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Non-crossing quantile double-autoregression for the analysis of streaming time series data 用于分析流式时间序列数据的非交叉量级双自回归
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-10-11 DOI: 10.1111/jtsa.12725
Rong Jiang, Siu Kai Choy, Keming Yu
{"title":"Non-crossing quantile double-autoregression for the analysis of streaming time series data","authors":"Rong Jiang,&nbsp;Siu Kai Choy,&nbsp;Keming Yu","doi":"10.1111/jtsa.12725","DOIUrl":"10.1111/jtsa.12725","url":null,"abstract":"<p>Many financial time series not only have varying structures at different quantile levels and exhibit the phenomenon of conditional heteroscedasticity at the same time but also arrive in the stream. Quantile double-autoregression is very useful for time series analysis but faces challenges with model fitting of streaming data sets when estimating other quantiles in subsequent batches. This article proposes a renewable estimation method for quantile double-autoregression analysis of streaming time series data due to its ability to break with storage barrier and computational barrier. Moreover, the proposed flexible parametric structure of the quantile function enables us to predict any interested quantile value without quantile curve crossing problem or keeping the desirable monotone property of the conditional quantile function. The proposed methods are illustrated using current data and the summary statistics of historical data. Theoretically, the proposed statistic is shown to have the same asymptotic distribution as the standard version computed on an entire data stream with the data batches pooled into one data set, without additional condition. Simulation studies and an empirical example are presented to illustrate the finite sample performance of the proposed methods.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 4","pages":"513-532"},"PeriodicalIF":0.9,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Test of change point versus long-range dependence in functional time series 检验功能时间序列中的变化点与长程依赖性
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-20 DOI: 10.1111/jtsa.12723
Changryong Baek, Piotr Kokoszka, Xiangdong Meng
{"title":"Test of change point versus long-range dependence in functional time series","authors":"Changryong Baek,&nbsp;Piotr Kokoszka,&nbsp;Xiangdong Meng","doi":"10.1111/jtsa.12723","DOIUrl":"10.1111/jtsa.12723","url":null,"abstract":"<p>In the context of functional time series, we propose a significance test to distinguish between short memory with a change point and long range dependence. The test is based on coefficients of projections onto an optimal direction that captures the dependence structure of the latent stationary functions that are not observable due to a potential change point. The optimal direction must be estimated as well. The test statistic is constructed using the local Whittle estimator applied to these coefficients. It has standard normal distribution under the null hypothesis (change point) and diverges to infinity under the alternative (long range dependence). The article includes asymptotic theory, a simulation study and an application to curve-valued time series derived from intraday asset prices.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 4","pages":"497-512"},"PeriodicalIF":0.9,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136375945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm 序列依赖下的多变化点检测：野性对比度最大化和加普-施瓦茨算法
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-18 DOI: 10.1111/jtsa.12722
Haeran Cho, Piotr Fryzlewicz
{"title":"Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm","authors":"Haeran Cho,&nbsp;Piotr Fryzlewicz","doi":"10.1111/jtsa.12722","DOIUrl":"10.1111/jtsa.12722","url":null,"abstract":"<p>We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion-based model selection strategy termed gappy Schwarz algorithm. The former is well-suited to separating shifts in the mean from fluctuations due to serial correlations, while the latter simultaneously estimates the dependence structure and the number of change points without performing the difficult task of estimating the level of the noise as quantified e.g. by the long-run variance. We provide modular investigation into their theoretical properties and show that the combined methodology, named WCM.gSa, achieves consistency in estimating both the total number and the locations of the change points. The good performance of WCM.gSa is demonstrated via extensive simulation studies, and we further illustrate its usefulness by applying the methodology to London air quality data.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 3","pages":"479-494"},"PeriodicalIF":0.9,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12722","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135207798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial announcement: Journal of Time Series Analysis Distinguished Authors 2023 编辑公告：时间序列分析期刊》2023 年杰出作者
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-17 DOI: 10.1111/jtsa.12724
Robert Taylor
{"title":"Editorial announcement: Journal of Time Series Analysis Distinguished Authors 2023","authors":"Robert Taylor","doi":"10.1111/jtsa.12724","DOIUrl":"10.1111/jtsa.12724","url":null,"abstract":"<p>In recognition of authors who have made significant contributions to this Journal, the <i>Journal of Time Series Analysis</i> runs a scheme to honour those authors by naming them as a <i>Journal of Time Series Analysis Distinguished Author</i>. The qualifying criterion for this award is 3.5 points where authors are awarded 1 point for each single-authored article, ½ point for each double-authored article, 1/3 point for each triple-authored article, and so on, that they have published in the <i>Journal of Time Series Analysis</i> since its inception. Distinguished Authors are entitled to a 1-year free on-line subscription to the Journal to mark the award, and will also receive a certificate commemorating the award.</p><p>In addition to the lists of Distinguished Authors announced previously in Volume 41 issue 4 (July 2020), Volume 42 Issue 1 (January 2021), Volume 43 Issue 1 (January 2022), and Volume 44 Issue 1 (January 2023), the <i>Journal of Time Series Analysis</i> is very pleased to welcome</p><p><b>Suhasini Subba Rao</b></p><p>to the list of <i>Journal of Time Series Analysis Distinguished Authors</i> for 2023 based on her publications in the Journal appearing up to and including Volume 44 Issues 5–6 (September–November 2023).</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 1","pages":"3"},"PeriodicalIF":0.9,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135259542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Smooth transition moving average models: Estimation, testing, and computation 平滑过渡移动平均模型：估计、测试和计算
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-07 DOI: 10.1111/jtsa.12721
Xinyu Zhang, Dong Li
{"title":"Smooth transition moving average models: Estimation, testing, and computation","authors":"Xinyu Zhang,&nbsp;Dong Li","doi":"10.1111/jtsa.12721","DOIUrl":"10.1111/jtsa.12721","url":null,"abstract":"<p>The article introduces a new subclass of nonlinear moving average model, called the smooth transition moving average (STMA) model, and studies its probabilistic properties. It is shown that, under some mild conditions, the least squares estimation (LSE) is strongly consistent and asymptotically normal. A powerful score-based goodness-of-fit test for the STMA model is presented. A different parametrization from the classical one is applied to numerically improve the identification and estimation of this model. Simulation studies are conducted to assess the performance of the LSE and the score-based test in finite samples. The results are illustrated with an application to the weekly exchange rate of the USA Dollar to the British Pound.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 3","pages":"463-478"},"PeriodicalIF":0.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44267129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Local Whittle estimation with (quasi-)analytic wavelets (拟)解析小波的局部Whittle估计
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-04 DOI: 10.1111/jtsa.12719
Sophie Achard, Irène Gannaz
{"title":"Local Whittle estimation with (quasi-)analytic wavelets","authors":"Sophie Achard,&nbsp;Irène Gannaz","doi":"10.1111/jtsa.12719","DOIUrl":"10.1111/jtsa.12719","url":null,"abstract":"<p>In the general setting of long-memory multivariate time series, the long-memory characteristics are defined by two components. The long-memory parameters describe the autocorrelation of each time series. And the long-run covariance measures the coupling between time series, with general phase parameters. It is of interest to estimate the long-memory, long-run covariance and general phase parameters of time series generated by this wide class of models although they are not necessarily Gaussian nor stationary. This estimation is thus not directly possible using real wavelets decomposition or Fourier analysis. Our purpose is to define an inference approach based on a representation using quasi-analytic wavelets. We first show that the covariance of the wavelet coefficients provides an adequate estimator of the covariance structure including the phase term. Consistent estimators based on a local Whittle approximation are then proposed. Simulations highlight a satisfactory behavior of the estimation on finite samples on multivariate fractional Brownian motions. An application on a real neuroscience dataset is presented, where long-memory and brain connectivity are inferred.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 3","pages":"421-443"},"PeriodicalIF":0.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42534099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Granger causality tests based on reduced variable information 基于约简变量信息的Granger因果检验
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-03 DOI: 10.1111/jtsa.12720
Neng-Fang Tseng, Ying-Chao Hung, Junji Nakano
{"title":"Granger causality tests based on reduced variable information","authors":"Neng-Fang Tseng,&nbsp;Ying-Chao Hung,&nbsp;Junji Nakano","doi":"10.1111/jtsa.12720","DOIUrl":"10.1111/jtsa.12720","url":null,"abstract":"<p>Granger causality is a classical and important technique for measuring predictability from one group of time series to another by incorporating information of the variables described by a full vector autoregressive (VAR) process. However, in some applications economic forecasts need to be made based on information provided merely by a portion of variates (e.g., removal of a listed stock due to halting, suspension or delisting). This requires a new formulation of forecast based on an embedded subprocess of VAR, whose theoretical properties are often difficult to obtain. To avoid the issue of identifying the VAR subprocess, we propose a computation-based approach so that sophisticated predictions can be made by utilizing a reduced variable information set estimated from sampled data. Such estimated information set allows us to develop a suitable statistical hypothesis testing procedure for characterizing all designated Granger causal relationships, as well as a useful graphical tool for presenting the causal structure over the prediction horizon. Finally, simulated data and a real example from the stock markets are used to illustrate the proposed method.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 3","pages":"444-462"},"PeriodicalIF":0.9,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49077711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Stationary Jackknife 固定式升降刀
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-25 DOI: 10.1111/jtsa.12714
Weilian Zhou, Soumendra Lahiri
{"title":"Stationary Jackknife","authors":"Weilian Zhou,&nbsp;Soumendra Lahiri","doi":"10.1111/jtsa.12714","DOIUrl":"10.1111/jtsa.12714","url":null,"abstract":"<p>Variance estimation is an important aspect in statistical inference, especially in the dependent data situations. Resampling methods are ideal for solving this problem since these do not require restrictive distributional assumptions. In this paper, we develop a novel resampling method in the Jackknife family called the <span>stationary jackknife</span>. It can be used to estimate the variance of a statistic in the cases where observations are from a general stationary sequence. Unlike the moving block jackknife, the <span>stationary jackknife</span> computes the jackknife replication by deleting a variable length block and the length has a truncated geometric distribution. Under appropriate assumptions, we can show the <span>stationary jackknife</span> variance estimator is a consistent estimator for the case of the sample mean and, more generally, for a class of nonlinear statistics. Further, the <span>stationary jackknife</span> is shown to provide reasonable variance estimation for a wider range of expected block lengths when compared with the moving block jackknife by simulation.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 3","pages":"333-360"},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45567502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Additive autoregressive models for matrix valued time series 矩阵值时间序列的加性自回归模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-25 DOI: 10.1111/jtsa.12718
Hong-Fan Zhang
{"title":"Additive autoregressive models for matrix valued time series","authors":"Hong-Fan Zhang","doi":"10.1111/jtsa.12718","DOIUrl":"10.1111/jtsa.12718","url":null,"abstract":"<p>In this article, we develop additive autoregressive models (Add-ARM) for the time series data with matrix valued predictors. The proposed models assume separable row, column and lag effects of the matrix variables, attaining stronger interpretability when compared with existing bilinear matrix autoregressive models. We utilize the Gershgorin's circle theorem to impose some certain conditions on the parameter matrices, which make the underlying process strictly stationary. We also introduce the alternating least squares estimation method to solve the involved equality constrained optimization problems. Asymptotic distributions of the parameter estimators are derived. In addition, we employ hypothesis tests to run diagnostics on the parameter matrices. The performance of the proposed models and methods is further demonstrated through simulations and real data analysis.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 3","pages":"398-420"},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49545023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Wasserstein distance bounds on the normal approximation of empirical autocovariances and cross-covariances under non-stationarity and stationarity 非平稳性和平稳性条件下经验自协方差和互协方差正态逼近的Wasserstein距离界
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-18 DOI: 10.1111/jtsa.12716
Andreas Anastasiou, Tobias Kley
{"title":"Wasserstein distance bounds on the normal approximation of empirical autocovariances and cross-covariances under non-stationarity and stationarity","authors":"Andreas Anastasiou,&nbsp;Tobias Kley","doi":"10.1111/jtsa.12716","DOIUrl":"10.1111/jtsa.12716","url":null,"abstract":"<p>The autocovariance and cross-covariance functions naturally appear in many time series procedures (e.g. autoregression or prediction). Under assumptions, empirical versions of the autocovariance and cross-covariance are asymptotically normal with covariance structure depending on the second- and fourth-order spectra. Under non-restrictive assumptions, we derive a bound for the Wasserstein distance of the finite-sample distribution of the estimator of the autocovariance and cross-covariance to the Gaussian limit. An error of approximation to the second-order moments of the estimator and an <span></span><math>\u0000 <mrow>\u0000 <mi>m</mi>\u0000 </mrow></math>-dependent approximation are the key ingredients to obtain the bound. As a worked example, we discuss how to compute the bound for causal autoregressive processes of order 1 with different distributions for the innovations. To assess our result, we compare our bound to Wasserstein distances obtained via simulation.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 3","pages":"361-375"},"PeriodicalIF":0.9,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41744961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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