Journal of Time Series Analysis最新文献

{"title":"Margin-closed vector autoregressive time series models","authors":"Lin Zhang,&nbsp;Harry Joe,&nbsp;Natalia Nolde","doi":"10.1111/jtsa.12712","DOIUrl":"10.1111/jtsa.12712","url":null,"abstract":"<p>Conditions are obtained for a Gaussian vector autoregressive time series of order <math></math>, VAR(<math></math>), to have univariate margins that are autoregressive of order <math></math> or lower-dimensional margins that are also VAR(<math></math>). This can lead to <math></math>-dimensional VAR(<math></math>) models that are closed with respect to a given partition <math></math> of <math></math> by specifying marginal serial dependence and some cross-sectional dependence parameters. The special closure property allows one to fit the subprocesses of multi-variate time series before assembling them by fitting the dependence structure between the subprocesses. We revisit the use of the Gaussian copula of the stationary joint distribution of observations in the VAR(<math></math>) process with non-Gaussian univariate margins but under the constraint of closure under margins. This construction allows more flexibility in handling higher-dimensional time series and a multi-stage estimation procedure can be used. The proposed class of models is applied to a macro-economic data set and compared with the relevant benchmark models.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 2","pages":"269-297"},"PeriodicalIF":0.9,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12712","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47421427","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":"Goodness-of-fit tests for the multivariate Student-t distribution based on i.i.d. data, and for GARCH observations","authors":"Simos Meintanis,&nbsp;Bojana Milošević,&nbsp;Marko Obradović,&nbsp;Mirjana Veljović","doi":"10.1111/jtsa.12713","DOIUrl":"10.1111/jtsa.12713","url":null,"abstract":"<p>We consider goodness-of-fit tests for the multivariate Student's <i>t</i>-distribution with i.i.d. data and for the innovation distribution in a generalized autoregressive conditional heteroskedasticity model. The methods are based on the empirical characteristic function and are relatively easy to implement, invariant under linear transformations, and globally consistent. Asymptotic properties of the proposed procedures are investigated, while the finite-sample properties are illustrated by means of a Monte Carlo study. The procedures are also applied to real data from the financial markets.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 2","pages":"298-319"},"PeriodicalIF":0.9,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64272880","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","authors":"Robert Taylor","doi":"10.1111/jtsa.12715","DOIUrl":"https://doi.org/10.1111/jtsa.12715","url":null,"abstract":"<p>On behalf of the editorial board of the <i>Journal of Time Series Analysis</i>, I am delighted to welcome Professors Liudas Giraitis (Queen Mary University of London), Robert Lund (University of California, Santa Cruz), and Neil Shephard (Harvard University) as Associate Editors of the journal, each with immediate effect. I would also like to thank Professors Konstantinos Fokianos (University of Cyprus) and Silvia Gonçalves (McGill University), who both step down as Associate Editors, each with immediate effect, for their service to the journal in these roles since 2013.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"44 5-6","pages":"439"},"PeriodicalIF":0.9,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50148073","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":"Adjustment coefficients and exact rational expectations in cointegrated vector autoregressive models","authors":"Søren Johansen,&nbsp;Anders Rygh Swensen","doi":"10.1111/jtsa.12705","DOIUrl":"10.1111/jtsa.12705","url":null,"abstract":"<p>In this article, we consider the cointegrated vector autoregressive model with adjustment parameters <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math> and cointegration vectors <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <annotation>$$ beta $$</annotation>\u0000 </semantics></math>. We discuss estimation of the model under the exact linear rational expectations, when we also have linear restrictions on the adjustment parameters <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math>. In particular we consider the same restriction on all vectors in <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math> and the hypothesis that some vectors in <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math> are known.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 2","pages":"248-268"},"PeriodicalIF":0.9,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539095","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":"Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates","authors":"Shan Dai,&nbsp;Ngai Hang Chan","doi":"10.1111/jtsa.12709","DOIUrl":"10.1111/jtsa.12709","url":null,"abstract":"<p>Cointegration has been widely used in macroeconomics and financial time series analysis, but traditional linear cointegration relationship is often rejected in empirical applications. Many constant parameters testing methods in semi-parametric functional coefficient cointegrated framework have been developed accordingly. However, there are little studies on constant parameters testing problem for the case that the index variable is integrated of order one. From a practical point of view, there is also a need for a test that accommodates integrated index variable in functional coefficient cointegrated setting, for example, in the study of the purchasing power parity hypothesis. In this article, an orthogonal series approximation-based test statistic is proposed to tackle the problem. The asymptotic results are also studied. Monte Carlo experiments are conducted to evaluate the finite sample performance of the proposed test, and an empirical example about price and exchange rate data is provided.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"44 5-6","pages":"474-486"},"PeriodicalIF":0.9,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41284852","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":"Special Issue of the Journal of Time Series Analysis in Honor of Professor Masanobu Taniguchi","authors":"Marc Hallin,&nbsp;Yoshihide Kakizawa,&nbsp;Hira Koul","doi":"10.1111/jtsa.12710","DOIUrl":"10.1111/jtsa.12710","url":null,"abstract":"Taniguchi Sensei – our colleague and friend Masanobu Taniguchi – retired from Waseda University in Tokyo at the end of March 2022 after a long and productive career that put Waseda on the international map of time series analysis and mathematical statistics. Masanobu arrived at Waseda from Osaka some 20 years ago and rapidly developed a powerful team of students (in total 19 theses defended) and researchers, as well as an impressive network of international collaborations. Thanks to him and the countless international conferences and symposiums he tirelessly organized all over Japan, numerous statisticians from all continents enjoyed his warm hospitality, established fruitful collaborative contacts with his team, and discovered the refinements of Japanese lifestyle and culture. Statistical inference for stochastic processes and time series is a red thread running through Masanobu’s entire research career. This does not mean, however, that his contributions are narrowly concentrated on one single subject! Quite on the contrary, his scientific interests are embracing an exceptionally wide spectrum of mathematical and applied statistics topics. While it is not possible here to do justice to all of his contributions, let us mention higher-order asymptotics, a notoriously difficult subject where he can be considered to be a worldwide expert, spectral methods, local asymptotic normality and Le Cam’s asymptotic theory of statistical experiments, Edgeworth expansions in stationary processes, estimating functions, discriminant analysis and clustering, empirical likelihood methods, long-memory processes, heavy tails, volatility models, ... not to forget economic and financial applications, risk analysis, and portfolio theory – all in the general framework of serially dependent observations. That activity has resulted in over 150 articles published in internationally acclaimed journals including the Annals of Statistics, the Journal of the Royal Statistical Society, the Journal of the American Statistical Association, Biometrika, the Journal of Econometrics, the Journal of Time Series Analysis, Econometric Theory, the Journal of Multivariate Analysis, among many others, and no less than seven books. It is an honor for us to guest-edit this special issue of the Journal of Time Series Analysis as a tribute to Masanobu’s scientific achievement. This issue contains 12 invited papers, all lying at the frontier in time series analysis research, by econometricians and statisticians. All papers were refereed as per the standards of the journal. Bhattacharjee, Chakraborty and Koul discuss the estimation of the regression parameters in a high-dimensional errors in variables linear regression model, where the measurement errors in the covariates are assumed to form a stationary short-memory moving average process having known Laplace stationary distribution and the regression errors are assumed to be independent nonidentically distributed. They also derive Massart’s i","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"44 5-6","pages":"440-441"},"PeriodicalIF":0.9,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44097078","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":"Functional principal component analysis for cointegrated functional time series","authors":"Won-Ki Seo","doi":"10.1111/jtsa.12707","DOIUrl":"10.1111/jtsa.12707","url":null,"abstract":"<p>Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool. Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel FPCA-based tests for examining essential properties of cointegrated functional time series.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 2","pages":"320-330"},"PeriodicalIF":0.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49248682","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":"Call for Papers: Special Issue on Recent Developments in Time Series Methods for Detecting Bubbles and Crashes","authors":"Robert Taylor","doi":"10.1111/jtsa.12708","DOIUrl":"10.1111/jtsa.12708","url":null,"abstract":"","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 2","pages":"163"},"PeriodicalIF":0.9,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49659708","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 testing approach to clustering scalar time series","authors":"Daniel Peña,&nbsp;Ruey S. Tsay","doi":"10.1111/jtsa.12706","DOIUrl":"10.1111/jtsa.12706","url":null,"abstract":"<p>This article considers clustering stationary scalar time series using their marginal properties and a hierarchical method. Two major issues involved are to detect the existence of clusters and to determine their number. We propose a new test statistic for detecting whether a data set consists of multiple clusters and a new procedure to determine the number of clusters. The proposed method is based on the jumps, that is, the increments, in the heights of the dendrogram when a hierarchical clustering is applied to the data. We use autoregressive sieve bootstrap to obtain a reference distribution of the test statistics and propose an iterative procedure to find the number of clusters. The clusters found are internally homogeneous according to the test statistics used in the analysis. The performance of the proposed procedure in finite samples is investigated by Monte Carlo simulations and illustrated by some empirical examples. Comparisons with some existing methods for selecting the number of clusters are also investigated.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"44 5-6","pages":"667-685"},"PeriodicalIF":0.9,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46933943","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":"Estimation on unevenly spaced time series","authors":"Liudas Giraitis,&nbsp;Fulvia Marotta","doi":"10.1111/jtsa.12704","DOIUrl":"10.1111/jtsa.12704","url":null,"abstract":"<p>In many different fields realizations of stationary time series might be recorded at irregular points in time, resulting in observed unevenly spaced samples. These missing observations can happen for several reasons, depending on the mechanisms that record the data or external conditions that force the missing observations. In this article, we first focus on the question if we can estimate the mean of a stationary time series when data are not equally spaced. We show that any unevenly spaced sample can be used to estimate the mean of an underlying stationary linear time series. Specifically, we do not impose any restrictions on sampling structure and times, as long as they are independent of the underlying time series. We provide an expression for the sample mean estimator and we establish its asymptotic properties and the central limit theorem. Subsequently we studentize estimation which allows to build confidence intervals for the mean. Finite sample properties of the estimator for the mean are investigated in a Monte Carlo study which confirms good performance of such estimation procedure.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"44 5-6","pages":"556-577"},"PeriodicalIF":0.9,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42297921","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}