.19911 [cn-pdf, pdf, other]

Title: Decomposing Co-Movements in Matrix-Valued Time Series: A Pseudo-Structural Reduced-Rank Approach Show Chinese title

Title: 矩阵值时间序列中共同运动的分解：一种伪结构降秩方法

Alain Hecq, Ivan Ricardo, Ines Wilms

Subjects: Econometrics (econ.EM)

We propose a pseudo-structural framework for analyzing contemporaneous co-movements in reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector-autoregressive (VAR) models that would discard the matrix structure, our formulation preserves it, enabling a decomposition of co-movements into three interpretable components: row-specific, column-specific, and joint (row-column) interactions across the matrix-valued time series. Our estimator admits standard asymptotic inference and we propose a BIC-type criterion for the joint selection of the reduced ranks and the autoregressive lag order. We validate the method's finite-sample performance in terms of estimation accuracy, coverage and rank selection in simulation experiments, including cases of rank misspecification. We illustrate the method's practical usefelness in identifying co-movement structures in two empirical applications: U.S. state-level coincident and leading indicators, and cross-country macroeconomic indicators. Show Chinese abstract

我们提出了一种伪结构框架，用于分析降秩矩阵自回归（RRMAR）模型中的同时共动情况。 与会丢弃矩阵结构的传统向量自回归（VAR）模型不同，我们的公式保留了该结构，使得能够将共动分解为三个可解释的组成部分：行特定、列特定以及跨矩阵值时间序列的联合（行-列）交互作用。 我们的估计器允许标准渐近推断，并我们提出了一个类似BIC的准则，用于联合选择降秩和自回归滞后阶数。 我们在模拟实验中验证了该方法在估计精度、覆盖范围和秩选择方面的有限样本性能，包括秩误指的情况。 我们在两个实证应用中展示了该方法的实际有用性：美国各州层面的同步指标和领先指标，以及国家间的宏观经济指标。

[2] arXiv:2509.19945 [cn-pdf, pdf, html, other]

Title: Identification and Estimation of Seller Risk Aversion in Ascending Auctions Show Chinese title

Title: 在增价拍卖中卖方风险厌恶的识别与估计

Nathalie Gimenes, Tonghui Qi, Sorawoot Srisuma

Subjects: Econometrics (econ.EM)

How sellers choose reserve prices is central to auction theory, and the optimal reserve price depends on the seller's risk attitude. Numerous studies have found that observed reserve prices lie below the optimal level implied by risk-neutral sellers, while the theoretical literature suggests that risk-averse sellers can rationalize these empirical findings. In this paper, we develop an econometric model of ascending auctions with a risk-averse seller under independent private values. We provide primitive conditions for the identification of the Arrow-Pratt measures of risk aversion and an estimator for these measures that is consistent and converges in distribution to a normal distribution at the parametric rate under standard regularity conditions. A Monte Carlo study demonstrates good finite-sample performance of the estimator, and we illustrate the approach using data from foreclosure real estate auctions in S\~{a}o Paulo. Show Chinese abstract

卖家如何选择保留价是拍卖理论的核心，而最优保留价取决于卖家的风险态度。 众多研究发现，观察到的保留价低于风险中性卖家所暗示的最优水平，而理论文献表明，风险规避的卖家可以合理解释这些实证结果。 在本文中，我们在独立私有价值下开发了一个风险规避卖家的增价拍卖计量经济学模型。 我们提供了识别Arrow-Pratt风险规避度量的基本条件，并提供了一个估计量，在标准正则条件下，该估计量是一致的，并以参数速率收敛于正态分布。 A 蒙特卡罗研究显示了该估计量的良好有限样本性能，并我们使用圣保罗抵押房地产拍卖数据来说明该方法。

Cross submissions (showing 1 of 1 entries )

[3] arXiv:2509.20194 (cross-list from stat.ME) [cn-pdf, pdf, other]

Title: Identification and Semiparametric Estimation of Conditional Means from Aggregate Data Show Chinese title

Title: 从聚合数据中识别和条件均值的半参数估计

Cory McCartan, Shiro Kuriwaki

Comments: 24 pages, plus references and appendices

Subjects: Methodology (stat.ME) ; Econometrics (econ.EM)

We introduce a new method for estimating the mean of an outcome variable within groups when researchers only observe the average of the outcome and group indicators across a set of aggregation units, such as geographical areas. Existing methods for this problem, also known as ecological inference, implicitly make strong assumptions about the aggregation process. We first formalize weaker conditions for identification, which motivates estimators that can efficiently control for many covariates. We propose a debiased machine learning estimator that is based on nuisance functions restricted to a partially linear form. Our estimator also admits a semiparametric sensitivity analysis for violations of the key identifying assumption, as well as asymptotically valid confidence intervals for local, unit-level estimates under additional assumptions. Simulations and validation on real-world data where ground truth is available demonstrate the advantages of our approach over existing methods. Open-source software is available which implements the proposed methods. Show Chinese abstract

我们引入了一种新方法，在研究人员仅观察到结果变量的平均值和一组聚合单元（如地理区域）的组指示符时，用于估计结果变量在组内的均值。 对于这个问题的现有方法，也称为生态推断，隐含地对聚合过程做出了强烈的假设。 我们首先形式化了较弱的识别条件，这促使可以有效控制许多协变量的估计量。 我们提出了一种去偏机器学习估计量，该估计量基于限制为部分线性形式的干扰函数。 我们的估计量还允许对关键识别假设的违反进行半参数敏感性分析，并且在额外假设下，对局部、单元级估计量提供渐近有效的置信区间。 模拟和在真实数据上的验证，当有真实情况可用时，证明了我们的方法优于现有方法。 开源软件已发布，实现了所提出的方法。

Replacement submissions (showing 7 of 7 entries )

[4] arXiv:2211.11915 (replaced) [cn-pdf, pdf, html, other]

Title: A Misuse of Specification Tests Show Chinese title

Title: 规范检验的误用

Naoya Sueishi

Subjects: Econometrics (econ.EM)

Empirical researchers often perform model specification tests, such as Hausman tests and overidentifying restrictions tests, to assess the validity of estimators rather than that of models. This paper examines the effectiveness of such specification pretests in detecting invalid estimators. We analyze the local asymptotic properties of test statistics and estimators and show that locally unbiased specification tests cannot determine whether asymptotically efficient estimators are asymptotically biased. In particular, an estimator may remain valid even when the null hypothesis of correct model specification is false, and it may be invalid even when the null hypothesis is true. The main message of the paper is that correct model specification and valid estimation are distinct issues: correct specification is neither necessary nor sufficient for asymptotically unbiased estimation. Show Chinese abstract

实证研究者经常进行模型设定检验，例如Hausman检验和过度识别限制检验，以评估估计量的有效性，而不是模型的有效性。本文研究了此类设定预检在检测无效估计量方面的有效性。我们分析了检验统计量和估计量的局部渐近性质，并表明局部无偏的设定检验无法确定渐近有效的估计量是否渐近有偏。特别是，当正确模型设定的原假设不成立时，估计量可能仍然有效，而当原假设成立时，估计量可能无效。本文的主要观点是，正确的模型设定和有效的估计是不同的问题：正确的设定对于渐近无偏估计既不是必要条件也不是充分条件。

[5] arXiv:2403.15220 (replaced) [cn-pdf, pdf, html, other]

Title: Modelling with Sensitive Variables Show Chinese title

Title: 带有敏感变量的建模

Felix Chan, Laszlo Matyas, Agoston Reguly

Comments: 31 pages, 2 tables, 2 figures

Subjects: Econometrics (econ.EM) ; Methodology (stat.ME)

The paper deals with models in which the dependent variable, some explanatory variables, or both represent sensitive data. We introduce a novel discretization method that preserves data privacy when working with such variables. A multiple discretization method is proposed that utilizes information from the different discretization schemes. We show convergence in distribution for the unobserved variable and derive the asymptotic properties of the OLS estimator for linear models. Monte Carlo simulation experiments presented support our theoretical findings. Finally, we contrast our method with a differential privacy method to estimate the Australian gender wage gap. Show Chinese abstract

本文讨论了因变量、一些解释变量或两者都代表敏感数据的模型。 我们引入了一种新的离散化方法，在处理这些变量时保护数据隐私。 提出了一种多重离散化方法，利用不同离散化方案的信息。 我们展示了未观测变量的分布收敛性，并推导了线性模型中OLS估计量的渐近性质。 提出的蒙特卡罗模拟实验支持我们的理论结果。 最后，我们将我们的方法与一种差分隐私方法进行对比，以估计澳大利亚的性别工资差距。

[6] arXiv:2405.17787 (replaced) [cn-pdf, pdf, other]

Title: Dyadic Regression with Sample Selection Show Chinese title

Title: 二元回归与样本选择

Kensuke Sakamoto

Subjects: Econometrics (econ.EM)

This paper addresses the sample selection problem in panel dyadic regression analysis. Dyadic data often include many zeros in the main outcomes due to the underlying network formation process. This not only contaminates popular estimators used in practice but also complicates the inference due to the dyadic dependence structure. We extend Kyriazidou (1997)'s approach to dyadic data and characterize the asymptotic distribution of our proposed estimator. The convergence rates are $\sqrt{n}$ or $\sqrt{n^{2}h_{n}}$, depending on the degeneracy of the H\'{a}jek projection part of the estimator, where $n$ is the number of nodes and $h_{n}$ is a bandwidth. We propose a bias-corrected confidence interval and a variance estimator that adapts to the degeneracy. A Monte Carlo simulation shows the good finite sample performance of our estimator and highlights the importance of bias correction in both asymptotic regimes when the fraction of zeros in outcomes varies. We illustrate our procedure using data from Moretti and Wilson (2017)'s paper on migration. Show Chinese abstract

本文解决了面板二元回归分析中的样本选择问题。 二元数据由于底层网络形成过程往往包含许多主结果中的零值。 这不仅污染了实践中常用的估计量，还由于二元依赖结构而使推断变得复杂。 我们扩展了Kyriazidou（1997）的方法以适用于二元数据，并描述了我们所提出估计量的渐近分布。 收敛速度为$\sqrt{n}$或$\sqrt{n^{2}h_{n}}$，具体取决于估计量的Hájek投影部分的退化性，其中$n$是节点数，$h_{n}$是带宽。 我们提出了一个偏差校正的置信区间和一个适应退化性的方差估计量。 蒙特卡洛模拟显示了我们估计量在有限样本中的良好表现，并强调了在结果中零值比例变化时，在两种渐近情形下偏差校正的重要性。 我们使用Moretti和Wilson（2017）关于迁移论文中的数据来说明我们的方法。

[7] arXiv:2407.00890 (replaced) [cn-pdf, pdf, html, other]

Title: Macroeconomic Forecasting with Large Language Models Show Chinese title

Title: 宏观经济预测与大型语言模型

Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar

Subjects: Econometrics (econ.EM) ; Computation and Language (cs.CL) ; Machine Learning (cs.LG)

This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LLMs have surged in popularity for forecasting due to their ability to capture intricate patterns in data and quickly adapt across very different domains. However, their effectiveness in forecasting macroeconomic time series data compared to conventional methods remains an area of interest. To address this, we conduct a rigorous evaluation of LLMs against traditional macro forecasting methods, using as common ground the FRED-MD database. Our findings provide valuable insights into the strengths and limitations of LLMs in forecasting macroeconomic time series, shedding light on their applicability in real-world scenarios Show Chinese abstract

本文提出了一项比较分析，评估大型语言模型（LLMs）在准确性方面与传统宏观时间序列预测方法的对比。近年来，由于LLMs能够捕捉数据中的复杂模式并在非常不同的领域中快速适应，它们在预测领域的受欢迎程度迅速上升。然而，与传统方法相比，它们在预测宏观经济时间序列数据方面的有效性仍是一个值得研究的领域。为了解决这个问题，我们对LLMs与传统的宏观预测方法进行了严格的评估，使用FRED-MD数据库作为共同基础。我们的研究结果提供了关于LLMs在预测宏观经济时间序列中的优势和局限性的宝贵见解，揭示了它们在现实场景中的适用性。

[8] arXiv:2504.21156 (replaced) [cn-pdf, pdf, html, other]

Title: Publication Design with Incentives in Mind Show Chinese title

Title: 考虑激励的出版设计

Ravi Jagadeesan, Davide Viviano

Subjects: Econometrics (econ.EM) ; Theoretical Economics (econ.TH) ; Statistics Theory (math.ST)

The publication process both determines which research receives the most attention, and influences the supply of research through its impact on researchers' private incentives. We introduce a framework to study optimal publication decisions when researchers can choose (i) whether or how to conduct a study and (ii) whether or how to manipulate the research findings (e.g., via selective reporting or data manipulation). When manipulation is not possible, but research entails substantial private costs for the researchers, it may be optimal to incentivize cheaper research designs even if they are less accurate. When manipulation is possible, it is optimal to publish some manipulated results, as well as results that would have not received attention in the absence of manipulability. Even if it is possible to deter manipulation, such as by requiring pre-registered experiments instead of (potentially manipulable) observational studies, it is suboptimal to do so when experiments entail high research costs. We illustrate the implications of our model in an application to medical studies. Show Chinese abstract

出版过程既决定了哪些研究获得最多关注，也通过其对研究人员私人激励的影响影响了研究的供给。我们引入了一个框架来研究当研究人员可以选择（i）是否或如何进行一项研究，以及（ii）是否或如何操纵研究结果（例如，通过选择性报告或数据操纵）时的最优出版决策。当无法操纵时，但研究对研究人员来说有显著的私人成本，即使这些研究设计准确性较低，也可能最优的是激励更便宜的研究设计。当可以操纵时，最优的是发表一些被操纵的结果，以及在没有可操纵性的情况下不会受到关注的结果。即使可以阻止操纵，例如通过要求预注册实验而不是（可能被操纵的）观察性研究，当实验的研究成本很高时，这样做是次优的。我们在医学研究的应用中说明了我们模型的含义。

[9] arXiv:2309.08808 (replaced) [cn-pdf, pdf, other]

Title: Adaptive Neyman Allocation Show Chinese title

Title: 自适应奈曼分配

Jinglong Zhao

Subjects: Methodology (stat.ME) ; Econometrics (econ.EM)

In the experimental design literature, Neyman allocation refers to the practice of allocating units into treated and control groups, potentially in unequal numbers proportional to their respective standard deviations, with the objective of minimizing the variance of the treatment effect estimator. This widely recognized approach increases statistical power in scenarios where the treated and control groups have different standard deviations, as is often the case in social experiments, clinical trials, marketing research, and online A/B testing. However, Neyman allocation cannot be implemented unless the standard deviations are known in advance. Fortunately, the multi-stage nature of the aforementioned applications allows the use of earlier stage observations to estimate the standard deviations, which further guide allocation decisions in later stages. In this paper, we introduce a competitive analysis framework to study this multi-stage experimental design problem. We propose a simple adaptive Neyman allocation algorithm, which almost matches the information-theoretic limit of conducting experiments. We provide theory for estimation and inference using data collected from our adaptive Neyman allocation algorithm. We demonstrate the effectiveness of our adaptive Neyman allocation algorithm using both online A/B testing data from a social media site and synthetic data. Show Chinese abstract

在实验设计文献中，Neyman分配指的是将单元分配到处理组和对照组的做法，可能根据各自的标准差按不成比例的方式进行分配，其目标是使处理效应估计量的方差最小化。 这种广为人知的方法在处理组和对照组具有不同标准差的情况下可以提高统计功效，这在社会实验、临床试验、市场营销研究和在线A/B测试中很常见。 然而，除非事先知道标准差，否则无法实施Neyman分配。 幸运的是，上述应用的多阶段性质允许使用早期阶段的观测值来估计标准差，从而进一步指导后续阶段的分配决策。 在本文中，我们引入了一个竞争分析框架来研究这个多阶段实验设计问题。 我们提出了一种简单的自适应Neyman分配算法，该算法几乎达到进行实验的信息理论极限。 我们提供了使用从我们的自适应Neyman分配算法中收集的数据进行估计和推断的理论。 我们使用来自社交媒体网站的在线A/B测试数据和合成数据来展示我们的自适应Neyman分配算法的有效性。

[10] arXiv:2504.19018 (replaced) [cn-pdf, pdf, html, other]

Title: Finite-Sample Properties of Generalized Ridge Estimators in Nonlinear Models Show Chinese title

Title: 广义岭估计量在非线性模型中的有限样本性质

Masamune Iwasawa

Subjects: Methodology (stat.ME) ; Econometrics (econ.EM)

This paper addresses the longstanding challenge of analyzing the mean squared error (MSE) of ridge-type estimators in nonlinear models, including duration, Poisson, and multinomial choice models, where theoretical results have been scarce. Using a finite-sample approximation technique from the econometrics literature, we derive new results showing that the generalized ridge maximum likelihood estimator (MLE) with a sufficiently small penalty achieves lower finite-sample MSE for both estimation and prediction than the conventional MLE, regardless of whether the hypotheses incorporated in the penalty are valid. A key theoretical contribution is to demonstrate that generalized ridge estimators generate a variance-bias trade-off in the first-order MSE of nonlinear likelihood-based models -- a feature absent for the conventional MLE -- which enables ridge-type estimators to attain smaller MSE when the penalty is properly selected. Extensive simulations and an empirical application to the estimation of marginal mean and quantile treatment effects further confirm the superior performance and practical relevance of the proposed method. Show Chinese abstract

本文解决了在非线性模型中分析岭型估计量均方误差（MSE）的长期挑战，包括持续时间、泊松和多项选择模型，其中理论结果一直很少。利用计量经济学文献中的有限样本近似技术，我们推导出新的结果，表明具有足够小惩罚的广义岭最大似然估计量（MLE）在估计和预测方面都比传统的MLE具有更低的有限样本MSE，无论惩罚中包含的假设是否有效。一个关键的理论贡献是证明广义岭估计量在非线性似然模型的第一阶MSE中产生方差-偏差权衡——这是传统MLE所不具备的特性——这使得当惩罚适当选择时，岭型估计量能够达到更小的MSE。大量的模拟和对边际均值和分位数处理效应估计的实证应用进一步证实了所提出方法的优越性能和实际相关性。