Journal of Econometrics最新文献

英文中文

Individual welfare analysis: Random quasilinear utility, independence, and confidence bounds

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105927

Junlong Feng , Sokbae Lee

We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-good-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a novel confidence set under an independence restriction. This confidence set is computationally simple and robust to weak instruments, nonlinearity, and partial identification. The validity of the confidence set is guaranteed by our new results on the joint limiting distribution of the independence test by Chatterjee (2021). These results together with the confidence set may have applications beyond welfare analysis. Monte Carlo simulations and two empirical applications on gasoline and food demand demonstrate the effectiveness of our method.

我们为个人层面的福利分析引入了一个新框架。它建立在一个具有准线性效用函数的连续需求参数模型之上，允许存在异质性系数和未观察到的个人--好的--水平偏好冲击。我们通过求解一个可扩展的优化问题，该问题受独立限制下的一个新置信度集的约束，从而得到在任何置信度水平下，假设价格上涨导致的个人层面消费者福利损失的界限。该置信集计算简单，对弱工具、非线性和部分识别具有稳健性。我们关于 Chatterjee（2021 年）独立性检验联合极限分布的新结果保证了置信集的有效性。这些结果和置信集的应用范围可能超出福利分析。蒙特卡罗模拟以及汽油和食品需求的两个经验应用证明了我们方法的有效性。

引用次数: 0

Shrinkage estimators for periodic autoregressions

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105937

Richard Paap, Philip Hans Franses

A periodic autoregression [PAR] is a seasonal time series model where the autoregressive parameters vary over the seasons. A drawback of PAR models is that the number of parameters increases dramatically when the number of seasons gets large. Hence, one needs many periods with intra-seasonal data to be able to get reliable parameter estimates. Therefore, these models are rarely applied for weekly or daily observations. In this paper we propose shrinkage estimators which shrink the periodic autoregressive parameters to a common value determined by the data. We derive the asymptotic properties of these estimators in case of a quadratic penalty and we illustrate the bias–variance trade-off. Empirical illustrations show that shrinkage improves forecasting with PAR models.

周期自回归 [PAR] 是一种季节性时间序列模型，其自回归参数随季节变化。PAR 模型的一个缺点是，当季节数变多时，参数数会急剧增加。因此，我们需要许多具有季节内数据的时期，才能获得可靠的参数估计。因此，这些模型很少用于周或日观测。在本文中，我们提出了收缩估计器，它可以将周期性自回归参数收缩到一个由数据决定的共同值。我们推导了这些估计器在二次惩罚情况下的渐近特性，并说明了偏差与方差的权衡。经验说明表明，缩减可以改善 PAR 模型的预测效果。

引用次数: 0

Estimating and testing for smooth structural changes in moment condition models 估计和测试矩条件模型中的平稳结构变化

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105896

Haiqi Li , Jin Zhou , Yongmiao Hong

Numerous studies have been devoted to estimating and testing for moment condition models. Most existing studies assume that structural parameters are either fixed or change abruptly over time. This study considers estimating and testing for smooth structural changes in moment condition models where the data-generating process is locally stationary. A novel local generalized method of moments estimator and its boundary-corrected counterpart are proposed to estimate the smoothly changing parameters. Consistency and asymptotic normality are established, and an optimal weighting matrix and its consistent estimator are obtained. Moreover, we propose a consistent test to detect both smooth changes and abrupt breaks, as well as a consistent test for a parametric functional form of time-varying parameters. The tests are asymptotically pivotal and do not require prior information about the alternatives. Monte Carlo simulation studies show that the proposed estimators and tests have superior finite-sample performance. In an empirical application, we document the time-varying features of the risk aversion parameter in an asset pricing model, indicating that investors’ risk aversion is counter-cyclical.

已有大量研究致力于估计和测试力矩条件模型。现有的大多数研究都假定结构参数要么固定不变，要么随时间发生突然变化。本研究考虑的是在数据生成过程是局部静止的情况下，对力矩条件模型中的平滑结构变化进行估计和测试。本文提出了一种新颖的局部广义矩估计方法及其边界校正对应方法，用于估计平稳变化的参数。建立了一致性和渐近正态性，并获得了最优加权矩阵及其一致性估计器。此外，我们还提出了检测平稳变化和突然中断的一致性检验，以及时变参数的参数函数形式的一致性检验。这些检验都是渐近枢轴检验，不需要关于备选方案的先验信息。蒙特卡罗模拟研究表明，所提出的估计和检验具有卓越的有限样本性能。在实证应用中，我们记录了资产定价模型中风险规避参数的时变特征，表明投资者的风险规避是反周期的。

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引用次数: 0

From LATE to ATE: A Bayesian approach 从LATE到ATE：贝叶斯方法

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105895

Isaac M. Opper

We develop a Bayesian model that produces a posterior distribution of the marginal treatment effect (MTE) function. The method provides researchers with a principled way to extrapolate from the observed moments using flexible assumptions, thereby allowing researchers to generate plausible ranges of important and potentially policy-relevant quantities of interest. We then use the model to propose a natural decomposition of the posterior variance into “statistical uncertainty,” i.e., variance that stems from the imprecise estimation of the observed moments, and “extrapolation uncertainty,” i.e., variance that stems from uncertainty in how to extrapolate away from the observed moments. We conclude by showing that under our preferred priors, even in an experiment as large as the Oregon Health Insurance Experiment, the main source of uncertainty in the ATE comes from uncertainty in the true values of the observed moments.

我们开发了一个贝叶斯模型，该模型产生了边际治疗效果（MTE）函数的后验分布。该方法为研究人员提供了一种原则性的方法，可以使用灵活的假设从观察到的时刻进行推断，从而使研究人员能够产生重要的和潜在的政策相关的兴趣量的合理范围。然后，我们使用该模型提出将后验方差自然分解为“统计不确定性”，即源于对观察到的时刻的不精确估计的方差，以及“外推不确定性”，即源于如何从观察到的时刻外推的不确定性的方差。我们的结论是，在我们的首选先验下，即使在俄勒冈健康保险实验这样大的实验中，ATE的不确定性的主要来源来自观察到的力矩的真实值的不确定性。

引用次数: 0

Multivariate spatiotemporal models with low rank coefficient matrix 低秩系数矩阵多变量时空模型

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105897

Dan Pu , Kuangnan Fang , Wei Lan , Jihai Yu , Qingzhao Zhang

Multivariate spatiotemporal data arise frequently in practical applications, often involving complex dependencies across cross-sectional units, time points and multivariate variables. In the literature, few studies jointly model the dependence in three dimensions. To simultaneously model the cross-sectional, dynamic and cross-variable dependence, we propose a multivariate reduced-rank spatiotemporal model. By imposing the low-rank assumption on the spatial influence matrix, the proposed model achieves substantial dimension reduction and has a nice interpretation, especially for financial data. Due to the innate endogeneity, we propose the quasi-maximum likelihood estimator (QMLE) to estimate the unknown parameters. A ridge-type ratio estimator is also developed to determine the rank of the spatial influence matrix. We establish the asymptotic distribution of the QMLE and the rank selection consistency of the ridge-type ratio estimator. The proposed methodology is further illustrated via extensive simulation studies and two applications to a stock market dataset and an air pollution dataset.

多变量时空数据在实际应用中经常出现，往往涉及横截面单位、时间点和多变量之间的复杂依赖关系。在文献中，很少有研究从三个维度对依赖关系进行联合建模。为了同时模拟横截面、动态和跨变量依赖关系，我们提出了一种多变量降低秩时空模型。通过对空间影响矩阵施加低秩假设，所提出的模型大大降低了维度，并具有很好的解释性，尤其适用于金融数据。由于存在先天的内生性，我们提出了准最大似然估计法（QMLE）来估计未知参数。我们还开发了一种脊型比率估计器来确定空间影响矩阵的秩。我们建立了 QMLE 的渐近分布和脊型比率估计器的秩选择一致性。我们通过大量的模拟研究以及股票市场数据集和空气污染数据集的两个应用，进一步说明了所提出的方法。

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引用次数: 0

Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models 半参数时间序列模型的伪方差准极大似然估计

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105894

Mirko Armillotta , Paolo Gorgi

We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a parametric pseudo-variance that can contain parametric restrictions with respect to the conditional expectation. The specification of the pseudo-variance and the parametric restrictions follow naturally in observation-driven models with bounds in the support of the observable process, such as count processes and double-bounded time series. We derive the asymptotic properties of the estimators and a validity test for the parameter restrictions. We show that the results remain valid irrespective of the correct specification of the pseudo-variance. The key advantage of the restricted estimators is that they can achieve higher efficiency compared to alternative quasi-likelihood methods that are available in the literature. Furthermore, the testing approach can be used to build specification tests for parametric time series models. We illustrate the practical use of the methodology in a simulation study and two empirical applications featuring integer-valued autoregressive processes, where assumptions on the dispersion of the thinning operator are formally tested, and autoregressions for double-bounded data with application to a realized correlation time series.

我们为一类半参数时间序列模型提出了一种新的估计方法，在这类模型中，条件期望是通过参数函数建模的。所提出的这一类估计方法基于高斯准似然比函数，它依赖于参数伪方差的指定，而参数伪方差可以包含与条件期望相关的参数限制。伪方差的规范和参数限制自然地遵循在可观测过程的支持中有界的观测驱动模型，如计数过程和双界时间序列。我们推导了估计量的渐近特性和参数限制的有效性检验。我们证明，无论伪方差的规范是否正确，结果仍然有效。限制估计器的主要优势在于，与文献中的其他准似然法相比，它们可以实现更高的效率。此外，该检验方法还可用于建立参数时间序列模型的规格检验。我们在一项模拟研究和两个经验应用中说明了该方法的实际应用：整数值自回归过程，其中对稀疏算子的离散性假设进行了正式检验；双约束数据的自回归，并应用于已实现的相关时间序列。

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引用次数: 0

Validating approximate slope homogeneity in large panels 验证大型面板中的近似斜率同质性

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105898

Tim Kutta , Holger Dette

In this paper, we introduce new inference methods for slope homogeneity in large regression panels. While most existing tests are developed for the hypothesis of slope homogeneity (equality of all individual slopes), we propose to test the more realistic relaxation of approximate slope homogeneity (similarity of all slopes). We present new test statistics for dense and sparse alternatives to approximate homogeneity. In the dense setting, the main focus of this paper, we develop statistics that converge to pivotal limits even under simultaneous temporal and intersectional dependence. We also demonstrate uniform consistency of these statistics against large classes of local alternatives. As a complementary diagnostic tool, we propose tests against sparse alternatives that are sensitive to excessive heterogeneity in a minority of slopes. Such tests can play an important role in the analysis of populations with diverse but small subgroups. A simulation study and a data example underline the usefulness of our approach.

本文介绍了大型回归面板中斜率同质性的新推断方法。现有的大多数检验方法都是针对斜率同质性（所有单个斜率相等）的假设开发的，而我们则建议检验更现实的近似斜率同质性（所有斜率相似）的放宽假设。我们针对近似同质性的密集型和稀疏型替代方案提出了新的检验统计量。在本文的重点--密集设置中，我们开发了即使在同时存在时间和交叉依赖性的情况下也能收敛到中枢极限的统计量。我们还证明了这些统计量与大量局部替代方案的一致性。作为补充诊断工具，我们提出了针对稀疏替代方案的检验方法，这些检验方法对少数斜坡的过度异质性非常敏感。这种检验在分析具有多样化但规模较小的子群体时可以发挥重要作用。一项模拟研究和一个数据实例强调了我们的方法的实用性。

引用次数: 0

Consistent causal inference for high-dimensional time series 高维时间序列的一致因果推理

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105902

Francesco Cordoni, Alessio Sancetta

A methodology for high-dimensional causal inference in a time series context is introduced. Time series dynamics are captured by a Gaussian copula, and estimation of the marginal distribution of the data is not required. The procedure can consistently identify the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high-dimensional variables, under sparsity conditions. Identification of the causal relations is in the form of a directed acyclic graph, which is equivalent to identifying the structural VAR model for the transformed variables. As illustrative applications, we consider the impact of supply-side oil shocks on the economy and the causal relations between aggregated variables constructed from the limit order book for four stock constituents of the S&P500.

本文介绍了一种在时间序列背景下进行高维因果推断的方法。时间序列动态由高斯共轭捕捉，不需要对数据的边际分布进行估计。在稀疏性条件下，该程序可以一致地识别描述过程动态的参数以及可能的高维变量之间的条件因果关系。因果关系的识别采用有向无环图的形式，相当于识别转换变量的结构 VAR 模型。作为示例应用，我们考虑了供应方石油冲击对经济的影响，以及根据 S&P500 指数四只股票成分股的限价订单簿构建的汇总变量之间的因果关系。

引用次数: 0

Variable selection in high dimensional linear regressions with parameter instability 具有参数不稳定性的高维线性回归的变量选择

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105900

Alexander Chudik , M. Hashem Pesaran , Mahrad Sharifvaghefi

This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also investigated, and in-sample fit of the selected regression is shown to have the oracle property. The theoretical results support the use of unweighted observations at the selection stage of OCMT, whilst applying down-weighting of observations only at the forecasting stage. Monte Carlo and empirical applications show that OCMT without down-weighting at the selection stage yields smaller mean squared forecast errors compared to Lasso, Adaptive Lasso, and boosting.

考虑了考虑参数不稳定性的变量选择问题。它区分了与目标变量相关的信号和伪信号变量，以及与目标变量不相关的噪声变量，并研究了Chudik等人（2018）在参数不满足下提出的One Covariate at a Time Multiple Testing （OCMT）方法的渐近性质。建立了OCMT继续渐近地选择一个包含所有信号而不包含噪声变量的近似模型。对后选择回归的性质也进行了研究，所选回归的样本内拟合显示出具有oracle属性。理论结果支持在OCMT的选择阶段使用未加权的观测值，而只在预测阶段使用降权的观测值。蒙特卡罗和经验应用表明，与Lasso、Adaptive Lasso和boosting相比，在选择阶段不降权的OCMT产生更小的均方预测误差。

引用次数: 0

GLS under monotone heteroskedasticity 单调异方差下的 GLS

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105899

Yoichi Arai , Taisuke Otsu , Mengshan Xu

The generalized least square (GLS) is one of the most basic tools in regression analyses. A major issue in implementing the GLS is estimation of the conditional variance function of the error term, which typically requires a restrictive functional form assumption for parametric estimation or smoothing parameters for nonparametric estimation. In this paper, we propose an alternative approach to estimate the conditional variance function under nonparametric monotonicity constraints by utilizing the isotonic regression method. Our GLS estimator is shown to be asymptotically equivalent to the infeasible GLS estimator with knowledge of the conditional error variance, and involves only some tuning to trim boundary observations, not only for point estimation but also for interval estimation or hypothesis testing. Simulation studies and an empirical example illustrate excellent finite sample performances of the proposed method.

广义最小二乘法（GLS）是回归分析中最基本的工具之一。实现 GLS 的一个主要问题是估计误差项的条件方差函数，参数估计通常需要限制性函数形式假设，非参数估计则需要平滑参数。在本文中，我们提出了另一种方法，即利用等调回归法来估计非参数单调性约束下的条件方差函数。结果表明，我们的 GLS 估计器在渐近上等同于知道条件误差方差的不可行 GLS 估计器，而且只需进行一些调整来修剪边界观测值，不仅适用于点估计，也适用于区间估计或假设检验。仿真研究和一个经验实例说明了所提方法的出色有限样本性能。

引用次数: 0

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Journal of Econometrics

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