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A new estimator for LARCH processes 一种新的LARCH过程估计方法
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-03-27 DOI: 10.1111/jtsa.12689
Jean-Marc Bardet
{"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":"45 1","pages":"103-132"},"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}
引用次数: 1
Inference for high-dimensional linear models with locally stationary error processes 具有局部静止误差过程的高维线性模型推理
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-03-21 DOI: 10.1111/jtsa.12686
Jiaqi Xia, Yu Chen, Xiao Guo
{"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":"45 1","pages":"78-102"},"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}
引用次数: 0
A first order continuous time VAR with random coefficients 具有随机系数的一阶连续时间VAR
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-03-16 DOI: 10.1111/jtsa.12685
Milena Hoyos
{"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":"45 1","pages":"57-77"},"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}
引用次数: 0
Optimal estimating function for weak location-scale dynamic models 弱位置尺度动态模型的最优估计函数
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-03-12 DOI: 10.1111/jtsa.12684
Christian Francq, Jean-Michel Zakoïan
{"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":"44 5-6","pages":"533-555"},"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}
引用次数: 1
Multi-purpose open-end monitoring procedures for multivariate observations based on the empirical distribution function 基于经验分布函数的多变量观测的多用途开放式监测程序
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-03-06 DOI: 10.1111/jtsa.12683
Mark Holmes, Ivan Kojadinovic, Alex Verhoijsen
{"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":"45 1","pages":"27-56"},"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}
引用次数: 0
A multiplicative thinning-based integer-valued GARCH model 一种基于乘性稀疏的整值GARCH模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-03-05 DOI: 10.1111/jtsa.12682
Abdelhakim Aknouche, Manuel G. Scotto
{"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":"45 1","pages":"4-26"},"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}
引用次数: 3
Editorial announcement 编辑公告
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-03-03 DOI: 10.1111/jtsa.12681
{"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":"44 3","pages":"261"},"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}
引用次数: 0
Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series 自回归条件比例：（0,1）值时间序列的乘性误差模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-01-19 DOI: 10.1111/jtsa.12679
Abdelhakim Aknouche, Stefanos Dimitrakopoulos
{"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":"44 4","pages":"393-417"},"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}
引用次数: 1
Geometric ergodicity and conditional self-weighted M-estimator of a GRCAR( p ) model with heavy-tailed errors 重尾误差GRCAR（p）模型的几何遍历性和条件自加权M估计
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-01-19 DOI: 10.1111/jtsa.12680
Xiaoyan Li, Jiazhu Pan, Anchao Song
{"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":"44 4","pages":"418-436"},"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}
引用次数: 0
Regime switching models for circular and linear time series 循环和线性时间序列的状态切换模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-01-16 DOI: 10.1111/jtsa.12678
Andrew Harvey, Dario Palumbo
{"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":"44 4","pages":"374-392"},"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}
引用次数: 1
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