Journal of Time Series Analysis最新文献

{"title":"Special Issue of the Journal of Time Series Analysis in Honor of Professor Masanobu Taniguchi","authors":"Marc Hallin, Yoshihide Kakizawa, 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}

{"title":"Weighted l1-Penalized Corrected Quantile Regression for High-Dimensional Temporally Dependent Measurement Errors","authors":"Monika Bhattacharjee,&nbsp;Nilanjan Chakraborty,&nbsp;Hira L. Koul","doi":"10.1111/jtsa.12703","DOIUrl":"10.1111/jtsa.12703","url":null,"abstract":"<p>This article derives some large sample properties of weighted <math>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>l</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow></math>-penalized corrected quantile estimators of the regression parameter vector in a high-dimensional errors in variables (EIVs) linear regression model. In this model, the number of predictors <math>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow></math> depends on the sample size <math>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow></math> and tends to infinity, generally at a faster rate than <math>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow></math>, as <math>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow></math> tends to infinity. Moreover, the measurement errors in the covariates are assumed to have linear stationary temporal dependence and known Laplace marginal distribution while the regression errors are assumed to be independent non-identically distributed random variables having possibly heavy tails. The article discusses some rates of consistency of these estimators, a model consistency result and an appropriate data adaptive algorithm for obtaining a suitable choice of weights. A simulation study assesses the finite sample performance of some of the proposed estimators. This article also contains analogs of Massart's inequality for independent and short memory moving average predictors, which is instrumental in establishing the said consistency rates of the above mentioned estimators in the current setup of high dimensional EIVs regression models.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42440178","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 for symmetric correlation matrices with applications to factor models","authors":"Nan-Jung Hsu,&nbsp;Lai Heng Sim,&nbsp;Ruey S. Tsay","doi":"10.1111/jtsa.12702","DOIUrl":"10.1111/jtsa.12702","url":null,"abstract":"Factor models have been widely used in recent years to model high‐dimensional spatio‐temporal data. However, the validity of employing factor models in a specific application has received less attention. This article proposes test statistics for testing the symmetry in cross‐correlation matrices of a high‐dimensional stochastic process implied by exact factor models. A rejection of symmetry indicates that the use of an exact factor model is questionable. Both simulations and real examples are used to demonstrate the applications and to study the finite‐sample performance of the proposed test statistics. Empirical results show that the proposed test statistics are effective in identifying cases where exact factor models are not appropriate, providing valuable guidance for choosing factor models in a high‐dimensional setting.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45524669","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":"Nonlinear kernel mode-based regression for dependent data","authors":"Tao Wang","doi":"10.1111/jtsa.12700","DOIUrl":"10.1111/jtsa.12700","url":null,"abstract":"<p>Under stationary <math>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow></math>-mixing dependent samples, we in this article develop a novel nonlinear regression based on mode value for time series sequences to achieve robustness without sacrificing estimation efficiency. The estimation process is built on a kernel-based objective function with a constant bandwidth (tuning parameter) that is independent of sample size and can be adjusted to maximize efficiency. The asymptotic distribution of the resultant estimator is established under suitable conditions, and the convergence rate is demonstrated to be the same as that in nonlinear mean regression. To numerically estimate the kernel mode-based regression, we develop a modified modal-expectation-maximization algorithm in conjunction with Taylor expansion. A robust Wald-type test statistic derived from the resulting estimator is also provided, along with its asymptotic distribution for the null and alternative hypotheses. The local robustness of the proposed estimation procedure is studied using influence function analysis, and the good finite sample performance of the newly suggested model is verified through Monte Carlo simulations. We finally combine the recommended kernel mode-based regression with neural networks to develop a kernel mode-based neural networks model, the performance of which is evidenced by an empirical examination of exchange rate prediction.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42733193","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":"Correcting the bias of the sample cross-covariance estimator","authors":"Yifan Li","doi":"10.1111/jtsa.12701","DOIUrl":"10.1111/jtsa.12701","url":null,"abstract":"<p>We derive the finite sample bias of the sample cross-covariance estimator based on a stationary vector-valued time series with an unknown mean. This result leads to a bias-corrected estimator of cross-covariances constructed from linear combinations of sample cross-covariances, which can in theory correct for the bias introduced by the first <math>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow></math> lags of cross-covariance with any <math>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow></math> not larger than the sample size minus two. Based on the bias-corrected cross-covariance estimator, we propose a bias-adjusted long run covariance (LRCOV) estimator. We derive the asymptotic relations between the bias-corrected estimators and their conventional Counterparts in both the small-<math>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow></math> and the fixed-<math>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow></math> limit. Simulation results show that: (i) our bias-corrected cross-covariance estimators are very effective in eliminating the finite sample bias of their conventional counterparts, with potential mean squared error reduction when the data generating process is highly persistent; and (ii) the bias-adjusted LRCOV estimators can have superior performance to their conventional counterparts with a smaller bias and mean squared error.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135792157","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":"Announcement: Call for Papers for Special Issue in Honour of Stephen J. Taylor","authors":"Torben Andersen,&nbsp;Kim Christensen,&nbsp;Ingmar Nolte","doi":"10.1111/jtsa.12693","DOIUrl":"10.1111/jtsa.12693","url":null,"abstract":"","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45926665","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}