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A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks 动态时空随机波动模型及其在环境风险中的应用

Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.11.002

Philipp Otto, Osman Doğan, Süleyman Taşpınar

A dynamic spatiotemporal stochastic volatility (SV) model is introduced, incorporating explicit terms accounting for spatial, temporal, and spatiotemporal spillover effects. Alongside these features, the model encompasses time-invariant site-specific factors, allowing for differentiation in volatility levels across locations. The statistical properties of an outcome variable within this model framework are examined, revealing the induction of spatial dependence in the outcome variable. Additionally, a Bayesian estimation procedure employing the Markov Chain Monte Carlo (MCMC) approach, complemented by a suitable data transformation, is presented. Simulation experiments are conducted to assess the performance of the proposed Bayesian estimator. Subsequently, the model is applied in the domain of environmental risk modeling, addressing the scarcity of empirical studies in this field. The significance of climate variation studies is emphasized, illustrated by an analysis of local air quality in Northern Italy during 2021, which underscores pronounced spatial and temporal clusters and increased uncertainties/risks during the winter season compared to the summer season.

介绍了一个动态时空随机波动(SV)模型，该模型包含了考虑空间、时间和时空溢出效应的明确术语。除了这些特征外，该模型还包含了时不变的地点特定因素，从而允许不同地点的波动水平存在差异。在这个模型框架内的结果变量的统计特性进行了检查，揭示了在结果变量的空间依赖性的诱导。此外，还提出了一种采用马尔可夫链蒙特卡罗(MCMC)方法的贝叶斯估计过程，并辅以适当的数据转换。通过仿真实验来评估所提出的贝叶斯估计器的性能。随后，将该模型应用于环境风险建模领域，解决了该领域实证研究的不足。通过对2021年意大利北部当地空气质量的分析，强调了气候变化研究的重要性，该分析强调了与夏季相比，冬季明显的时空聚集性和不确定性/风险增加。

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引用次数: 2

Consistent estimation of panel data sample selection models 面板数据样本选择模型的一致性估计

Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.11.003

Sergi Jiménez-Martín, José M. Labeaga, Majid al Sadoon

The properties of classical panel data estimators including fixed effect, first-differences, random effects, and generalized method of moments-instrumental variables estimators in both static as well as dynamic panel data models are investigated under sample selection. The correlation of the unobserved errors is shown not to be sufficient for the inconsistency of these estimators. A necessary condition for this to arise is the presence of common (and/or non-independent) non-deterministic covariates in the selection and outcome equations. When both equations do not have covariates in common and independent of each other, the fixed effects, and random effects estimators in static models with exogenous covariates are consistent. Furthermore, the first-differenced generalized method of moments estimator uncorrected for sample selection as well as the instrumental variables estimator uncorrected for sample selection are both consistent for autoregressive models even with endogenous covariates. The same results hold when both equations have no covariates in common but are correlated once we account for such correlation. Under the same circumstances, the system generalized method of moments estimator adding more moments from the levels equation has moderate bias. Alternatively, when both equations have common covariates the appropriate correction method is suggested. Serial correlation of the errors being a key determinant for that choice. The finite sample properties of the proposed estimators are evaluated using a Monte Carlo study. Two empirical illustrations are provided.

在样本选择的条件下，研究了静态和动态面板数据模型中经典面板数据估计量的性质，包括固定效应、第一差分、随机效应和广义矩量方法-工具变量估计量。未观测误差的相关性不足以解释这些估计的不一致性。出现这种情况的必要条件是在选择和结果方程中存在共同(和/或非独立)非确定性协变量。当两个方程没有共同且相互独立的协变量时，具有外源性协变量的静态模型中的固定效应和随机效应估计量是一致的。此外，对于自回归模型，即使存在内源性协变量，一阶差分广义矩估计法和工具变量估计法在样本选择上都是一致的。当两个方程没有共同的协变量，但一旦我们考虑到这种相关性，就会产生相同的结果。在相同的情况下，从水平方程中加入更多矩的系统广义矩估计方法具有中等的偏差。或者，当两个方程有共同的协变量时，建议适当的修正方法。误差的序列相关性是该选择的关键决定因素。利用蒙特卡罗方法对所提估计量的有限样本性质进行了评估。给出了两个实证例证。

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引用次数: 0

Robust Clustering with Normal Mixture Models: A Pseudo β-Likelihood Approach 正态混合模型的鲁棒聚类:一种伪β似然方法

Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.10.004

Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh

As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from being an ill-posed optimization problem). A robust alternative to the ordinary likelihood approach for this estimation problem is proposed which performs simultaneous estimation and data clustering and leads to subsequent anomaly detection. To invoke robustness, the methodology based on the minimization of the density power divergence (or alternatively, the maximization of the β-likelihood) is utilized under suitable constraints. An iteratively reweighted least squares approach has been followed in order to compute the proposed estimators for the component means (or equivalently cluster centers) and component dispersion matrices which leads to simultaneous data clustering. Some exploratory techniques are also suggested for anomaly detection, a problem of great importance in the domain of statistics and machine learning. The proposed method is validated with simulation studies under different set-ups; it performs competitively or better compared to the popular existing methods like K-medoids, TCLUST, trimmed K-means and MCLUST, especially when the mixture components (i.e., the clusters) share regions with significant overlap or outlying clusters exist with small but non-negligible weights (particularly in higher dimensions). Two real datasets are also used to illustrate the performance of the newly proposed method in comparison with others along with an application in image processing. The proposed method detects the clusters with lower misclassification rates and successfully points out the outlying (anomalous) observations from these datasets.

与其他估计场景一样，在正常的混合设置中，基于似然的估计对于模型错误规范和异常值的存在是高度非鲁棒的(除了是一个不适定的优化问题)。针对该估计问题，提出了一种替代普通似然方法的鲁棒替代方法，该方法可以同时进行估计和数据聚类，并导致随后的异常检测。为了调用鲁棒性，在适当的约束条件下利用了基于密度功率散度最小化(或者，β-似然最大化)的方法。采用迭代重加权最小二乘方法来计算分量均值(或等价的聚类中心)和分量弥散矩阵的估计量，从而导致数据同时聚类。在统计和机器学习领域中，异常检测是一个非常重要的问题。通过不同设置下的仿真研究验证了该方法的有效性;与流行的现有方法(如k - mediids, TCLUST，修剪K-means和MCLUST)相比，它的性能具有竞争力或更好，特别是当混合成分(即簇)共享具有显著重叠的区域或外围簇存在较小但不可忽略的权重时(特别是在高维中)。用两个真实的数据集来说明新提出的方法与其他方法的性能以及在图像处理中的应用。该方法可以检测出错误分类率较低的聚类，并成功地指出这些数据集中的外围(异常)观测值。

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引用次数: 3

Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels 动态异质面板长期关系的混合Bewley估计

Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.11.001

Alexander Chudik, M. Hashem Pesaran, Ron P. Smith

Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small sample performance of PB compared to the existing estimators in the literature, namely PMG, panel dynamic OLS (PDOLS), and panel fully-modified OLS (FMOLS). Application of two bias-correction methods and a bootstrapping of critical values to conduct inference robust to cross-sectional dependence of errors are also considered. The utility of the PB estimator is illustrated in an empirical application to the aggregate consumption function.

通过对自回归分布滞后模型的变换，提出了一种具有异质短期动态的动态面板长期系数池Bewley (PB)估计方法。PB估计量与广泛使用的PMG (Pooled Mean Group)估计量具有直接可比性，并被证明是一致的和渐近正态的。蒙特卡罗模拟表明，与文献中现有的估计器(即PMG、面板动态OLS (pols)和面板全修正OLS (FMOLS))相比，PB具有良好的小样本性能。还考虑了两种偏差校正方法的应用和临界值的自举来对误差的横截面依赖性进行鲁棒推断。PB估计器的效用在总消费函数的经验应用中得到说明。

引用次数: 0

Networks in risk spillovers: A multivariate GARCH perspective 风险溢出中的网络：多元GARCH视角

IF 1.9 Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2020.12.003

Monica Billio , Massimiliano Caporin , Lorenzo Frattarolo , Loriana Pelizzon

A spatiotemporal approach is proposed for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and a new bilateral Multivariate GARCH specification is introduced. The covariance stationarity and identification of the model is studied, developing the quasi-maximum-likelihood estimator and analysing its consistency and asymptotic normality. Further, it is shown how to isolate risk channels and it is discussed how to compute target exposure in order to reduce the system variance. An empirical analysis on Euro-area sovereign credit default swap data indicates that Italy and Ireland are key players in spreading risk, France and Portugal are major risk receivers, and Spain’s non-trivial role as a risk middleman is uncovered.

基于可观察金融网络，提出了一种利用时变邻近矩阵建模风险溢出的时空方法，并引入了一种新的双边多变量GARCH规范。研究了模型的协方差平稳性和辨识性，建立了拟极大似然估计量，分析了其一致性和渐近正态性。此外，还展示了如何隔离风险渠道，并讨论了如何计算目标暴露以减少系统方差。对欧元区主权信用违约掉期数据的实证分析表明，意大利和爱尔兰是分散风险的关键参与者，法国和葡萄牙是主要的风险接受者，西班牙作为风险中间人的重要作用被揭示。

引用次数: 0

Bayesian Analysis of ARCH-M model with a dynamic latent variable 具有动态潜变量的ARCH-M模型的贝叶斯分析

IF 1.9 Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.10.001

Zefang Song , Xinyuan Song , Yuan Li

A time-varying coefficient ARCH-in-mean (ARCH-M) model with a dynamic latent variable that follows an AR process is considered. The joint model extends the existing ARCH-M model by considering a dynamic structure of latent variable for examining a latent effect on the time-varying risk–return relationship. A Bayesian approach coped with Markov Chain Monte Carlo algorithm is developed to perform the joint estimation of model parameters and the latent variable. Simulation results show that the proposed inference procedure performs satisfactorily. An application of the proposed method to a financial study of the Chinese stock market is presented.

研究了一个具有动态潜变量的时变系数ARCH-M模型。联合模型通过考虑潜在变量的动态结构来扩展现有的ARCH-M模型，以检验对时变风险-收益关系的潜在影响。提出了一种与马尔可夫链蒙特卡罗算法相结合的贝叶斯方法，对模型参数和潜在变量进行联合估计。仿真结果表明，所提出的推理过程性能良好。介绍了该方法在中国股票市场金融研究中的应用。

引用次数: 1

Multi-objective optimisation of split-plot designs 分割地块设计的多目标优化

IF 1.9 Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2022.04.001

Matteo Borrotti , Francesco Sambo , Kalliopi Mylona

Modern experiments allow scientists to tackle scientific problems of increasing complexity. Often experiments are characterised by factors that have levels which are harder to set than others. A possible solution is the use of a split-plot design. Many solutions are available in the literature to find optimal designs that focus solely on optimising a single criterion. Multi-criteria approaches have been developed to overcome the limitations of the one-objective optimisation, however they mainly focus on estimating the precision of the fixed factor effects, ignoring the variance component estimation. The Multi-Stratum Two-Phase Local Search (MS-TPLS) algorithm for multi-objective optimisation of designs of experiments is extended, in order to ensure pure-error estimation of the variance components. The proposed solution is applied to two motivating problems and the final optimal Pareto front and related designs are compared with other designs from the relevant literature. Experimental results show that the designs from the obtained Pareto front represent good candidate solutions based on the different objectives.

现代实验使科学家能够解决日益复杂的科学问题。实验的特点往往是因素的水平比其他因素更难设定。一种可能的解决方案是使用分割图设计。文献中有许多解决方案可用于寻找仅专注于优化单个标准的最佳设计。已经开发了多准则方法来克服单目标优化的局限性，但它们主要集中于估计固定因素效应的精度，而忽略了方差分量估计。为了保证方差分量的纯误差估计，扩展了用于实验设计多目标优化的多层两相局部搜索（MS-TPLS）算法。将所提出的解决方案应用于两个激励问题，并将最终的最优Pareto前沿和相关设计与相关文献中的其他设计进行了比较。实验结果表明，来自所获得的Pareto前沿的设计代表了基于不同目标的良好候选解。

引用次数: 2

Factor-augmented Bayesian treatment effects models for panel outcomes 面板结果的因子增强贝叶斯治疗效果模型

IF 1.9 Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2022.04.003

Helga Wagner , Sylvia Frühwirth-Schnatter , Liana Jacobi

A new, flexible model for inference of the effect of a binary treatment on a continuous outcome observed over subsequent time periods is proposed. The model allows to separate the associations due to endogeneity under treatment selection and additional longitudinal association of the outcomes, thus yielding unbiased estimates of dynamic treatment effects if both sources of association are present. The performance of the proposed method is investigated on simulated data and employed to re-analyze data on the longitudinal effects of a long maternity leave on mothers’ earnings after their return to the labour market.

提出了一种新的、灵活的模型，用于推断二元治疗对后续时间段内观察到的连续结果的影响。该模型允许分离由于治疗选择下的内生性和结果的额外纵向关联而产生的关联，从而在两种关联来源都存在的情况下对动态治疗效果产生无偏估计。在模拟数据上调查了所提出方法的性能，并用于重新分析长期产假对母亲重返劳动力市场后收入的纵向影响数据。

引用次数: 0

Estimating the number of common trends in large T and N factor models via canonical correlations analysis 通过典型相关分析估计大T和N因子模型中共同趋势的数量

Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2023.10.001

Massimo Franchi, Iliyan Georgiev, Paolo Paruolo

Asymptotic results for canonical correlations are derived when the analysis is performed between levels and cumulated levels of N time series of length T, generated by a factor model with s common stochastic trends. For T→∞ and fixed N and s, the largest s squared canonical correlations are shown to converge to a non-degenerate limit distribution while the remaining N−s converge in probability to 0. Furthermore, if s grows at most linearly in N, the largest s squared canonical correlations are shown to converge in probability to 1 as (T,N)seq→∞. This feature allows one to estimate the number of common trends as the integer with largest decrease in adjacent squared canonical correlations. The maximal gap equals 1 in the limit and this criterion is shown to be consistent. A Monte Carlo simulation study illustrates the findings.

当对长度为T的N个时间序列的水平和累积水平进行分析时，得到典型相关的渐近结果，该时间序列由具有s个常见随机趋势的因子模型生成。对于T→∞和固定的N和s，最大的s平方正则相关收敛于一个非退化极限分布，而剩余的N−s在概率上收敛于0。此外，如果s在N中最线性增长，则最大的s平方典型相关在概率上收敛于1，为(T,N)seq→∞。这一特性使人们可以估计共同趋势的数量为相邻标准相关的平方下降幅度最大的整数。在极限处最大间隙等于1，证明了该准则是一致的。蒙特卡罗模拟研究说明了这一发现。

引用次数: 0

On the consistency of K-sign depth tests 关于k符号深度检验的一致性

Q2 ECONOMICS

Econometrics and Statistics

Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2023.10.002

Kevin Leckey, Mirko Jakubzik, Christine H. Müller

The consistency of the so-called K-sign depth tests is considered. These tests are based on the K-sign depth, which originated from the simplicial regression depth, but is easier to compute. The K-sign depth tests use only the signs of residuals and are equivalent to the classical sign test for K=2. However, K-sign depth tests with K≥3 show a much better power than the classical sign tests in simulation studies. This property is attributed to the consistency of these tests for K=3. After deriving a general condition for consistency, it is shown that this condition is in particular satisfied for several relevant hypotheses in polynomial regression models.

考虑了所谓的k符号深度检验的一致性。这些测试是基于k符号深度，它起源于简单回归深度，但更容易计算。K符号深度检验只使用残差的符号，与K=2的经典符号检验等效。然而，在模拟研究中，K≥3的K符号深度测试显示出比经典符号测试更好的能力。这一特性归因于K=3时这些测试的一致性。在推导出一致性的一般条件后，证明了多项式回归模型中的几个相关假设特别满足该条件。

引用次数: 0

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