Journal of Time Series Analysis最新文献

{"title":"Corrigendum: Error bounds and asymptotic expansions for Toeplitz product functionals of unbounded spectra","authors":"Tetsuya Takabatake","doi":"10.1111/jtsa.12690","DOIUrl":"10.1111/jtsa.12690","url":null,"abstract":"<p>We investigate error orders for integral limit approximations to traces of products of Toeplitz matrices generated by integrable functions on <math>\u0000 <mrow>\u0000 <mo>[</mo>\u0000 <mo>−</mo>\u0000 <mi>π</mi>\u0000 <mo>,</mo>\u0000 <mi>π</mi>\u0000 <mo>]</mo>\u0000 </mrow></math> having some singularities at the origin. Even though a sharp error order of the above approximation is derived in Theorem 2 of Lieberman and Phillips (2004, <i>Journal of Time Series Analysis</i>, 25(5) 733–753), its proof contains an inaccuracy. In the present article, we reinvestigate error orders of the above trace approximation problem and rigorously validate the sharp error order derived in Lieberman and Phillips (2004, <i>Journal of Time Series Analysis</i>, 25(5) 733–753).</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43489912","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":"Stochastic local and moderate departures from a unit root and its application to unit root testing","authors":"Mikihito Nishi,&nbsp;Eiji Kurozumi","doi":"10.1111/jtsa.12691","DOIUrl":"10.1111/jtsa.12691","url":null,"abstract":"<p>Local-to-unity and moderate-deviations specifications have been popular alternatives to unit root modeling. This article considers another kind of departures from a unit root, of the form <math>\u0000 <mrow>\u0000 <mi>c</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mi>v</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>/</mo>\u0000 <msup>\u0000 <mrow>\u0000 <mi>T</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 </msup>\u0000 </mrow></math>, where <math>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>v</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow></math> is random and <math>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow></math> determines the distance from a unit root. We classify the stochastic departures into two types: local and moderate. This classification task is completed by investigating the asymptotic behavior of unit root tests that assume the stochastic unit root (STUR) processes as the alternative hypothesis. The stochastic local-to-unity model arises when <math>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 <mo>=</mo>\u0000 <mn>3</mn>\u0000 <mo>/</mo>\u0000 <mn>4</mn>\u0000 </mrow></math>; in this case, the test statistics have limiting distributions different from those under the unit root null, and their asymptotic powers are greater than size. Moderate deviations emerge when <math>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>/</mo>\u0000 <mn>2</mn>\u0000 <mo>≤</mo>\u0000 <mi>β</mi>\u0000 <mo>&lt;</mo>\u0000 <mn>3</mn>\u0000 <mo>/</mo>\u0000 <mn>4</mn>\u0000 </mrow></math>, in which case the test statistics diverge. We also propose new tests for a unit root against an STUR, whose construction is based on the limit theory developed in this article. To evaluate the performance of these new tests, we derive the limiting Gaussian power envelope under the local alternative from an approximate model.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12691","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44475299","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":"Portmanteau tests for periodic ARMA models with dependent errors","authors":"Y. Boubacar Maïnassara,&nbsp;A. Ilmi Amir","doi":"10.1111/jtsa.12692","DOIUrl":"10.1111/jtsa.12692","url":null,"abstract":"<p>In this article, we derive the asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations of (parsimonious) periodic autoregressive moving-average (PARMA) models under the assumption that the errors are uncorrelated but not necessarily independent. We then deduce the modified portmanteau statistics. We establish the asymptotic behavior of the proposed statistics. It is shown that the asymptotic distribution of the modified portmanteau tests is that of a weighted sum of independent chi-squared random variables, which can be different from the usual chi-squared approximation used under independent and identically distributed assumption on the noise. We also propose another test based on a self-normalization approach to check the adequacy of PARMA models. A set of Monte Carlo experiments and an application to financial data are presented.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64272733","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":"Clustering multivariate time series using energy distance","authors":"Richard A. Davis,&nbsp;Leon Fernandes,&nbsp;Konstantinos Fokianos","doi":"10.1111/jtsa.12688","DOIUrl":"10.1111/jtsa.12688","url":null,"abstract":"<p>A novel methodology is proposed for clustering multivariate time series data using energy distance defined in Székely and Rizzo (2013). Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure the separation between the finite-dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is calculated, a hierarchical clustering method is then applied to obtain the dendrogram. This procedure is completely nonparametric as the dissimilarities between stationary distributions are directly calculated without making any model assumptions. In order to justify this procedure, asymptotic properties of the energy distance estimates are derived for general stationary and ergodic time series. The method is illustrated in a simulation study for various component time series that are either linear or nonlinear. Finally, the methodology is applied to two examples; one involves the GDP of selected countries and the other is the population size of various states in the U.S.A. in the years 1900–1999.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44254149","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":"Robert  Taylor","doi":"10.1111/jtsa.12687","DOIUrl":"10.1111/jtsa.12687","url":null,"abstract":"<p>I am delighted to welcome Sam Astill, James Duffy and Liudas Giraitis to the editorial board of the <i>Journal of Time Series Analysis</i>. All three join as Associate Editors with effect from 1 March 2023. At the same time, I would like to thank Professor Konstantinos Fokianos, who steps down as an Associate Editor with effect from 1 March 2023, for his work for the journal in this capacity since 2013.</p><p>Sam Astill is a Senior Lecturer in Econometrics at Essex Business School. His research interests include theoretical and applied time series econometrics and financial econometrics, in particular asset price bubble detection and predictive regression. He has published his research in <i>Journal of Financial Econometrics</i>, <i>Journal of Time Series Analysis</i>, <i>The Econometrics Journal</i>, among others.</p><p>James Duffy is an Associate Professor in Economics at the University of Oxford. His research is focused on strongly dependent time series, particularly with respect to: non-parametric estimation and inference; nonlinear generalizations of cointegration; and the robustness of inferences to varying levels of persistence. He is also interested in problems of identification and inference in structural macroeconomic models. His research has been published in the <i>Annals of Statistics</i>, the <i>Journal of Econometrics</i>, <i>Econometric Theory</i>, among others.</p><p>Liudas Giraitis is Professor of Econometrics at Queen Mary, University of London. His research interests cover long memory and integrated <i>I</i>(<i>d</i>) models, non-parametric methods for time series models with time-varying parameters, ARCH modeling, asymptotic theory for dependent variables and their statistical and econometric applications. He has published his research in <i>Annals of Statistics</i>, <i>Annals of Probability</i>, <i>Journal of Time Series Analysis</i>, <i>Econometric Theory</i>, <i>Journal of Econometrics</i>, among others.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41949389","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":"A new estimator for LARCH processes","authors":"Jean-Marc Bardet","doi":"10.1111/jtsa.12689","DOIUrl":"10.1111/jtsa.12689","url":null,"abstract":"<p>The aim of this article is to provide a new estimator of parameters for LARCH<math>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>∞</mi>\u0000 <mo>)</mo>\u0000 </mrow></math> processes, and thus also for LARCH<math>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>p</mi>\u0000 <mo>)</mo>\u0000 </mrow></math> or GLARCH<math>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>p</mi>\u0000 <mo>,</mo>\u0000 <mi>q</mi>\u0000 <mo>)</mo>\u0000 </mrow></math> processes. This estimator results from minimizing a contrast leading to a least squares estimator for the absolute values of the process. Strong consistency and asymptotic normality are shown, and convergence occurs at the rate <math>\u0000 <mrow>\u0000 <msqrt>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msqrt>\u0000 </mrow></math> as well in short or long memory cases. Numerical experiments confirm the theoretical results and show that this new estimator significantly outperforms the smoothed quasi-maximum likelihood estimators or weighted least squares estimators commonly used for such processes.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42175723","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":"Inference for high-dimensional linear models with locally stationary error processes","authors":"Jiaqi Xia,&nbsp;Yu Chen,&nbsp;Xiao Guo","doi":"10.1111/jtsa.12686","DOIUrl":"10.1111/jtsa.12686","url":null,"abstract":"<p>Linear regression models with stationary errors are well studied but the non-stationary assumption is more realistic in practice. An estimation and inference procedure for high-dimensional linear regression models with locally stationary error processes is developed. Combined with a proper estimator for the autocovariance matrix of the non-stationary error, the desparsified lasso estimator is adopted for the statistical inference of the regression coefficients under the fixed design setting. The consistency and asymptotic normality of the desparsified estimators is established under certain regularity conditions. Element-wise confidence intervals for regression coefficients are constructed. The finite sample performance of our method is assessed by simulation and real data analysis.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64272980","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":"A first order continuous time VAR with random coefficients","authors":"Milena Hoyos","doi":"10.1111/jtsa.12685","DOIUrl":"10.1111/jtsa.12685","url":null,"abstract":"<p>This article considers a first order continuous time vector autoregression with random coefficients. We discuss some difficulties that arise when the exact discrete analogue is used for estimating the continuous time parameters and provide an estimation method based on an approximate discrete model. Some expressions for the estimator of the drift parameter matrix, for its approximated bias and for the covariance matrix of the parameter estimates are derived. The finite sample performance of the proposed method is studied by a Monte Carlo experiment. We also illustrate the advantages of our model in an application on the expectations theory of the term structure of interest rates. Results show that the performance of the proposed methodology is good, and allowing for time variation on coefficients results in large reductions in the root mean square error of the parameter estimates.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43249410","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":"Optimal estimating function for weak location-scale dynamic models","authors":"Christian Francq,&nbsp;Jean-Michel Zakoïan","doi":"10.1111/jtsa.12684","DOIUrl":"10.1111/jtsa.12684","url":null,"abstract":"<p>Estimating functions provide a very general framework for the statistical inference of dynamic models under weak assumptions. We consider a class of time series models consisting in the parametrization of the first two conditional moments which – by contrast with classical location-scale dynamic models – do not impose further constraints on the conditional distribution/moments. Quasi-likelihood estimators (QLE) are obtained by solving estimating equations deduced from those two conditional moments. Conditions ensuring the existence and asymptotic properties (consistency and asymptotic normality) of such estimators are provided. We propose optimal QLEs in Godambe's sense, deduced from a condition obtained by Chandra and Taniguchi (2001, <i>Annals of the Institute of Statistical Mathematics</i> 53, 125–141). The particular case of the quasi-maximum likelihood estimators is considered. For pure location models, a data-driven procedure for optimally choosing the QLE is proposed. Our results are illustrated via Monte Carlo experiments and real financial data.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42330213","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":"Multi-purpose open-end monitoring procedures for multivariate observations based on the empirical distribution function","authors":"Mark Holmes,&nbsp;Ivan Kojadinovic,&nbsp;Alex Verhoijsen","doi":"10.1111/jtsa.12683","DOIUrl":"10.1111/jtsa.12683","url":null,"abstract":"<p>We propose non-parametric open-end sequential testing procedures that can detect all types of changes in the contemporary distribution function of possibly multivariate observations. Their asymptotic properties are theoretically investigated under stationarity and under alternatives to stationarity. Monte Carlo experiments reveal their good finite-sample behavior in the case of continuous univariate, bivariate and trivariate observations. A short data example concludes the work.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46942393","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}