Journal of Time Series Analysis最新文献

{"title":"Estimation of the Long-Run Variance of Nonlinear Time Series With an Application to Change Point Analysis","authors":"Vaidotas Characiejus,&nbsp;Piotr Kokoszka,&nbsp;Xiangdong Meng","doi":"10.1111/jtsa.12844","DOIUrl":"https://doi.org/10.1111/jtsa.12844","url":null,"abstract":"<div>\u0000 \u0000 <p>For a broad class of nonlinear time series known as Bernoulli shifts, we establish the asymptotic normality of the smoothed periodogram estimator of the long-run variance. This estimator uses only a narrow band of Fourier frequencies around the origin and so has been extensively used in local Whittle estimation. Existing asymptotic normality results apply only to linear time series, so our work substantially extends the scope of the applicability of the smoothed periodogram estimator. As an illustration, we apply it to a test of changes in mean against long-range dependence. A simulation study is also conducted to illustrate the performance of the test for nonlinear time series.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 5","pages":"939-951"},"PeriodicalIF":1.2,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719294","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":"Markov Determinantal Point Process for Dynamic Random Sets","authors":"Christian Gouriéroux,&nbsp;Yang Lu","doi":"10.1111/jtsa.12823","DOIUrl":"https://doi.org/10.1111/jtsa.12823","url":null,"abstract":"<p>The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two neighboring terms follows the LDPP, both the transition distribution and the stationary distribution belong to the LDPP family as well. We explore how their parameterizations are related. We investigate various constrained model specifications, develop procedures for exploratory analysis of a series of sets, and discuss model estimation on both simulated data and topics published in National Geographic.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"784-802"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12823","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247817","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":"Self-Normalized KPSS Tests With Power Enhancement","authors":"JunYi Peng-Zhou,&nbsp;Xiaojun Song","doi":"10.1111/jtsa.12818","DOIUrl":"https://doi.org/10.1111/jtsa.12818","url":null,"abstract":"<div>\u0000 \u0000 <p>Much research has focused on testing the null hypothesis of stationarity against the unit root alternative. In this paper, we propose a novel class of self-normalized KPSS tests without needing a consistent estimation of the long-run variance. Under persistent autocorrelation, the widely used heteroskedasticity and autocorrelation consistent long-run variance estimator proposed by Newey and West [<i>Econometrica</i> (1987) Vol. 55, pp. 703–708], Newey and West [<i>The Review of Economic Studies</i> (1994) Vol. 61, pp. 631–653], and Andrews [<i>Econometrica</i> (1991) Vol. 59, pp. 817–858] may not be reliable and often lead to tests with size distortions and power losses in finite samples. In addition, the practitioner has to choose the truncation lag that is ultimately arbitrary. To improve the finite sample performance of the stationarity tests and make them robust to realistic amounts of dependence, we propose the use of self-normalizing methods, for example, the range-based self-normalization by Hong et al. [<i>Journal of Econometrics</i> (2024) Vol. 238, 105603] and the fixed-<span></span><math>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow></math> asymptotics by Kiefer and Vogelsang [<i>Econometric Theory</i> (2005) Vol. 21, pp. 1130–1164] and Amsler, Schmidt, and Vogelsang [<i>Journal of Time Series Econometrics</i> (2009) Vol. 1, pp. 1–44], to control the effect of the long-run variance. The self-normalized tests are inconsistent in that, under the unit root alternative, they do not diverge as the sample size approaches infinity. To recover the consistency of the tests, we devise a mechanism similar to the power enhancement mechanism proposed by Fan, Liao, and Yao [<i>Econometrica</i> (2015) Vol. 83, pp. 1497–1541]. Under the null hypothesis, this mechanism is asymptotically negligible. However, under the alternative hypothesis, the mechanism diverges as the sample size increases. We show that this mechanism also enhances the power of the self-normalized stationarity tests. A simulation study and an empirical application demonstrate the merits of the approach advocated.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"751-773"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247831","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":"Automated Bandwidth Selection for Inference in Linear Models With Time-Varying Coefficients","authors":"Charisios Grivas,&nbsp;Zacharias Psaradakis","doi":"10.1111/jtsa.12842","DOIUrl":"https://doi.org/10.1111/jtsa.12842","url":null,"abstract":"<p>The problem of selecting the smoothing parameter, or bandwidth, for kernel-based estimators of time-varying coefficients in linear models with possibly endogenous explanatory variables is considered. We examine automated bandwidth selection by means of cross-validation, a nonparametric variant of Akaike's information criterion, and bootstrap procedures based on wild bootstrap and dependent wild bootstrap resampling schemes. Our simulations show that data-driven selectors based on cross-validation and the dependent wild bootstrap are the most successful overall in a variety of settings that are relevant in econometrics. Empirical examples illustrate the practical use of the automated procedures.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"854-875"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12842","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247846","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":"Time Series Models on Compact Spaces, With an Application to Dynamic Modeling of Relative Abundance Data in Ecology","authors":"Guillaume Franchi,&nbsp;Lionel Truquet","doi":"10.1111/jtsa.12836","DOIUrl":"https://doi.org/10.1111/jtsa.12836","url":null,"abstract":"<div>\u0000 \u0000 <p>Motivated by the dynamic modeling of relative abundance data in ecology, we introduce a general approach for modeling stationary Markovian or non-Markovian time series on (relatively) compact spaces, such as a hypercube, the simplex, or a sphere in a Euclidean space. Our approach is based on a general construction of infinite memory models, called chains with complete connections. The two main ingredients involved in our generic construction are a parametric family of probability distributions on the state space and a map from the state space to the parameter space. Our framework encompasses Markovian models, observation-driven models, and more general infinite memory models. Simple conditions ensuring the existence and uniqueness of a stationary and ergodic path are given. We then study in more detail statistical inference in two time series models on the simplex, based on either a Dirichlet or a multivariate logistic-normal conditional distribution. The usefulness of our models to analyze abundance data in ecosystems is also discussed.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"822-838"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247821","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":"Time-Varying Dispersion Integer-Valued GARCH Models","authors":"Wagner Barreto-Souza,&nbsp;Luiza S. C. Piancastelli,&nbsp;Konstantinos Fokianos,&nbsp;Hernando Ombao","doi":"10.1111/jtsa.12838","DOIUrl":"https://doi.org/10.1111/jtsa.12838","url":null,"abstract":"<p>We introduce a general class of INteger-valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing simultaneously time-varying mean and dispersion parameters. We call such models time-varying dispersion INGARCH (tv-DINGARCH) models. More specifically, we consider mixed Poisson INGARCH models and allow for dynamic modeling of both mean and dispersion parameters. We derive conditions to obtain stochastic stability of tv-DINGARCH processes. Additionally, we study maximum likelihood estimation in detail including its asymptotic distribution. A restricted bootstrap procedure is proposed for testing constant dispersion against time-varying dispersion. Monte Carlo simulation studies are presented for checking point estimation, standard errors, and the performance of the restricted bootstrap approach. We apply the tv-DINGARCH process to model the weekly number of reported measles infections in North Rhine-Westphalia, Germany, from January 2001 to May 2013, and compare its performance to the ordinary INGARCH approach.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"839-853"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12838","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247881","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":"Extremely Fast Maximum Likelihood Estimation of High-Order Autoregressive Models","authors":"Daniel F. Schmidt,&nbsp;Enes Makalic","doi":"10.1111/jtsa.12839","DOIUrl":"https://doi.org/10.1111/jtsa.12839","url":null,"abstract":"<p>We consider the problem of exact maximum likelihood estimation of potentially high-order (<span></span><math>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 <mo>&gt;</mo>\u0000 <mn>50</mn>\u0000 </mrow></math>) autoregressive models. We propose an extremely fast coordinate-wise algorithm for fitting autoregressive models. This fast algorithm exploits several properties of the negative log-likelihood when parameterised in terms of partial autocorrelations. We consider extensions to learning a single autoregressive model from multiple time series and to the more general case of regressions with autoregressive residuals. An implementation of the coordinate-wise descent algorithm is shown to be the orders of magnitude faster than competing algorithms and appears to be the fastest known algorithm for maximum likelihood estimation of autoregressive models.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"876-884"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247797","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":"Oracally Efficient Estimation and Consistent Model Selection for Spatial ARMA Process With Bivariate Trend","authors":"Tong Zhang,&nbsp;Yuanyuan Zhang,&nbsp;Chen Zhong","doi":"10.1111/jtsa.12847","DOIUrl":"https://doi.org/10.1111/jtsa.12847","url":null,"abstract":"<div>\u0000 \u0000 <p>The analysis of nonlinearity in spatial and spatial-temporal data continues to be a challenging topic. This article introduces a two-step estimation procedure for modeling nonstationary spatial processes that comprise a smooth bivariate trend function and a spatial autoregressive and moving average (SRAMA) error term. To remove the bivariate trend from the observed process, we apply the cutting-edge bivariate penalized spline method. The modified maximum likelihood estimator based on the residuals, as suggested by Yao and Brockwell (2006), is shown to be consistent and oracle efficient, achieving the same efficiency asymptotically as if the true trend function were known and removed to obtain the SARMA errors. Furthermore, we establish the consistency of Bayesian information criteria for model selection concerning the residual sequence. The finite sample performance of the proposed approach is evaluated with simulations and real data.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"885-903"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247805","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":"On Exponential-Family INGARCH Models","authors":"Alan Huang,&nbsp;Thomas Fung,&nbsp;Kyle Macaskill,&nbsp;Rowan Aukes","doi":"10.1111/jtsa.12831","DOIUrl":"https://doi.org/10.1111/jtsa.12831","url":null,"abstract":"<p>A range of integer-valued generalised autoregressive conditional heteroscedastic (INGARCH) models have been proposed in the literature, including those based on conditional Poisson, negative binomial and Conway-Maxwell-Poisson distributions. This note considers a larger class of exponential-family INGARCH models, showing that maximum empirical likelihood estimation over this semiparametric class of models can lead to consistent estimates as well as unbiased inferences on model parameters. The proposed framework is tested on two data analysis examples and a simulation study.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"912-918"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12831","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247765","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":"Partial Sums of Almost Overdifferenced, Near-Stationary Processes With Time-Varying Properties","authors":"Matei Demetrescu,&nbsp;Mehdi Hosseinkouchack","doi":"10.1111/jtsa.12817","DOIUrl":"https://doi.org/10.1111/jtsa.12817","url":null,"abstract":"<div>\u0000 \u0000 <p>This note analyzes the limiting behavior of suitably normalized partial sums of processes with an ARMA(1,1) structure when the AR and MA roots are in various types of neighborhoods of 1 and <span></span><math>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow></math>, respectively. In doing so, we allow for time-varying volatility and persistence. Our results show how the radii of neighborhoods in the MA and AR filters interact and affect the corresponding invariance principles.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 4","pages":"904-911"},"PeriodicalIF":1.0,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148247809","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}