Journal of Econometrics最新文献

英文中文

Nonparametric regression under cluster sampling 聚类抽样下的非参数回归

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-09-22 DOI: 10.1016/j.jeconom.2025.106102

Yuya Shimizu

This paper develops a general asymptotic theory for nonparametric kernel regression in the presence of cluster dependence. We examine nonparametric density estimation, Nadaraya–Watson kernel regression, and local linear estimation. Our theory accommodates growing and heterogeneous cluster sizes. We derive asymptotic conditional bias and variance, establish uniform consistency, and prove asymptotic normality. Our findings reveal that under heterogeneous cluster sizes, the asymptotic variance includes a new term reflecting within-cluster dependence, which is overlooked when cluster sizes are presumed to be bounded. We propose valid approaches for bandwidth selection and inference, introduce estimators of the asymptotic variance, and demonstrate their consistency. In simulations, we verify the effectiveness of the cluster-robust bandwidth selection and show that the derived cluster-robust confidence interval improves the coverage ratio. We illustrate the application of these methods using a policy-targeting dataset in development economics.

本文提出了一类存在聚类依赖的非参数核回归的一般渐近理论。我们研究了非参数密度估计、Nadaraya-Watson核回归和局部线性估计。我们的理论适应了不断增长和异构的集群大小。导出渐近条件偏差和方差，建立一致相合性，证明渐近正态性。我们的研究结果表明，在异质簇大小下，渐近方差包含一个反映簇内依赖的新项，当假设簇大小有界时，该项被忽略。我们提出了带宽选择和推断的有效方法，引入了渐近方差的估计量，并证明了它们的一致性。通过仿真，验证了该算法的有效性，并表明所得到的簇鲁棒置信区间提高了覆盖比。我们使用发展经济学中的政策目标数据集来说明这些方法的应用。

引用次数: 0

Structural periodic vector autoregressions 结构周期向量自回归

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-09-18 DOI: 10.1016/j.jeconom.2025.106099

Daniel Dzikowski, Carsten Jentsch

While seasonality inherent to raw macroeconomic data is commonly removed by seasonal adjustment techniques before it is used for structural inference, this may distort valuable information in the data. As an alternative method to commonly used structural vector autoregressions (SVARs) for seasonally adjusted data, we propose to model potential periodicity in seasonally unadjusted (raw) data directly by structural periodic vector autoregressions (SPVARs). This approach does not only allow for periodically time-varying intercepts, but also for periodic autoregressive parameters and innovations variances. As this larger flexibility leads to an increased number of parameters, we propose linearly constrained estimation techniques. Moreover, based on SPVARs, we provide two novel identification schemes and propose a general framework for impulse response analyses that allows for direct consideration of seasonal patterns. We provide asymptotic theory for SPVAR estimators and impulse responses under flexible linear restrictions and introduce a test for seasonality in impulse responses. For the construction of confidence intervals, we discuss several residual-based (seasonal) bootstrap methods and prove their bootstrap consistency under different assumptions. A real data application shows that useful information about the periodic structure in the data may be lost when relying on common seasonal adjustment methods.

虽然原始宏观经济数据固有的季节性通常在用于结构推断之前通过季节调整技术去除，但这可能会扭曲数据中的宝贵信息。作为对季节性调整数据常用的结构向量自回归（SVARs）的替代方法，我们提出直接使用结构周期向量自回归（spvar）来模拟季节性未调整（原始）数据的潜在周期性。这种方法不仅允许周期性时变截距，而且允许周期性自回归参数和创新方差。由于这种更大的灵活性导致参数数量的增加，我们提出了线性约束估计技术。此外，基于spvar，我们提供了两种新的识别方案，并提出了一个允许直接考虑季节模式的脉冲响应分析的一般框架。给出了弹性线性约束下SPVAR估计量和脉冲响应的渐近理论，并引入了脉冲响应的季节性检验。对于置信区间的构造，我们讨论了几种基于残差的（季节）自举方法，并证明了它们在不同假设下的自举一致性。一个实际的数据应用表明，当依赖于常用的季节调整方法时，可能会丢失有关数据周期结构的有用信息。

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引用次数: 0

Misspecification-robust bootstrap t-test for irrelevant factor in linear stochastic discount factor models 线性随机折现因子模型中不相关因子的错误规格-稳健自举t检验

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-09-12 DOI: 10.1016/j.jeconom.2025.106097

Antoine A. Djogbenou , Ulrich Hounyo

This paper examines the applicability of the bootstrap approach to test for irrelevant risk factors that are potentially useless in misspecified linear stochastic discount factor (SDF) models. In the literature, the misspecification-robust inference with useless factors is known to give rise to nonstandard limiting distributions bounded stochastically to compute critical values. We show how and to what extent the wild bootstrap yields a more accurate approximation of the distribution of

t

-statistics when testing for an unpriced factor in the context of linear SDF models. Simulation experiments and empirical tests are also used to document the relevance of the bootstrap method.

本文研究了自举方法在测试不相关风险因素的适用性，这些风险因素在错误指定的线性随机贴现因子（SDF）模型中可能是无用的。在文献中，已知带有无用因素的错误规范鲁棒推断会导致随机有界计算临界值的非标准极限分布。我们展示了在线性SDF模型的背景下测试未定价因素时，野生自举如何以及在多大程度上产生更准确的t统计分布近似值。模拟实验和实证测试也被用来证明自举方法的相关性。

引用次数: 0

On-line detection of changes in the shape of intraday volatility curves 日内波动率曲线形状变化的在线检测

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-09-08 DOI: 10.1016/j.jeconom.2025.106089

Torben G. Andersen , Yingwen Tan , Viktor Todorov , Zhiyuan Zhang

We devise an on-line detector for temporal instability in the shape of average intraday volatility curves under a general semimartingale setup for the price-volatility dynamics. We adopt a block-based strategy to estimate volatility nonparametrically from the intraday observations over local time windows with asymptotically shrinking size. Our detector then tracks sequential changes in running means of the intraday volatility curve estimates. Asymptotic size and power properties of the detector follow from a weak form invariance principle, which is established under the strong mixing condition aligned with our semimartingale setup. Simulation and empirical results demonstrate good finite-sample performance of the proposed detection method.

在价格波动动力学的一般半鞅设置下，我们设计了一个以平均日内波动曲线形状的时间不稳定性在线检测器。我们采用一种基于块的策略，从局部时间窗口的日内观测中非参数地估计波动率，该窗口的尺寸渐近缩小。然后，我们的检测器跟踪日内波动曲线估计的运行方法的顺序变化。探测器的渐近大小和功率性质遵循弱形式不变性原理，该原理是在与我们的半鞅设置一致的强混合条件下建立的。仿真和实证结果表明，该方法具有良好的有限样本检测性能。

引用次数: 0

High dimensional factor analysis with weak factors 含弱因子的高维因子分析

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-08-30 DOI: 10.1016/j.jeconom.2025.106086

Jungjun Choi , Ming Yuan

This paper studies the principal components (PC) estimator for high dimensional approximate factor models with weak factors in that the factor loading (

Λ^{0}

) scales sublinearly in the number

N

of cross-section units, i.e.,

Λ^{0 ⊤} Λ^{0} / N^{α}

is positive definite in the limit for some

α \in (0, 1)

. While the consistency and asymptotic normality of these estimates are by now well known when the factors are strong, i.e.,

α = 1

, the statistical properties for weak factors remain less explored. Here, we show that the PC estimator maintains consistency and asymptotic normality for any

α \in (0, 1)

, provided suitable conditions regarding the dependence structure in the noise are met. This complements earlier result by Onatski (2012) that the PC estimator is inconsistent when

α = 0

, and the more recent work by Bai and Ng (2023) who established the asymptotic normality of the PC estimator when

α \in (1 / 2, 1)

. Our proof strategy integrates the traditional eigendecomposition-based approach for factor models with leave-one-out analysis similar in spirit to those used in matrix completion and other settings. This combination allows us to deal with factors weaker than the former and at the same time relax the incoherence and independence assumptions often associated with the later.

本文研究了具有弱因子的高维近似因子模型的主成分（PC）估计量，其中因子载荷（Λ0）在截面单元数N上呈次线性缩放，即Λ0， Λ0/Nα在某些α∈(0,1)的极限下是正定的。虽然这些估计的一致性和渐近正态性现在是众所周知的，当因素是强的，即，α=1，弱因素的统计性质仍然很少探索。在这里，我们证明了对于任意α∈(0,1)，只要满足噪声中相关结构的适当条件，PC估计量保持一致性和渐近正态性。这补充了Onatski（2012）早期的结果，即当α=0时PC估计量是不一致的，以及Bai和Ng（2023）最近的工作，他们建立了当α∈(1/2,1)时PC估计量的渐近正态性。我们的证明策略集成了传统的基于特征分解的因子模型方法，与在矩阵补全和其他设置中使用的类似精神的留一分析。这种组合使我们能够处理比前者更弱的因素，同时放松通常与后者相关的不连贯和独立性假设。

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引用次数: 0

On regression-adjusted imputation estimators of average treatment effects 平均处理效果的回归校正归因估计

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-08-26 DOI: 10.1016/j.jeconom.2025.106080

Zhexiao Lin , Fang Han

Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that combine imputation and regression adjustments are believed to be comparable to augmented inverse probability weighting. Accordingly, people for a long time conjectured that such estimators, while avoiding directly constructing the weights, are also doubly robust (Imbens, 2004; Stuart, 2010). Generalizing an earlier result of the authors (Lin et al., 2023), this paper formalizes this conjecture, showing that a large class of regression-adjusted imputation methods are indeed doubly robust for estimating average treatment effects. In addition, they are provably semiparametrically efficient as long as both the density and regression models are correctly specified. Notable examples of imputation methods covered by our theory include kernel matching, (weighted) nearest neighbor matching, local linear matching, and (honest) random forests.

使用估计的回归函数来计算缺失的潜在结果是估计因果效应的自然想法。在文献中，结合归算和回归调整的估计器被认为可与增广逆概率加权相媲美。因此，人们长期以来一直推测，这种估计器在避免直接构造权重的同时，也具有双重鲁棒性（Imbens, 2004; Stuart, 2010）。本文推广了作者的早期结果（Lin et al., 2023），形式化了这一猜想，表明大量经回归调整的归算方法对于估计平均处理效果确实具有双重鲁棒性。此外，只要正确指定密度模型和回归模型，它们就可以证明是半参数有效的。我们的理论所涵盖的插值方法的值得注意的例子包括核匹配，（加权）最近邻匹配，局部线性匹配和（诚实）随机森林。

引用次数: 0

Support vector decision making 支持向量决策

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-08-26 DOI: 10.1016/j.jeconom.2025.106087

Yixiao Sun

The paper develops a support vector machine (SVM) for binary decision-making within a utility framework. Given an information set, a decision-maker first predicts a binary outcome and then selects a binary action based on this prediction to maximize expected utility, where the utility function can depend on the action taken, observable covariates, and the binary outcome subsequently realized. The proposed maximum utility SVM differs from the traditional SVM in four key aspects. First, as a conceptual innovation, it incorporates the optimal cutoff function as a separate and special covariate. Second, there is a sign restriction on this special covariate. Third, it accounts for the dependence of the utility-induced loss on both the covariates and the binary outcome. Finally, it allows the margin to differ across different classes of outcomes. The paper proves that the proposed method is Bayes-consistent under the maximum utility criterion and establishes a finite-sample generalization bound. A simulation study shows that the proposed method outperforms existing methods under the data-generating processes considered in the literature.

本文开发了一种实用框架下的二元决策支持向量机（SVM）。给定一个信息集，决策者首先预测一个二元结果，然后根据该预测选择一个二元行为，以最大化预期效用，其中效用函数可能取决于所采取的行动、可观察的协变量和随后实现的二元结果。本文提出的最大效用支持向量机与传统支持向量机的区别在于四个关键方面。首先，作为一个概念创新，它将最优截止函数作为一个单独的特殊协变量。其次，这个特殊协变量有符号限制。第三，它解释了效用导致的损失对协变量和二元结果的依赖。最后，它允许边际在不同类别的结果中有所不同。证明了该方法在最大效用准则下是贝叶斯一致的，并建立了有限样本泛化界。仿真研究表明，在文献中考虑的数据生成过程下，该方法优于现有方法。

引用次数: 0

Shrinkage estimation of spatial panel data models with multiple structural breaks and a multifactor error structure 具有多重结构断裂和多因素误差结构的空间面板数据模型的收缩估计

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-08-22 DOI: 10.1016/j.jeconom.2025.106082

Siqi Dai , Yongmiao Hong , Haiqi Li , Chaowen Zheng

This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a significant challenge. To address the dual challenges of endogeneity and time heterogeneity, we propose a novel penalized generalized method of moments estimation with common correlated effects (PGMM-CCEX). Specifically, this method addresses the endogeneity issue by utilizing the cross-sectional averages of regressors as factor proxies when constructing the internal instrumental variables, while employing adaptive group fused Lasso to detect multiple structural breaks. The PGMM-CCEX method consistently estimates both the number of breaks and their locations. Furthermore, the post-PGMM-CCEX regime-specific coefficient estimates are consistent and asymptotically follow a normal distribution. Notably, the method remains valid even when factor loadings vary over time, whether synchronously or asynchronously with the parameters of interest. Monte Carlo simulations confirm the satisfactory finite-sample performance of the proposed PGMM-CCEX method. Finally, we apply our method to analyze cross-country economic growth across 106 countries from 1970 to 2019, revealing the time-varying influence of key economic factors on growth dynamics.

本文对空间面板数据模型进行了研究，该模型具有多因素误差结构，并且空间滞后变量和解释变量的系数都存在多重结构断裂。虽然广泛的研究已经解决了面板数据的横截面依赖性，包括在单一框架内整合空间和因素结构的方法，但很少有研究考虑时变模型参数，实现一致的估计仍然是一个重大挑战。为了解决内质性和时间异质性的双重挑战，我们提出了一种具有共同相关效应的惩罚性广义矩估计方法（PGMM-CCEX）。具体而言，该方法通过在构建内部工具变量时使用回归量的横截面平均值作为因子代理来解决内生性问题，同时使用自适应群体融合Lasso来检测多个结构断裂。PGMM-CCEX方法一致地估计断裂的数量和位置。此外，pgmm - ccex后的制度特异性系数估计是一致的，并渐近地服从正态分布。值得注意的是，即使因子加载随时间而变化（无论是与感兴趣的参数同步还是异步），该方法仍然有效。蒙特卡罗仿真验证了所提出的PGMM-CCEX方法的有限样本性能。最后，我们运用该方法分析了106个国家1970 - 2019年的跨国经济增长，揭示了关键经济因素对增长动态的时变影响。

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引用次数: 0

Identification and inference for semiparametric single index transformation models 半参数单指标变换模型的辨识与推理

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-08-21 DOI: 10.1016/j.jeconom.2025.106084

Yingqian Lin , Yundong Tu

This paper considers a semiparametric single index model in which the dependent variable is subject to a nonparametric transformation. The model has the form

G_{0} (Y) = g_{0} (X^{⊤} θ_{0}) + e

, where

X

is a random vector of regressors,

Y

is the dependent variable and

e

is the random noise, the monotonic function

G_{0}

, the smooth function

g_{0}

and the index vector

θ_{0}

are all unknown. This model is quite general in the sense that it nests many popular regression models as special cases. We first propose identification strategies for the three unknown quantities, based on which estimators are then constructed. The kernel density weighted average derivative estimator of

δ

(proportional to

θ_{0}

) has a

V

-statistic representation and its asymptotical normality is established under the small bandwidth asymptotics. The kernel estimator of the transformation function

G_{0}

is a functional of the conditional distribution estimator of

Y

given

X^{⊤} θ_{0}

and is shown to be

\sqrt{n}

-consistent and asymptotically normal. The sieve estimator of

g_{0}

is shown to enjoy the standard nonparametric asymptotic properties. A specification test for the single index structure and extension to allow for endogeneous regressors are also developed. In addition, data-driven choices of the smoothing parameters are discussed. Simulation results illustrate the nice finite sample performance of the proposed estimators and specification test. An empirical application to studying the impact of family income on child achievement demonstrates the practical merits of the proposed model.

本文研究了一类因变量经过非参数变换的半参数单指标模型。模型的形式为G0(Y)= G0(X θ0)+e，其中X是回归量的随机向量，Y是因变量，e是随机噪声，单调函数G0、平滑函数G0和指标向量θ0都是未知的。这个模型是非常通用的，因为它嵌套了许多流行的回归模型作为特殊情况。我们首先提出了三个未知量的识别策略，然后在此基础上构造了估计量。δ（与θ0成比例）的核密度加权平均导数估计量具有v统计量表示，并在小带宽渐近条件下建立了其渐近正态性。变换函数G0的核估计量是给定X θ0的条件分布估计量Y的一个泛函，并且被证明是n一致的和渐近正态的。证明了g0的筛估计量具有标准的非参数渐近性质。还开发了单指标结构的规范测试和允许内禀回归量的扩展。此外，还讨论了数据驱动下平滑参数的选择。仿真结果表明所提出的估计器具有良好的有限样本性能和规格测试。通过对家庭收入对儿童学业成就影响的实证研究，证明了该模型的实用价值。

{"title":"Identification and inference for semiparametric single index transformation models","authors":"Yingqian Lin , Yundong Tu","doi":"10.1016/j.jeconom.2025.106084","DOIUrl":"10.1016/j.jeconom.2025.106084","url":null,"abstract":"<div><div>This paper considers a semiparametric single index model in which the dependent variable is subject to a nonparametric transformation. The model has the form <span><math><mrow><msub><mrow><mi>G</mi></mrow><mrow><mn>0</mn></mrow></msub><mrow><mo>(</mo><mi>Y</mi><mo>)</mo></mrow><mo>=</mo><msub><mrow><mi>g</mi></mrow><mrow><mn>0</mn></mrow></msub><mrow><mo>(</mo><msup><mrow><mi>X</mi></mrow><mrow><mo>⊤</mo></mrow></msup><msub><mrow><mi>θ</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>)</mo></mrow><mo>+</mo><mi>e</mi></mrow></math></span>, where <span><math><mi>X</mi></math></span> is a random vector of regressors, <span><math><mi>Y</mi></math></span> is the dependent variable and <span><math><mi>e</mi></math></span> is the random noise, the monotonic function <span><math><msub><mrow><mi>G</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, the smooth function <span><math><msub><mrow><mi>g</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> and the index vector <span><math><msub><mrow><mi>θ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> are all unknown. This model is quite general in the sense that it nests many popular regression models as special cases. We first propose identification strategies for the three unknown quantities, based on which estimators are then constructed. The kernel density weighted average derivative estimator of <span><math><mi>δ</mi></math></span> (proportional to <span><math><msub><mrow><mi>θ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) has a <span><math><mi>V</mi></math></span>-statistic representation and its asymptotical normality is established under the small bandwidth asymptotics. The kernel estimator of the transformation function <span><math><msub><mrow><mi>G</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> is a functional of the conditional distribution estimator of <span><math><mi>Y</mi></math></span> given <span><math><mrow><msup><mrow><mi>X</mi></mrow><mrow><mo>⊤</mo></mrow></msup><msub><mrow><mi>θ</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></math></span> and is shown to be <span><math><msqrt><mrow><mi>n</mi></mrow></msqrt></math></span>-consistent and asymptotically normal. The sieve estimator of <span><math><msub><mrow><mi>g</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> is shown to enjoy the standard nonparametric asymptotic properties. A specification test for the single index structure and extension to allow for endogeneous regressors are also developed. In addition, data-driven choices of the smoothing parameters are discussed. Simulation results illustrate the nice finite sample performance of the proposed estimators and specification test. An empirical application to studying the impact of family income on child achievement demonstrates the practical merits of the proposed model.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106084"},"PeriodicalIF":4.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hedonic prices and quality adjusted price indices powered by AI 由人工智能驱动的享乐价格和质量调整价格指数

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-08-20 DOI: 10.1016/j.jeconom.2025.106052

P. Bajari , Z. Cen , V. Chernozhukov , M. Manukonda , S. Vijaykumar , J. Wang , R. Huerta , J. Li , L. Leng , G. Monokroussos , S. Wang

We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or “features”) from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon’s data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with

R^{2}

ranging from 80% to 90%. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.

我们开发了经验模型，有效地处理大量非结构化产品数据（文本、图像、价格、数量），以产生准确的享乐价格估计和衍生指数。为了实现这一点，我们使用深度神经网络从描述和图像中生成抽象的产品属性（或“特征”）。然后使用这些属性来估计享乐价格函数。为了证明这种方法的有效性，我们将模型应用于亚马逊的第一方服装销售数据，并估计享乐价格。所得模型具有非常高的样本外预测精度，R2范围从80%到90%。最后，我们构建了基于人工智能的hedonic Fisher价格指数，并将其与CPI和其他电子指数进行对比。

引用次数: 0

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