Journal of Time Series Analysis最新文献

{"title":"A multiplicative thinning-based integer-valued GARCH model","authors":"Abdelhakim Aknouche,&nbsp;Manuel G. Scotto","doi":"10.1111/jtsa.12682","DOIUrl":"10.1111/jtsa.12682","url":null,"abstract":"<p>In this article, we introduce a multiplicative integer-valued time series model, which is defined as the product of a unit-mean integer-valued independent and identically distributed (i.i.d.) sequence, and an integer-valued dependent process. The latter is defined as a binomial thinning operation of its own past and of the past of the observed process. Furthermore, it combines some features of the integer-valued GARCH (INGARCH), the autoregressive conditional duration (ACD), and the integer autoregression (INAR) processes. The proposed model has an unspecified distribution and is able to parsimoniously generate very high overdispersion, persistence, and heavy-tailedness. The dynamic probabilistic structure of the model is first analyzed. In addition, parameter estimation is considered by using a two-stage weighted least squares estimate (2SWLSE), consistency and asymptotic normality (CAN) of which are established under mild conditions. Applications of the proposed formulation to simulated and actual count time series data are provided.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45424045","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}

{"title":"Editorial announcement","authors":"","doi":"10.1111/jtsa.12681","DOIUrl":"https://doi.org/10.1111/jtsa.12681","url":null,"abstract":"<p>On behalf of both the editorial board and the readership of the <i>Journal of Time Series Analysis</i>, I would like to take this opportunity to thank Professor Steve Leybourne and Professor Dag Tjøstheim very much for their dedicated service as Co-Editors of the <i>Journal of Time Series Analysis</i> since January 2013, and as Associate Editors of the journal prior to that. Both have stepped down with effect from 28th February 2023. I am, however, very pleased to announce that both Steve and Dag have agreed to become Advisory Editors of the <i>Journal of Time Series Analysis</i> in each case with effect from 1st March 2023.</p><p>I am delighted to welcome Alexander Aue and Christian Francq as new Co-Editors of the <i>Journal of Time Series Analysis</i>, in each case effective from 1st March 2023.</p><p></p><p><b>Alexander Aue</b> is a professor in the Department of Statistics at the University of California, Davis. His research interests are in time series analysis, structural breaks and high-dimensional statistics. His most recent work is on devising methodology for functional time series and on applying random matrix theory to high-dimensional inference problems.</p><p></p><p><b>Christian Francq</b> is a member of the CREST Laboratory and professor of Applied Mathematics at the University of Lille and ENSAE, where he teaches time series analysis and financial econometrics. His main research interests include financial and time series econometrics, as well as theoretical econometrics. He is the author and co-author of several articles published in statistical and econometric journals. His current research focuses on risk estimation, estimation of volatility models and models for time-varying betas.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50119576","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}

{"title":"Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series","authors":"Abdelhakim Aknouche,&nbsp;Stefanos Dimitrakopoulos","doi":"10.1111/jtsa.12679","DOIUrl":"10.1111/jtsa.12679","url":null,"abstract":"<p>We propose a multiplicative autoregressive conditional proportion (ARCP) model for (0,1)-valued time series, in the spirit of GARCH (generalized autoregressive conditional heteroscedastic) and ACD (autoregressive conditional duration) models. In particular, our underlying process is defined as the product of a (0,1)-valued independent and identically distributed (i.i.d.) sequence and the inverted conditional mean, which, in turn, depends on past reciprocal observations in such a way that is larger than unity. The probability structure of the model is studied in the context of the stochastic recurrence equation theory, while estimation of the model parameters is performed with the exponential quasi-maximum likelihood estimator (EQMLE). The consistency and asymptotic normality of the EQMLE are both established under general regularity assumptions. Finally, the usefulness of our proposed model is illustrated with two real datasets.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47810806","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}

{"title":"Geometric ergodicity and conditional self-weighted M-estimator of a GRCAR(\u0000 \u0000 p\u0000 ) model with heavy-tailed errors","authors":"Xiaoyan Li,&nbsp;Jiazhu Pan,&nbsp;Anchao Song","doi":"10.1111/jtsa.12680","DOIUrl":"10.1111/jtsa.12680","url":null,"abstract":"<p>We establish the geometric ergodicity for general stochastic functional autoregressive (linear and nonlinear) models with heavy-tailed errors. The stationarity conditions for a generalized random coefficient autoregressive model (GRCAR(<math>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow></math>)) are presented as a corollary. And then, a conditional self-weighted M-estimator for parameters in the GRCAR(<math>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow></math>) is proposed. The asymptotic normality of this estimator is discussed by allowing infinite variance innovations. Simulation experiments are carried out to assess the finite-sample performance of the proposed methodology and theory, and a real heavy-tailed data example is given as illustration.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45743765","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}

{"title":"Regime switching models for circular and linear time series","authors":"Andrew Harvey,&nbsp;Dario Palumbo","doi":"10.1111/jtsa.12678","DOIUrl":"10.1111/jtsa.12678","url":null,"abstract":"<p>The score-driven approach to time series modelling is able to handle circular data and switching regimes with intra-regime dynamics. Furthermore it enables a dynamic model to be fitted to a linear and a circular variable when their joint distribution is a cylinder. The viability of the new method is illustrated by estimating models for hourly data on wind direction and speed in Galicia, north-west Spain. The modelling of intra-regime dynamics is shown to be of critical importance.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44321969","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}

{"title":"Some recent trends in embeddings of time series and dynamic networks","authors":"Dag Tjøstheim,&nbsp;Martin Jullum,&nbsp;Anders Løland","doi":"10.1111/jtsa.12677","DOIUrl":"10.1111/jtsa.12677","url":null,"abstract":"<p>We give a review of some recent developments in embeddings of time series and dynamic networks. We start out with traditional principal components and then look at extensions to dynamic factor models for time series. Unlike principal components for time series, the literature on time-varying nonlinear embedding is rather sparse. The most promising approaches in the literature is neural network based, and has recently performed well in forecasting competitions. We also touch on different forms of dynamics in topological data analysis (TDA). The last part of the article deals with embedding of dynamic networks, where we believe there is a gap between available theory and the behavior of most real world networks. We illustrate our review with two simulated examples. Throughout the review, we highlight differences between the static and dynamic case, and point to several open problems in the dynamic case.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46354498","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}

{"title":"Factor models for high-dimensional functional time series I: Representation results","authors":"Marc Hallin,&nbsp;Gilles Nisol,&nbsp;Shahin Tavakoli","doi":"10.1111/jtsa.12676","DOIUrl":"10.1111/jtsa.12676","url":null,"abstract":"<p>In this article, which consists of two parts (Part I: representation results; Part II: estimation and forecasting methods), we set up the theoretical foundations for a high-dimensional functional factor model approach in the analysis of large cross-sections (panels) of functional time series (FTS). In Part I, we establish a representation result stating that, under mild assumptions on the covariance operator of the cross-section, we can represent each FTS as the sum of a common component driven by scalar factors loaded via functional loadings, and a mildly cross-correlated idiosyncratic component. Our model and theory are developed in a general Hilbert space setting that allows for mixed panels of functional and scalar time series. We then turn, in Part II, to the identification of the number of factors, and the estimation of the factors, their loadings, and the common components. We provide a family of information criteria for identifying the number of factors, and prove their consistency. We provide average error bounds for the estimators of the factors, loadings, and common components; our results encompass the scalar case, for which they reproduce and extend, under weaker conditions, well-established similar results. Under slightly stronger assumptions, we also provide uniform bounds for the estimators of factors, loadings, and common components, thus extending existing scalar results. Our consistency results in the asymptotic regime where the number <math>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow></math> of series and the number <math>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow></math> of time observations diverge thus extend to the functional context the ‘blessing of dimensionality’ that explains the success of factor models in the analysis of high-dimensional (scalar) time series. We provide numerical illustrations that corroborate the convergence rates predicted by the theory, and provide a finer understanding of the interplay between   <math>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow></math> and <math>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow></math> for estimation purposes. We conclude with an application to forecasting mortality curves, where we demonstrate that our approach outperforms existing methods.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43248757","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}

{"title":"Factor models for high-dimensional functional time series II: Estimation and forecasting","authors":"Shahin Tavakoli,&nbsp;Gilles Nisol,&nbsp;Marc Hallin","doi":"10.1111/jtsa.12675","DOIUrl":"10.1111/jtsa.12675","url":null,"abstract":"<p>This article is the second one in a set of two laying the theoretical foundations for a high-dimensional functional factor model approach in the analysis of large cross-sections (panels) of functional time series (FTS). Part I establishes a representation result by which, under mild assumptions on the covariance operator of the cross-section, any FTS admits a canonical representation as the sum of a common and an idiosyncratic component; common components are driven by a finite and typically small number of scalar factors loaded via functional loadings, while idiosyncratic components are only mildly cross-correlated. Building on that representation result, Part II is dealing with the identification of the number of factors, their estimation, the estimation of their loadings and the common components, and the resulting forecasts. We provide a family of information criteria for identifying the number of factors, and prove their consistency. We provide average error bounds for the estimators of the factors, loadings, and common components; our results encompass the scalar case, for which they reproduce and extend, under weaker conditions, well-established similar results. Under slightly stronger assumptions, we also provide uniform bounds for the estimators of factors, loadings, and common components, thus extending existing scalar results. Our consistency results in the asymptotic regime where the number <math>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow></math> of series and the number <math>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow></math> of time points diverge thus extend to the functional context the ‘blessing of dimensionality’ that explains the success of factor models in the analysis of high-dimensional (scalar) time series. We provide numerical illustrations that corroborate the convergence rates predicted by the theory, and provide a finer understanding of the interplay between <math>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow></math> and <math>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow></math> for estimation purposes. We conclude with an application to forecasting mortality curves, where our approach outperforms existing methods.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42515220","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}

{"title":"Detecting relevant changes in the spatiotemporal mean function","authors":"Holger Dette,&nbsp;Pascal Quanz","doi":"10.1111/jtsa.12674","DOIUrl":"10.1111/jtsa.12674","url":null,"abstract":"<p>For a spatiotemporal process <math>\u0000 <mo>{</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>j</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>(</mo>\u0000 <mi>s</mi>\u0000 <mo>,</mo>\u0000 <mi>t</mi>\u0000 <mo>)</mo>\u0000 <mo>∣</mo>\u0000 <mi>s</mi>\u0000 <mo>∈</mo>\u0000 <mi>S</mi>\u0000 <mo>,</mo>\u0000 <mspace></mspace>\u0000 <mi>t</mi>\u0000 <mo>∈</mo>\u0000 <mi>T</mi>\u0000 <mo>}</mo>\u0000 <msub>\u0000 <mrow></mrow>\u0000 <mrow>\u0000 <mi>j</mi>\u0000 <mo>=</mo>\u0000 <mn>1</mn>\u0000 <mo>,</mo>\u0000 <mi>…</mi>\u0000 <mo>,</mo>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub></math>, where <math>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 </mrow></math> denotes the set of spatial locations and <math>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow></math> the time domain, we consider the problem of testing for a change in the sequence of mean functions <math>\u0000 <msub>\u0000 <mrow>\u0000 <mo>{</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>μ</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>j</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>(</mo>\u0000 <mi>s</mi>\u0000 <mo>,</mo>\u0000 <mi>t</mi>\u0000 <mo>)</mo>\u0000 <mo>∣</mo>\u0000 <mi>s</mi>\u0000 <mo>∈</mo>\u0000 <mi>S</mi>\u0000 <mo>,</mo>\u0000 <mspace></mspace>\u0000 <mi>t</mi>\u0000 <mo>∈</mo>\u0000 <mi>T</mi>\u0000 <mo>}</mo>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>j</mi>\u0000 <mo>=</mo>\u0000 <mn>1</mn>\u0000 <mo>,</mo>\u0000 <mi>…</mi>\u0000 <mo>,</mo>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub></math>. In contrast to most of the literature, we are not interested in arbitrarily small changes but only in changes with a norm exceeding a given threshold. Asymptotically distribution free tests are proposed, which do not require the estimation of the long-run spatiotemporal covariance structure. In particular, we consider a fully functional approach and a test based on the cumulative sum paradigm, investigate the large sample properties of the corresponding test statistics and stu","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41321936","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}

{"title":"Editorial Announcement: Journal of Time Series Analysis Distinguished Authors 2022","authors":"Robert Taylor","doi":"10.1111/jtsa.12673","DOIUrl":"10.1111/jtsa.12673","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 one-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) and Volume 43 Issue 1 (January 2022), the <i>Journal of Time Series Analysis</i> is very pleased to welcome (in alphabetical order):</p><p>Francesco Bravo,</p><p>Evangelos E. Ioannidis,</p><p>Piotr Kokoszka,</p><p>Dong Li,</p><p>and</p><p>Pierre Perron</p><p>to the list of <i>Journal of Time Series Analysis Distinguished Authors</i> for 2022 based on their publications in the Journal appearing up to and including Volume 43 Issue 6 (November 2022).</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49535149","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}