Econometrics and Statistics最新文献_第3页

Factor models and high-dimensional time series A tribute to Marco Lippi on the occasion of his 80th birthday 因子模型和高维时间序列：在马可-里皮 80 岁生日之际向他致敬

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2024-05-01 DOI: 10.1016/j.ecosta.2024.04.005

Matteo Barigozzi, Manfred Deistler, Marc Hallin

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Estimating the Output Gap with High‐Dimensional Time Series 利用高维时间序列估算产出缺口

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2024-06-26 DOI: 10.1016/j.ecosta.2024.06.004

A. Giovannelli , T. Proietti

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Estimation of Extreme Risk Measures for Stochastic Volatility Models with Long Memory and Heavy Tails 长记忆重尾随机波动模型的极端风险测度估计

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2023-07-22 DOI: 10.1016/j.ecosta.2023.07.004

Clémonell Bilayi-Biakana, Gail Ivanoff, Rafał Kulik

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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

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2023-10-14 DOI: 10.1016/j.ecosta.2023.10.001

Massimo Franchi, Iliyan Georgiev, Paolo Paruolo

{"title":"Estimating the number of common trends in large T and N factor models via canonical correlations analysis","authors":"Massimo Franchi, Iliyan Georgiev, Paolo Paruolo","doi":"10.1016/j.ecosta.2023.10.001","DOIUrl":"10.1016/j.ecosta.2023.10.001","url":null,"abstract":"<div><div>Asymptotic results for canonical correlations are derived when the analysis is performed between levels and cumulated levels of <span><math><mi>N</mi></math></span> time series of length <span><math><mi>T</mi></math></span>, generated by a factor model with <span><math><mi>s</mi></math></span> common stochastic trends. For <span><math><mrow><mi>T</mi><mo>→</mo><mi>∞</mi></mrow></math></span> and fixed <span><math><mi>N</mi></math></span> and <span><math><mi>s</mi></math></span>, the largest <span><math><mi>s</mi></math></span> squared canonical correlations are shown to converge to a non-degenerate limit distribution while the remaining <span><math><mrow><mi>N</mi><mo>−</mo><mi>s</mi></mrow></math></span> converge in probability to 0. Furthermore, if <span><math><mi>s</mi></math></span> grows at most linearly in <span><math><mi>N</mi></math></span>, the largest <span><math><mi>s</mi></math></span> squared canonical correlations are shown to converge in probability to 1 as <span><math><mrow><msub><mrow><mo>(</mo><mi>T</mi><mo>,</mo><mi>N</mi><mo>)</mo></mrow><mrow><mi>s</mi><mi>e</mi><mi>q</mi></mrow></msub><mo>→</mo><mi>∞</mi></mrow></math></span>. 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.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"39 ","pages":"Pages 81-95"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135762528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A novel estimation procedure for robust CANDECOMP/PARAFAC model fitting 一种新的稳健CANDECOMP/PARAFAC模型拟合估计方法

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2023-07-13 DOI: 10.1016/j.ecosta.2023.07.001

Valentin Todorov, Violetta Simonacci, Michele Gallo, Nikolay Trendafilov

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Risk Assessment and Spurious Seasonality 风险评估和虚假季节性

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2022-07-08 DOI: 10.1016/j.ecosta.2022.07.003

Malte S. Kurz , Stefan Mittnik

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Vine Copula based Portfolio Level Conditional Risk Measure Forecasting 基于Vine Copula的组合水平条件风险度量预测

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2023-08-21 DOI: 10.1016/j.ecosta.2023.08.002

Emanuel Sommer , Karoline Bax , Claudia Czado

{"title":"Vine Copula based Portfolio Level Conditional Risk Measure Forecasting","authors":"Emanuel Sommer , Karoline Bax , Claudia Czado","doi":"10.1016/j.ecosta.2023.08.002","DOIUrl":"10.1016/j.ecosta.2023.08.002","url":null,"abstract":"<div><div><span><span>Accurately estimating risk measures for financial portfolios and validating their robustness is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level, hence they fail to capture the complex cross-dependencies between portfolio components and particularly provide no methodology to perform a sensitivity analysis on the estimates. To address both aspects, a new approach is presented that uses vine copulas in combination with univariate ARMA-GARCH models for marginal modelling to compute conditional portfolio-level risk measure estimates by simulating portfolio-level forecasts conditioned on a stress factor. A quantile-based approach is then presented to observe the behaviour of risk measures given a particular state of the conditioning asset(s). In an illustrative </span>case study of Spanish equities with different stress factors, the results show that the portfolio is quite robust to a sharp downturn in the American market. At the same time, there is no evidence of this behaviour with respect to the European market. The novel algorithms presented are ready for use through the R package </span><em>portvine</em>, which is publicly available on CRAN.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"39 ","pages":"Pages 160-178"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136221687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Second Special Issue on time series econometrics 时间序列计量经济学第二期特刊

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2026-06-03 DOI: 10.1016/j.ecosta.2026.06.002

Alessandra Amendola (Guest Editors) , Christian Francq , Marc Hallin , Zacharias Psaradakis

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A spline-assisted semiparametric approach to nonparametric measurement error models 非参数测量误差模型的样条辅助半参数方法

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2023-06-01 DOI: 10.1016/j.ecosta.2023.05.001

Fei Jiang, Yanyuan Ma, Raymond J. Carroll

{"title":"A spline-assisted semiparametric approach to nonparametric measurement error models","authors":"Fei Jiang, Yanyuan Ma, Raymond J. Carroll","doi":"10.1016/j.ecosta.2023.05.001","DOIUrl":"10.1016/j.ecosta.2023.05.001","url":null,"abstract":"<div><div>A spline-assisted approach is proposed to handle the measurement error problem in treating the pollution and asthma data. It is well known that the minimax rate of convergence in nonparametric regression function estimation of a random variable measured with error is much slower than the rate in the error free case. A different problem is considered. It is shown that if one is willing to impose a relatively mild assumption in requiring that the error-prone variable has a compact support, then standard nonparametric results are obtainable for measurement error models. New and constructive methods to take full advantage of the compact support assumption via spline-assisted semiparametric methods are proposed. It is proven that the new estimator achieves the usual nonparametric rate as if there were no measurement error. Furthermore it is shown that similar spline approach can be used to assess the probability density function on a compact set, using observations with error, and the resulting estimator differs from the true density function only by a constant scale. In addition, it retains the nonparametric density convergence properties of the error free case. The performance of the new methods is demonstrated through simulations and the methods are implemented to analyze the relation between asthma and pollution.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"39 ","pages":"Pages 338-352"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82388094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Robust nonparametric multiple changepoint detection for multivariate variability 多变量变异性的鲁棒非参数多变化点检测

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Econometrics and Statistics Pub Date : 2026-07-01 Epub Date: 2023-10-05 DOI: 10.1016/j.ecosta.2023.09.001

Kelly Ramsay, Shojaeddin Chenouri

{"title":"Robust nonparametric multiple changepoint detection for multivariate variability","authors":"Kelly Ramsay, Shojaeddin Chenouri","doi":"10.1016/j.ecosta.2023.09.001","DOIUrl":"10.1016/j.ecosta.2023.09.001","url":null,"abstract":"<div><div><span>Two robust, nonparametric multiple changepoint detection algorithms are introduced: DWBS and MKWP. These algorithms can detect multiple changes in the variability of a sequence of independent multivariate observations, even when the number of changepoints is unknown. The algorithms DWBS and MKWP require minimal distributional assumptions and are robust to outlying observations and heavy tails. The DWBS algorithm uses a local search method based on depth-based ranks and wild binary segmentation. The MKWP algorithm estimates changepoints globally via maximizing a penalized version of the classical Kruskal–Wallis ANOVA test statistic. It is demonstrated that this objective function can be maximized via the well-known PELT algorithm. Under mild, nonparametric assumptions, both of these algorithms are shown to be consistent for the correct number of changepoints and the correct location(s) of the changepoint(s). A data driven thresholding method for multivariate data is introduced, based on the Schwartz information criteria. The robustness and accuracy of the new methods is demonstrated with a simulation study, where the algorithms are compared to several existing algorithms. These new methods can estimate the number of changepoints and their locations accurately when the data are heavy tailed or skewed and the </span>sample size is large. Lastly, the proposed algorithms are applied to a four-dimensional sequence of European daily stock returns.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"39 ","pages":"Pages 310-337"},"PeriodicalIF":3.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134936298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0