Journal of Econometrics最新文献

英文中文

Phase transitions in nonparametric regressions 非参数回归中的阶段转换

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2023.105640

Ying Zhu

When the unknown regression function of a single variable is known to have derivatives up to the

(γ + 1)

th order bounded in absolute values by a common constant everywhere or a.e. (i.e.,

(γ + 1)

th degree of smoothness), the minimax optimal rate of the mean integrated squared error (MISE) is stated as

{(\frac{1}{n})}^{\frac{2 γ + 2}{2 γ + 3}}

in the literature. This paper shows that: (i) if

n \leq {(γ + 1)}^{2 γ + 3}

, the minimax optimal MISE rate is

\frac{log n}{n log (log n)}

and the optimal degree of smoothness to exploit is roughly

max ((\frac{log n}{2 log (log n)}), 1)

; (ii) if

n > {(γ + 1)}^{2 γ + 3}

, the minimax optimal MISE rate is

{(\frac{1}{n})}^{\frac{2 γ + 2}{2 γ + 3}}

and the optimal degree of smoothness to exploit is

γ + 1

The fundamental contribution of this paper is a set of metric entropy bounds we develop for smooth function classes. Some of our bounds are original, and some of them improve and/or generalize the ones in the literature (e.g., Kolmogorov and Tikhomirov, 1959). Our metric entropy bounds allow us to show phase transitions in the minimax optimal MISE rates associated with some commonly seen smoothness classes as well as non-standard smoothness classes, and can also be of independent interest outside the nonparametric regression problems.

当已知单变量的未知回归函数在(γ+1)阶以下的导数绝对值处处或a.e.（即(γ+1)阶平滑度）受一个公共常数约束时，文献中平均综合平方误差（MISE）的最小最优率为1n2γ+22γ+3。本文指出(i) 如果 n≤γ+12γ+3，最小最优 MISE 率为 lognnlog(logn)，利用的最优平滑度大致为 maxlogn2loglogn,1；(ii) 如果 n>γ+12γ+3，最小最优 MISE 率为 1n2γ+22γ+3，利用的最优平滑度为 γ+1。本文的基本贡献在于我们为光滑函数类开发的一组度量熵边界。我们的一些边界是原创的，一些则改进和/或概括了文献中的边界（如 Kolmogorov 和 Tikhomirov，1959 年）。我们的度量熵边界允许我们展示与一些常见的平滑度类以及非标准平滑度类相关的最小最优 MISE 率的相变，也可以在非参数回归问题之外引起独立的兴趣。

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引用次数: 0

Nonlinear budget set regressions in random utility models: Theory and application to taxable income 随机效用模型中的非线性预算集回归：理论及其在应税所得中的应用

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105859

Soren Blomquist , Anil Kumar , Che-Yuan Liang , Whitney K. Newey

This paper is about the nonparametric regression of a choice variable on a nonlinear budget set under utility maximization with general heterogeneity, i.e. in the random utility model (RUM). We show that utility maximization and convex budget sets make this regression three dimensional with a more parsimonious specification than previously derived. We show that nonconvexities in the budget set will have little effect on these results in important cases. We characterize all the restrictions of utility maximization on the budget set regression and show how to check these restrictions in applications. We formulate budget set effects that can be identified by this regression and give automatic debiased machine learners of these effects. We consider use of control functions to allow for endogeneity. Throughout we take as the main example the effect of taxes on taxable income including accounting for productivity growth. In an application to Swedish data we find the taxable income elasticity of a change in the slope of each segment to be

. 52

, that the regression satisfies the restrictions of utility maximization at the values chosen for over 95% of observations, and that a productivity growth rate we estimate is close to other estimates.

本文研究了具有一般异质性的非线性预算集在效用最大化条件下的选择变量的非参数回归问题，即随机效用模型（RUM）。我们证明了效用最大化和凸预算集使这种回归具有比以前推导的更简洁的三维规格。我们表明，在重要的情况下，预算集的非凸性对这些结果的影响很小。我们描述了预算集回归中效用最大化的所有限制，并展示了如何在应用中检查这些限制。我们制定了可以通过这种回归识别的预算集效应，并给出了这些效应的自动去偏见机器学习器。我们考虑使用控制函数来允许内生性。在整个过程中，我们以税收对应税收入的影响为主要例子，包括对生产率增长的考虑。在对瑞典数据的应用中，我们发现每段斜率变化的应税收入弹性为。52，回归满足95%以上的观测值所选择的效用最大化的限制，并且我们估计的生产率增长率接近其他估计值。

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引用次数: 0

Addressing endogeneity issues in a spatial autoregressive model using copulas 空间自回归模型的内生性问题

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2025.106106

Yanli Lin , Yichun Song

This paper develops a novel, instrument-free semiparametric copula framework for a spatial autoregressive (SAR) model to address endogeneity arising from an endogenous spatial weights matrix, endogenous regressors, or both. Moving beyond conventional Gaussian copulas, we develop a flexible estimator based on the Student’s

t

copula with an unknown degrees-of-freedom (df) parameter, which nests the Gaussian case and allows the data to determine the presence of tail dependence. We propose a sieve maximum likelihood estimator (SMLE) that jointly estimates all structural, copula, and nonparametric marginal parameters, and establish that this joint estimator is consistent, asymptotically normal, and – unlike prevailing multi-stage copula-correction methods – semiparametrically efficient. Monte Carlo simulations highlight the flexibility of our approach, showing that copula misspecification inflates bias and variance, whereas joint estimation improves efficiency. In an empirical application to regional productivity spillovers, we find evidence of tail dependence and demonstrate that our method offers a credible alternative to approaches that rely on hard-to-verify excluded instruments.

本文为空间自回归（SAR）模型开发了一种新的、无仪器的半参数copula框架，以解决由内源性空间权重矩阵、内源性回归量或两者引起的内生性问题。超越传统的高斯耦合，我们开发了一个基于未知自由度（df）参数的学生t耦合的灵活估计器，该估计器嵌套在高斯情况下，并允许数据确定尾部依赖的存在。我们提出了一个筛子最大似然估计（SMLE），它联合估计所有的结构、联结和非参数的边际参数，并建立了这个联合估计是一致的、渐近正态的，并且-不同于流行的多阶段联结校正方法-半参数有效的。蒙特卡罗模拟突出了我们方法的灵活性，表明联结错误规范会使偏差和方差膨胀，而联合估计则提高了效率。在对区域生产力溢出的实证应用中，我们发现了尾部依赖的证据，并证明我们的方法为依赖于难以验证的排除工具的方法提供了可靠的替代方法。

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引用次数: 0

Weighted-average quantile regression 加权平均分位数回归

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2025.106115

Denis Chetverikov , Yukun Liu , Aleh Tsyvinski

In this paper, we introduce the weighted-average quantile regression model. We argue that this model is of interest in many applied settings and develop an estimator for parameters of this model. We show that our estimator is

\sqrt{T}

-consistent and asymptotically normal under weak conditions, where

T

is the sample size. We demonstrate the usefulness of our estimator in two empirical settings. First, we study the factor structures of the expected shortfalls of the industry portfolios. Second, we study inequality and social welfare dependence on individual characteristics.

本文引入了加权平均分位数回归模型。我们认为该模型在许多应用环境中都是有意义的，并为该模型的参数开发了一个估计器。我们证明了我们的估计量在弱条件下是T一致和渐近正态的，其中T是样本量。我们在两个经验设置中证明了我们的估计器的有用性。首先，我们研究了行业投资组合预期缺口的因素结构。其次，我们研究了个体特征的不平等和社会福利依赖。

引用次数: 0

Test of neglected heterogeneity in dyadic models 检验二元模型中被忽视的异质性

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105736

Jinyong Hahn , Hyungsik Roger Moon , Ruoyao Shi

We develop a Lagrange Multiplier (LM) test of neglected heterogeneity in dyadic models. The test statistic is derived by modifying Breusch and Pagan (1980)’s test. We establish the asymptotic distribution of the test statistic under the null using a novel martingale construction. We also consider the power of the LM test in generic panel models. Even though the test is motivated by random effects, we show that it has a power for detecting fixed effects as well. Finally, we examine how the estimation noise of the maximum likelihood estimator affects the asymptotic distribution of the test under the null, and show that such a noise may be ignored in large samples.

我们开发了一个拉格朗日乘数（LM）检验在二元模型中被忽视的异质性。检验统计量是通过修改Breusch和Pagan（1980）的检验而得到的。我们用一种新的鞅构造建立了检验统计量在零下的渐近分布。我们还考虑了LM测试在通用面板模型中的作用。尽管测试是由随机效应驱动的，但我们表明它也具有检测固定效应的能力。最后，我们研究了极大似然估计量的估计噪声如何影响零下检验的渐近分布，并表明这种噪声在大样本中是可以忽略的。

引用次数: 0

Introduction to the Annals Issue in Honor of James Powell 《纪念詹姆斯·鲍威尔年鉴》简介

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2025.106051

Bryan Graham , Hidehiko Ichimura , Michael Jansson , Shakeeb Khan

引用次数: 0

Increasing the power of moment-based tests 增加基于矩的测试的能力

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2025.106008

Tiemen Woutersen

This paper shows how to increase the power of the Hansen (1982) test for the case where only a subset of the exclusion restrictions is used. The ‘ignored’ exclusion restrictions are used to derive a new estimator for the covariance matrix, which has a different probability limit than the usual one when the model is false. If the null hypothesis is true, then the proposed test has the same distribution as the existing ones in large samples. If the hypothesis is false, then the proposed test statistic is larger with probability approaching one as the sample size increases in several important examples. Simulations show that the improvement can be substantial. As the Hansen (1982) test is very popular in empirical work, including testing the validity of Euler equations, we expect the current results to be useful as well.

本文展示了如何在只使用排除限制的一个子集的情况下增加Hansen（1982）检验的威力。利用“忽略”的排除限制推导出一个新的协方差矩阵估计量，该估计量在模型为假时具有不同于通常的概率极限。如果零假设成立，则提议的检验在大样本中具有与现有检验相同的分布。如果假设为假，那么在几个重要示例中，随着样本量的增加，所建议的检验统计量更大，概率接近1。仿真结果表明，改进的效果是显著的。由于Hansen（1982）测试在实证工作中非常流行，包括测试欧拉方程的有效性，我们期望当前的结果也很有用。

引用次数: 0

Statistical inference for the low dimensional parameters of linear regression models in the presence of high-dimensional data: An orthogonal projection approach 高维数据存在下线性回归模型低维参数的统计推断：一种正交投影方法

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105851

Cheng Hsiao , Qiankun Zhou

We consider the estimation and statistical inference for low dimensional parameters for a regression model with covariates whose dimension increases with sample size. We suggest a computationally simple one stage orthogonal projection approach to estimate the low dimensional parameters under strict or approximate sparsity conditions. The orthogonal projection approach is simple to implement and the inference for the low dimensional parameters is straightforward to derive whether the high dimensional function is linear or nonlinear. It also avoids the complicated regularization bias issues commonly associated with two stage estimation methods. Monte Carlo simulations and empirical applications are also conducted to investigate the finite sample performance of the proposed estimator vs the double/debiased estimator of Belloni et al. (2014) and Chernozhukov et al. (2018).

我们考虑了一个随样本量增加的协变量回归模型的低维参数的估计和统计推断。我们提出了一种计算简单的单阶段正交投影法来估计严格或近似稀疏性条件下的低维参数。正交投影法实现简单，对低维参数的推断可以直接导出高维函数是线性的还是非线性的。它还避免了通常与两阶段估计方法相关的复杂的正则化偏差问题。还进行了蒙特卡罗模拟和经验应用，以研究所提出的估计量与Belloni等人（2014）和Chernozhukov等人（2018）的双/去偏估计量的有限样本性能。

引用次数: 0

Making distributionally robust portfolios feasible in high dimension 使分布鲁棒投资组合在高维上可行

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-10-21 DOI: 10.1016/j.jeconom.2025.106118

Ruike Wu , Yanrong Yang , Han Lin Shang , Huanjun Zhu

Robust estimation for modern portfolio selection on a large set of assets becomes more important due to the large deviation of empirical inference on big data. We propose a distributionally robust methodology for high-dimensional mean–variance portfolio problems, aiming to select an optimal conservative portfolio allocation by considering distributional uncertainty. With the help of factor structure, we extend the distributionally robust mean–variance problem investigated by Blanchet et al. (2022) to the high-dimensional scenario and transform it to a new penalized risk minimization problem. Furthermore, we propose a data-adaptive method to quantify both the uncertainty size and the lowest acceptable target return. Since the selection of these quantities requires knowledge of certain unknown population parameters, we further develop an estimation procedure, and establish its corresponding asymptotic consistency. Our Monte-Carlo simulation results show that the estimated uncertainty size and target return from the proposed procedure are very close to the corresponding oracle level, and the newly proposed robust portfolio achieves high out-of-sample Sharpe ratio. Finally, we conduct empirical studies based on the components of the S&P 500 index and the Russell 2000 index to demonstrate the superior return–risk performance of our proposed portfolio selection, in comparison with various existing strategies.

由于大数据上的经验推断偏差较大，对现代大资产组合选择的稳健估计变得更加重要。针对高维均值方差投资组合问题，提出了一种分布鲁棒性方法，在考虑分布不确定性的情况下选择最优的保守投资组合配置。借助因子结构，我们将Blanchet等（2022）研究的分布鲁棒均值-方差问题扩展到高维场景，并将其转化为新的惩罚风险最小化问题。此外，我们还提出了一种数据自适应方法来量化不确定性大小和最低可接受目标收益。由于这些量的选择需要知道某些未知的总体参数，我们进一步开发了一个估计过程，并建立了相应的渐近一致性。蒙特卡罗模拟结果表明，该方法估计的不确定性大小和目标收益率非常接近于相应的oracle水平，并且新提出的稳健投资组合具有较高的样本外夏普比。最后，我们基于标准普尔500指数和罗素2000指数的组成部分进行了实证研究，以证明与各种现有策略相比，我们提出的投资组合选择具有优越的回报-风险表现。

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引用次数: 0

Shrinkage methods for treatment choice 收缩处理方法的选择

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-10-16 DOI: 10.1016/j.jeconom.2025.106117

Takuya Ishihara , Daisuke Kurisu

This study examines the problem of determining whether to treat individuals based on observed covariates. The most common decision rule is the conditional empirical success (CES) rule proposed by Manski (2004), which assigns individuals to treatments that yield the best experimental outcomes conditional on the observed covariates. Conversely, using shrinkage estimators, which shrink unbiased but noisy preliminary estimates toward the average of these estimates, is a common approach in statistical estimation problems because it is well-known that shrinkage estimators may have smaller mean squared errors than unshrunk estimators. Inspired by this idea, we propose a computationally tractable shrinkage rule that selects the shrinkage factor by minimizing an upper bound of the maximum regret. Then, we compare the maximum regret of the proposed shrinkage rule with those of the CES and pooling rules when the space of conditional average treatment effects (CATEs) is correctly specified or misspecified. Our theoretical results demonstrate that the shrinkage rule performs well in many cases and these findings are further supported by numerical experiments. Specifically, we show that the maximum regret of the shrinkage rule can be strictly smaller than those of the CES and pooling rules in certain cases when the space of CATEs is correctly specified. In addition, we find that the shrinkage rule is robust against misspecification of the space of CATEs. Finally, we apply our method to experimental data from the National Job Training Partnership Act Study.

本研究探讨了是否根据观察到的协变量对个体进行治疗的问题。最常见的决策规则是Manski（2004）提出的条件经验成功（CES）规则，该规则根据观察到的协变量将个体分配给产生最佳实验结果的治疗。相反，使用收缩估计器，将无偏但有噪声的初步估计缩小到这些估计的平均值，是统计估计问题中的常用方法，因为众所周知，收缩估计器可能比未收缩估计器具有更小的均方误差。受此启发，我们提出了一种计算上易于处理的收缩规则，该规则通过最小化最大遗憾的上界来选择收缩因子。然后，我们比较了在条件平均处理效应（CATEs）空间正确指定或错误指定时，所提出的收缩规则与ce和池化规则的最大遗憾。理论结果表明，在许多情况下，收缩规律都是有效的，数值实验结果进一步支持了这一结论。具体来说，我们证明了在某些情况下，当正确指定CATEs的空间时，收缩规则的最大遗憾可以严格小于CES和池化规则的最大遗憾。此外，我们发现收缩规则对于CATEs空间的错配具有鲁棒性。最后，我们将我们的方法应用于国家职业培训伙伴关系法案研究的实验数据。

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