Journal of Econometrics最新文献

英文中文

From LATE to ATE: A Bayesian approach

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.

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

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

Variable selection in high dimensional linear regressions with parameter instability

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.

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

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

Inference in predictive quantile regressions 预测性量化回归的推论

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-10-01 DOI: 10.1016/j.jeconom.2024.105875

Alex Maynard , Katsumi Shimotsu , Nina Kuriyama

This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC)

t

-statistic in terms of functionals of Ornstein–Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has a reliable size in small samples and good power. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors – the dividend price ratio, earnings price ratio, and book-to-market ratio – to predict the median, shoulders, and tails of the stock return distribution.

本文研究了预测性回归因子具有近单位根时的预测性量化回归推断。我们根据 Ornstein-Uhlenbeck 过程的函数推导出了量级回归估计器及其异方差和自相关一致（HAC）t 统计量的渐近分布。然后，我们提出了一种转换-完全修正（FM）的量子预测性检验。当预测因子具有近似单位根时，所提出的检验采用 FM 式修正，并对局部到单位参数进行 Bonferroni 约束。当预测因子的最大根位于静态范围内时，它将切换到标准预测性量化回归检验，临界值略显保守。模拟结果表明，该检验在小样本中具有可靠的规模和良好的功率。我们采用这一新方法检验了三个常用的、高度持久的内生滞后估值回归因子--股息价格比、盈利价格比和账面市值比--预测股票收益率分布的中位数、肩部和尾部的能力。

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引用次数: 0

On the spectral density of fractional Ornstein–Uhlenbeck processes 论分数奥恩斯坦-乌伦贝克过程的谱密度

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2024-10-01 DOI: 10.1016/j.jeconom.2024.105872

Shuping Shi , Jun Yu , Chen Zhang

This paper introduces a novel and easy-to-implement method for accurately approximating the spectral density of discretely sampled fractional Ornstein–Uhlenbeck (fOU) processes. The method offers a substantial reduction in approximation error, particularly within the rough region of the fractional parameter

H \in (0, 0.5)

. This approximate spectral density has the potential to enhance the performance of estimation methods and hypothesis testing that make use of spectral densities. We introduce the approximate Whittle maximum likelihood (AWML) method for discretely sampled fOU processes, utilizing the approximate spectral density, and demonstrate that the AWML estimator exhibits properties of consistency and asymptotic normality when

H \in (0, 1)

, akin to the conventional Whittle maximum likelihood method. Through extensive simulation studies, we show that AWML outperforms existing methods in terms of estimation accuracy in finite samples. We then apply the AWML method to the trading volume of 40 financial assets. Our empirical findings reveal that the estimated Hurst parameters for these assets fall within the range of 0.10 to 0.21, indicating a rough dynamic.

本文介绍了一种新颖且易于实施的方法，用于精确逼近离散采样分数奥恩斯坦-乌伦贝克（fOU）过程的谱密度。该方法大大减少了近似误差，尤其是在分数参数 H∈(0,0.5) 的粗糙区域内。这种近似谱密度有可能提高使用谱密度的估计方法和假设检验的性能。我们利用近似谱密度，为离散采样的 fOU 过程引入了近似惠特尔最大似然法（AWML），并证明当 H∈(0,1)时，AWML 估计器与传统惠特尔最大似然法类似，具有一致性和渐近正态性。通过大量的模拟研究，我们表明 AWML 在有限样本中的估计精度优于现有方法。然后，我们将 AWML 方法应用于 40 种金融资产的交易量。我们的实证研究结果表明，这些资产的赫斯特参数估计值在 0.10 到 0.21 之间，表明其具有粗略的动态性。

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