Journal of Econometrics最新文献

英文中文

Jump detection in high-frequency order prices 高频订单价格的跳跃检测

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-12 DOI: 10.1016/j.jeconom.2025.106133

Markus Bibinger , Nikolaus Hautsch , Alexander Ristig

We propose methods to infer jumps of a semi-martingale, which describes long-term price dynamics, based on discrete, noisy, high-frequency observations. Different to the classical model of additive, centered market microstructure noise, we consider one-sided microstructure noise for order prices in a limit order book.

We develop methods to estimate, locate and test for jumps using local minima of best ask quotes. We provide a local jump test and show that we can consistently estimate jump sizes and jump times. One main contribution is a global test for jumps. We establish the asymptotic properties and optimality of this test. We derive the asymptotic distribution of a maximum statistic under the null hypothesis of no jumps based on extreme value theory. We prove consistency under the alternative hypothesis. The rate of convergence for local alternatives is determined and shown to be much faster than optimal rates for the standard market microstructure noise model. This allows the identification of smaller jumps. In the process, we establish uniform consistency for spot volatility estimation under one-sided noise. Online jump detection based on the new approach is shown to achieve a speed advantage compared to standard methods applied to mid quotes.

A simulation study sheds light on the finite-sample implementation and properties of the new approach and draws a comparison to a popular method for market microstructure noise. We showcase how our new approach helps to improve jump detection in an empirical analysis of intra-daily limit order book data.

我们提出的方法来推断跳跃的半鞅，描述长期价格动态，基于离散的，有噪声的，高频的观察。不同于经典的附加的、中心的市场微观结构噪声模型，我们考虑了限价订单中订单价格的片面微观结构噪声。我们开发了使用最佳报价的局部最小值来估计、定位和测试跳跃的方法。我们提供了一个局部跳跃测试，并证明我们可以一致地估计跳跃大小和跳跃时间。一个主要贡献是对跳跃的全球测试。我们建立了这个检验的渐近性质和最优性。基于极值理论，导出了无跳跃零假设下最大统计量的渐近分布。我们在备择假设下证明了一致性。确定并证明了局部替代方案的收敛速度比标准市场微观结构噪声模型的最佳速度快得多。这允许识别较小的跳跃。在此过程中，我们建立了单边噪声下的点波动率估计的一致一致性。与应用于中引号的标准方法相比，基于新方法的在线跳转检测显示具有速度优势。仿真研究揭示了新方法的有限样本实现和特性，并与市场微观结构噪声的流行方法进行了比较。我们展示了我们的新方法如何帮助改善跳跃检测在每日限价订单数据的实证分析。

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引用次数: 0

Identification and estimation of partial effects in nonlinear semiparametric panel models 非线性半参数面板模型部分效应的辨识与估计

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105860

Laura Liu , Alexandre Poirier , Ji-Liang Shiu

Average partial effects (APEs) are often not point identified in panel models with unrestricted unobserved individual heterogeneity, as in binary response panel models with fixed effects and logistic errors for example. This lack of point identification occurs despite the identification of these models’ common coefficients. We provide a unified framework to establish the point identification of various partial effects in a wide class of nonlinear semiparametric models under an index sufficiency assumption on the unobserved heterogeneity, even when the error distribution is unspecified and non-stationary. This assumption does not impose parametric restrictions on the unobserved heterogeneity and idiosyncratic errors. We also present partial identification results when the support condition fails. We then propose three-step semiparametric estimators for APEs, average structural functions, and average marginal effects, and show their consistency and asymptotic normality. Finally, we illustrate our approach in a study of determinants of married women’s labor supply.

在具有不受限制的未观察到的个体异质性的面板模型中，平均部分效应（ape）通常不能被点识别，例如在具有固定效应和逻辑误差的二元响应面板模型中。尽管识别了这些模型的公共系数，但仍然会出现缺乏点识别的情况。我们提供了一个统一的框架，在未观测到异质性的指标充分性假设下，即使误差分布未指定和非平稳，也可以在一类广泛的非线性半参数模型中建立各种局部效应的点识别。这一假设没有对未观察到的异质性和特质误差施加参数限制。给出了支撑条件失效时的部分辨识结果。然后，我们提出了类似性、平均结构函数和平均边际效应的三步半参数估计，并证明了它们的一致性和渐近正态性。最后，我们在已婚妇女劳动力供给决定因素的研究中说明了我们的方法。

引用次数: 0

Causal inference in network experiments: Regression-based analysis and design-based properties 网络实验中的因果推理：基于回归分析和基于设计的特性

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2025.106119

Mengsi Gao , Peng Ding

Network experiments are powerful tools for studying spillover effects, which avoid endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. We show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the interference patterns under network experiments. We first recall a well-known result that the Hájek estimator is numerically identical to the coefficient from the weighted-least-squares fit based on the inverse probability of the exposure mapping. Moreover, we demonstrate that the regression-based approach offers three notable advantages: its ease of implementation, the ability to derive standard errors through the same regression fit, and the potential to integrate covariates into the analysis to improve efficiency. Recognizing that the regression-based network-robust covariance estimator can be anti-conservative under nonconstant effects, we propose an adjusted covariance estimator to improve the empirical coverage rates.

网络实验是研究溢出效应的有力工具，它通过对网络上的单位随机分配处理方法来避免内生性。然而，在不强加强建模假设的情况下正确分析网络实验是非常重要的。我们表明，如果仔细指定回归函数和鲁棒标准误差以适应网络实验下的干扰模式，基于回归的点估计器和标准误差可以有很强的理论保证。我们首先回顾一个众所周知的结果，即Hájek估计量在数值上与基于暴露映射逆概率的加权最小二乘拟合的系数相同。此外，我们证明了基于回归的方法提供了三个显著的优势：易于实现，通过相同的回归拟合获得标准误差的能力，以及将协变量集成到分析中以提高效率的潜力。认识到基于回归的网络鲁棒协方差估计在非常数效应下可能是反保守的，我们提出了一个调整的协方差估计来提高经验覆盖率。

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引用次数: 0

Factor and idiosyncratic VAR volatility matrix models for heavy-tailed high-frequency financial observations 重尾高频金融观察的因子和特质VAR波动矩阵模型

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2025.106129

Minseok Shin , Donggyu Kim , Yazhen Wang , Jianqing Fan

This paper introduces a novel process for both factor and idiosyncratic volatility matrices whose eigenvalues follow the vector auto-regressive (VAR) model. We call it the factor and idiosyncratic VAR (FIVAR) model. The FIVAR model accounts for the dynamics of the factor and idiosyncratic volatilities and includes many parameters. In addition, many empirical studies have shown that high-frequency stock returns and volatilities often exhibit heavy tails. To handle these two problems simultaneously, we propose a penalized optimization procedure with a truncation scheme for parameter estimation. We apply the proposed parameter estimation procedure to predicting large volatility matrices and establish its asymptotic properties.

本文介绍了一种求解特征值遵循向量自回归模型的因子波动矩阵和特质波动矩阵的新方法。我们称之为因子与特质VAR （FIVAR）模型。FIVAR模型考虑了因素和特殊波动率的动态，包括许多参数。此外，许多实证研究表明，高频股票收益和波动性往往表现出沉重的尾部。为了同时处理这两个问题，我们提出了一种带有截断方案的参数估计惩罚优化程序。我们将所提出的参数估计方法应用于预测大波动矩阵，并建立了其渐近性质。

引用次数: 0

Loss aversion and the welfare ranking of policy interventions 损失规避与政策干预的福利排序

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2023.105643

Sergio Firpo , Antonio F. Galvao , Martyna Kobus , Thomas Parker , Pedro Rosa-Dias

This paper develops theoretical criteria and econometric methods to rank policy interventions in terms of welfare when individuals are loss-averse. Our new criterion for “loss aversion-sensitive dominance” defines a weak partial ordering of the distributions of policy-induced gains and losses. It applies to the class of welfare functions which model individual preferences with non-decreasing and loss-averse attitudes towards changes in outcomes. We also develop new semiparametric statistical methods to test loss aversion-sensitive dominance in practice, using nonparametric plug-in estimates; these allow inference to be conducted through a special resampling procedure. Since point-identification of the distribution of policy-induced gains and losses may require strong assumptions, we extend our comparison criteria, test statistics, and resampling procedures to the partially-identified case. We illustrate our methods with a simple empirical application to the welfare comparison of alternative income support programs in the US.

本文提出了一些理论标准和计量经济学方法，以便在个人规避损失的情况下对政策干预进行福利排序。我们新提出的 "对损失规避敏感的优势 "标准定义了政策导致的收益和损失分布的弱局部排序。它适用于以对结果变化的非递减和损失规避态度的个人偏好为模型的福利函数类别。我们还开发了新的半参数统计方法，在实践中利用非参数插件估计来检验对损失规避敏感的支配地位；这些方法允许通过特殊的重采样程序进行推断。由于政策引起的收益和损失分布的点识别可能需要很强的假设，我们将比较标准、检验统计和重采样程序扩展到部分识别的情况。我们通过一个简单的实证应用来说明我们的方法，即美国替代收入支持计划的福利比较。

引用次数: 0

Identification of time-varying counterfactual parameters in nonlinear panel models 识别非线性面板模型中的时变反事实参数

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2023.105639

Irene Botosaru, Chris Muris

We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our results do not require parametric assumptions on the error terms or time-homogeneity on the outcome equation. Our main results focus on static models, with a set of results applying to models without any exogeneity conditions. We show that the survival distribution of counterfactual outcomes is identified (point or partial) in this class of models. This parameter is a building block for most partial and marginal effects of interest in applied practice that are based on the average structural function as defined by Blundell and Powell (2003, 2004). To the best of our knowledge, ours are the first results on average partial and marginal effects for binary choice and ordered choice models with two-way fixed effects and non-logistic errors.

我们建立了一个通用框架，用于识别一类具有固定效应和时间效应的非线性半参数面板模型中的反事实参数。我们的方法适用于离散结果（如双向固定效应二元选择）或连续结果（如删减回归）模型，以及离散或连续回归因子。我们的结果不要求误差项的参数假设或结果方程的时间同质性。我们的主要结果集中于静态模型，其中一组结果适用于没有任何外生性条件的模型。我们表明，在这类模型中，反事实结果的生存分布是确定的（点或部分）。该参数是应用实践中大多数局部效应和边际效应的基础，这些应用实践基于布伦德尔和鲍威尔（2003，2004）定义的平均结构函数。据我们所知，我们的研究是关于具有双向固定效应和非逻辑误差的二元选择和有序选择模型的平均部分效应和边际效应的第一个结果。

引用次数: 0

Estimation of wage inequality in the UK by quantile regression with censored selection 通过带删减选择的量子回归估算英国的工资不平等情况

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105733

Songnian Chen , Nianqing Liu , Hanghui Zhang , Yahong Zhou

Arellano and Bonhomme (2017) proposed a quantile selection model to study the evolution of wage inequality in the UK, which specifies a binary selection equation and requires an exclusion restriction. In this paper we propose a quantile selection model with a more informative censored selection equation. Following Heckman (1974, 1979), Heckman and Sedlacek (1990), and Blundell et al. (2003), among others, the employment selection equation could be equivalently modeled by an hours worked equation through a censored selection. In our model, both the outcome and selection equations are specified as semiparametric quantile regressions, and no exclusion restriction is needed. We propose a quantile selection estimator that was applied to study wage inequality using the same data as in Arellano and Bonhomme (2017). Among our major findings based on our method, after adjusting for sample selection, (i) there is significant negative selection among males, in contrast to the finding of significant positive selection by Arellano and Bonhomme (2017); (ii) similar to Arellano and Bonhomme (2017), we also find positive selection for females, but our selection effects are more significant than those of Arellano and Bonhomme (2017) (See Section 5 for more details); (iii) the gender wage gap has remained large and accounting for selection leads to much smaller reduction in the gender wage gap over time, compared with the observed wage distribution and that of Arellano and Bonhomme (2017).

Arellano和Bonhomme（2017）提出了一个分位数选择模型来研究英国工资不平等的演变，该模型指定了一个二元选择方程，并需要排除限制。在本文中，我们提出了一个分位数选择模型与一个更有信息量的审查选择方程。继Heckman (1974,1979)， Heckman和Sedlacek(1990)，以及Blundell等人（2003）等人之后，就业选择方程可以通过审查选择等效地用工作时间方程来建模。在我们的模型中，结果方程和选择方程都被指定为半参数分位数回归方程，并且不需要排除限制。我们提出了一个分位数选择估计器，使用与Arellano和Bonhomme（2017）相同的数据来研究工资不平等。根据我们的方法，在调整样本选择后，我们的主要发现是：(i)与Arellano和Bonhomme（2017）的发现相比，男性之间存在显著的负选择；（ii）与Arellano and Bonhomme（2017）相似，我们也发现了女性的正向选择，但我们的选择效应比Arellano and Bonhomme（2017）更显著（详见第5节）；（iii）与观察到的工资分布以及Arellano和Bonhomme（2017）相比，性别工资差距仍然很大，考虑到选择，随着时间的推移，性别工资差距的缩小幅度要小得多。

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引用次数: 0

Higher-order refinements of small bandwidth asymptotics for density-weighted average derivative estimators 密度加权平均导数估计的小带宽渐近的高阶改进

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105855

Matias D. Cattaneo , Max H. Farrell , Michael Jansson , Ricardo P. Masini

The density weighted average derivative (DWAD) of a regression function is a canonical parameter of interest in economics. Classical first-order large sample distribution theory for kernel-based DWAD estimators relies on tuning parameter restrictions and model assumptions that imply an asymptotic linear representation of the point estimator. These conditions can be restrictive, and the resulting distributional approximation may not be representative of the actual sampling distribution of the statistic of interest. In particular, the approximation is not robust to bandwidth choice. Small bandwidth asymptotics offers an alternative, more general distributional approximation for kernel-based DWAD estimators that allows for, but does not require, asymptotic linearity. The resulting inference procedures based on small bandwidth asymptotics were found to exhibit superior finite sample performance in simulations, but no formal theory justifying that empirical success is available in the literature. Employing Edgeworth expansions, this paper shows that small bandwidth asymptotic approximations lead to inference procedures with higher-order distributional properties that are demonstrably superior to those of procedures based on asymptotic linear approximations.

回归函数的密度加权平均导数（DWAD）是经济学中一个典型的参数。经典的基于核的DWAD估计的一阶大样本分布理论依赖于调整参数限制和模型假设，这意味着点估计量的渐近线性表示。这些条件可能是限制性的，并且所得的分布近似可能不能代表感兴趣的统计量的实际抽样分布。特别是，该近似对带宽选择不具有鲁棒性。小带宽渐近为基于核的DWAD估计器提供了另一种更一般的分布近似，它允许但不要求渐近线性。由此产生的基于小带宽渐近的推理程序在模拟中显示出优越的有限样本性能，但没有正式的理论证明在文献中可以获得经验成功。利用Edgeworth展开式，本文证明了小带宽渐近逼近导致的推理过程具有明显优于基于渐近线性逼近的推理过程的高阶分布性质。

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引用次数: 0

Measuring the effects of segregation in the presence of social spillovers: A nonparametric approach 衡量存在社会溢出的隔离效应：一种非参数方法

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2025.106006

Bryan S. Graham , Guido W. Imbens , Geert Ridder

In this paper we nonparametrically analyze the effects of reallocating individuals across social groups in the presence of social spillovers. Individuals are either ‘high’ or ‘low’ types. Own outcomes may vary with the fraction of high types in one’s social group. We characterize the average outcome and inequality effects of small increases in segregation by type. We also provide a measure of average spillover strength. We generalize the setup used by Benabou (1996) and others to study sorting in the presence of social spillovers by incorporating unobserved individual- and group-level heterogeneity. We relate our reallocation estimands to this theory. For each estimand we provide conditions for nonparametric identification, propose estimators, and characterize their large sample properties. We also consider the social planner’s problem. We illustrate our approach by studying the effects of sex segregation in classrooms on mathematics achievement.

本文非参数地分析了社会溢出效应下个体在不同社会群体间的再配置效应。个体分为“高”型和“低”型。自己的结果可能会随着一个人的社会群体中高类型的比例而变化。我们按类型描述了隔离小幅度增加的平均结果和不平等效应。我们还提供了平均溢出强度的度量。我们推广了Benabou（1996）和其他人使用的设置，通过纳入未观察到的个人和群体水平异质性来研究存在社会溢出的分类。我们把我们的再分配估计与这个理论联系起来。对于每个估计，我们提供了非参数识别的条件，提出了估计量，并描述了它们的大样本性质。我们还考虑了社会规划师的问题。我们通过研究教室性别隔离对数学成绩的影响来说明我们的方法。

引用次数: 0

Improved estimation of semiparametric dynamic copula models with filtered nonstationarity 滤波非平稳半参数动态耦合模型的改进估计

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics

Pub Date : 2025-11-01 DOI: 10.1016/j.jeconom.2024.105739

Xiaohong Chen , Bo Wang , Zhijie Xiao , Yanping Yi

This paper considers estimation of short-run dynamics in time series that contain a nonstationary component. We assume that appropriate preliminary methods can be applied to the observed time series to separate short-run elements from long-run slowly evolving secular components, and focus on estimation of the short-run dynamics based on the filtered data. We use a flexible copula-generated Markov model to capture the nonlinear temporal dependence in the short-run component and study estimation of the copula model. Using the rescaled empirical distribution of the filtered data as an estimator of the marginal distribution, Chen et al. (2022) proposed a simple, yet flexible, two-step estimation procedure for the copula model. The two-step estimator works well when the tail dependence is small. However, simulations reveal that the two-step estimator may be biased in finite samples in the presence of tail dependence. To improve the performance of short-term dynamic analysis in the presence of tail dependence, we propose in this paper a pseudo sieve maximum likelihood (PSML) procedure to jointly estimate the residual copula parameter and the invariant density of the filtered residuals. We establish the root-

n

consistency and asymptotic distribution of the PSML estimator of any smooth functional of the residual copula parameter and invariant residual density. We further show that the PSML estimator of the residual copula parameter is asymptotically normal, with the limiting distribution independent of the filtration. Simulations reveal that in the presence of strong tail dependence, compared to the two-step estimates of Chen et al. (2022), the proposed PSML estimates have smaller biases and smaller mean squared errors even in small samples. Applications to nonstationary macro-finance and climate time series are presented.

本文研究了包含非平稳分量的时间序列的短期动态估计问题。我们假设适当的初步方法可以应用于观测时间序列，将短期因素与长期缓慢演变的长期因素分开，并重点关注基于过滤数据的短期动态估计。我们使用一个灵活的copula生成的马尔可夫模型来捕捉短期分量中的非线性时间依赖性，并研究了copula模型的估计。Chen等人（2022）利用过滤后数据的重标经验分布作为边际分布的估计量，提出了一种简单而灵活的copula模型两步估计方法。当尾部相关性较小时，两步估计方法效果较好。然而，仿真结果表明，在有限的样本中，由于存在尾部依赖性，两步估计器可能存在偏差。为了提高存在尾依赖性的短期动态分析的性能，本文提出了一种伪筛极大似然（PSML）方法来联合估计残差耦合参数和滤波后残差的不变密度。建立了残差共轭参数和不变残差密度的任意光滑泛函的PSML估计量的根n相合性和渐近分布。进一步证明了残差联结参数的PSML估计量是渐近正态的，其极限分布与滤波无关。模拟表明，与Chen等人（2022）的两步估计相比，在存在强尾依赖性的情况下，即使在小样本中，所提出的PSML估计也具有较小的偏差和均方误差。介绍了该方法在非平稳宏观金融和气候时间序列中的应用。

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