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Journal of Time Series Analysis最新文献

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Dependence properties of stochastic volatility models 随机波动模型的依赖特性
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-08-12 DOI: 10.1111/jtsa.12765
Piotr Kokoszka, Neda Mohammadi, Haonan Wang
{"title":"Dependence properties of stochastic volatility models","authors":"Piotr Kokoszka, Neda Mohammadi, Haonan Wang","doi":"10.1111/jtsa.12765","DOIUrl":"https://doi.org/10.1111/jtsa.12765","url":null,"abstract":"The concepts of physical dependence and approximability have been extensively used over the past two decades to quantify nonlinear dependence in time series. We show that most stochastic volatility models satisfy both dependence conditions, even if their realizations take values in abstract Hilbert spaces, thus covering univariate, multi‐variate and functional models. Our results can be used to apply to general stochastic volatility models a multitude of inferential procedures established for Bernoulli shifts.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206458","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
General estimation results for tdVARMA array models tdVARMA阵列模型的一般估算结果
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-07-29 DOI: 10.1111/jtsa.12761
Abdelkamel Alj, Rajae Azrak, Guy Mélard
{"title":"General estimation results for tdVARMA array models","authors":"Abdelkamel Alj, Rajae Azrak, Guy Mélard","doi":"10.1111/jtsa.12761","DOIUrl":"https://doi.org/10.1111/jtsa.12761","url":null,"abstract":"The article will focus on vector autoregressive‐moving average (VARMA) models with time‐dependent coefficients (td) to represent general nonstationary time series, not necessarily Gaussian. The coefficients depend on time, possibly on the length of the series , hence the name tdVARMA for the models, but not necessarily on the rescaled time . As a consequence of the dependency on of the model, we need to consider array processes instead of stochastic processes. Under appropriate assumptions, it is shown that a Gaussian quasi‐maximum likelihood estimator is consistent in probability and asymptotically normal. The theoretical results are illustrated using three examples of bivariate processes, the first two with marginal heteroscedasticity. The first example is a tdVAR(1) process while the second example is a tdVMA(1) process. In these two cases, the finite‐sample behavior is checked via a Monte Carlo simulation study. The results are compatible with the asymptotic properties even for small . A third example shows the application of the tdVARMA models for a real time series.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141864063","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
Estimating a common break point in means for long‐range dependent panel data 估算长程依存面板数据均值的共同断点
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-07-20 DOI: 10.1111/jtsa.12763
Daiqing Xi, Cheng‐Der Fuh, Tianxiao Pang
{"title":"Estimating a common break point in means for long‐range dependent panel data","authors":"Daiqing Xi, Cheng‐Der Fuh, Tianxiao Pang","doi":"10.1111/jtsa.12763","DOIUrl":"https://doi.org/10.1111/jtsa.12763","url":null,"abstract":"In this article, we study a common break point in means for panel data with cross‐sectional dependence through unobservable common factors, in which the observations are long‐range dependent over time and are heteroscedastic and may have different degrees of dependence across panels. First, we adopt the least squares method without taking the data features into account to estimate the common break point and to see how the data features affect the asymptotic behaviors of the estimator. Then, an iterative least squares estimator of the common break point which accounts for the common factors in the estimation procedure is examined. Our theoretical results reveal that: (1) There is a trade‐off between the overall break magnitude of the panel data and the long‐range dependence for both estimators. (2) The second estimation procedure can eliminate the effects of common factors from the asymptotic behaviors of the estimator successfully, but it cannot improve the rate of convergence of the estimator in most cases. Moreover, Monte Carlo simulations are given to illustrate the theoretical results on finite‐sample performance.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738816","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 trinomial difference autoregressive process for the bounded ℤ‐valued time series 有界ℤ值时间序列的三叉差分自回归过程
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-07-11 DOI: 10.1111/jtsa.12762
Huaping Chen, Zifei Han, Fukang Zhu
{"title":"A trinomial difference autoregressive process for the bounded ℤ‐valued time series","authors":"Huaping Chen, Zifei Han, Fukang Zhu","doi":"10.1111/jtsa.12762","DOIUrl":"https://doi.org/10.1111/jtsa.12762","url":null,"abstract":"This article tackles the modeling challenge of bounded ‐valued time series by proposing a novel trinomial difference autoregressive process. This process not only maintains the autocorrelation structure presenting in the classical binomial GARCH model, but also facilitates the analysis of bounded ‐valued time series with negative or positive correlation. We verify the stationarity and ergodicity of the couple process (comprising both the observed process and its conditional mean process) while also presenting several stochastic properties. We further discuss the conditional maximum likelihood estimation and establish their asymptotic properties. The effectiveness of these estimators is assessed through simulation studies, followed by the application of the proposed models to two real datasets.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141610983","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
Bootstrapping non‐stationary and irregular time series using singular spectral analysis 利用奇异谱分析引导非平稳和不规则时间序列
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-07-04 DOI: 10.1111/jtsa.12759
Don S. Poskitt
{"title":"Bootstrapping non‐stationary and irregular time series using singular spectral analysis","authors":"Don S. Poskitt","doi":"10.1111/jtsa.12759","DOIUrl":"https://doi.org/10.1111/jtsa.12759","url":null,"abstract":"This article investigates the consequences of using Singular Spectral Analysis (SSA) to construct a time series bootstrap. The bootstrap replications are obtained via a SSA decomposition obtained using rescaled trajectories (RT‐SSA), a procedure that is particularly useful in the analysis of time series that exhibit nonlinear, non‐stationary and intermittent or transient behaviour. The theoretical validity of the RT‐SSA bootstrap when used to approximate the sampling properties of a general class of statistics is established under regularity conditions that encompass a very broad range of data generating processes. A smeared and a boosted version of the RT‐SSA bootstrap are also presented. Practical implementation of the bootstrap is considered and the results are illustrated using stationary, non‐stationary and irregular time series examples.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549046","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
Selecting the number of factors in multi‐variate time series 选择多变量时间序列中的因子数
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-06-26 DOI: 10.1111/jtsa.12760
Angela Caro, Daniel Peña
{"title":"Selecting the number of factors in multi‐variate time series","authors":"Angela Caro, Daniel Peña","doi":"10.1111/jtsa.12760","DOIUrl":"https://doi.org/10.1111/jtsa.12760","url":null,"abstract":"How many factors are there? It is a critical question that researchers and practitioners deal with when estimating factor models. We proposed a new eigenvalue ratio criterion for the number of factors in static approximate factor models. It considers a pooled squared correlation matrix which is defined as a weighted combination of the main observed squared correlation matrices. Theoretical results are given to justify the expected good properties of the criterion, and a Monte Carlo study shows its good finite sample performance in different scenarios, depending on the idiosyncratic error structure and factor strength. We conclude comparing different criteria in a forecasting exercise with macroeconomic data.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141511250","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
Estimation of non‐smooth non‐parametric estimating equations models with dependent data 具有依存数据的非平稳非参数估计方程模型的估计
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-06-05 DOI: 10.1111/jtsa.12758
Francesco Bravo
{"title":"Estimation of non‐smooth non‐parametric estimating equations models with dependent data","authors":"Francesco Bravo","doi":"10.1111/jtsa.12758","DOIUrl":"https://doi.org/10.1111/jtsa.12758","url":null,"abstract":"This article considers estimation of non‐smooth possibly overidentified non‐parametric estimating equations models with weakly dependent data. The estimators are based on a kernel smoothed version of the generalized empirical likelihood and the generalized method of moments approaches. The article derives the asymptotic normality of both estimators and shows that the proposed local generalized empirical likelihood estimator is more efficient than the local generalized moment estimator unless a two‐step procedure is used. The article also proposes novel tests for the correct specification of the considered model that are shown to have power against local alternatives and are consistent against fixed alternatives. Monte Carlo simulations and an empirical application illustrate the finite sample properties and applicability of the proposed estimators and test statistics.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383025","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
Testing for the extent of instability in nearly unstable processes 测试几乎不稳定过程的不稳定程度
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-06-01 DOI: 10.1111/jtsa.12751
Marie Badreau, Frédéric Proïa
{"title":"Testing for the extent of instability in nearly unstable processes","authors":"Marie Badreau, Frédéric Proïa","doi":"10.1111/jtsa.12751","DOIUrl":"https://doi.org/10.1111/jtsa.12751","url":null,"abstract":"This article deals with unit root issues in time series analysis. It has been known for a long time that unit root tests may be flawed when a series although stationary has a root close to unity. That motivated recent papers dedicated to autoregressive processes where the bridge between stability and instability is expressed by means of time‐varying coefficients. The process we consider has a companion matrix with spectral radius satisfying , a situation described as ‘nearly‐unstable’. The question we investigate is: given an observed path supposed to come from a nearly unstable process, is it possible to test for the ‘extent of instability’, i.e. to test how close we are to the unit root? In this regard, we develop a strategy to evaluate and to test for : ‘’ against : ‘’ when lies in an inner ‐neighborhood of the unity, for some . Empirical evidence is given about the advantages of the flexibility induced by such a procedure compared to the common unit root tests. We also build a symmetric procedure for the usually left out situation where the dominant root lies around .","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189682","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
Fractional gaussian noise: Spectral density and estimation methods 分数高斯噪声：频谱密度和估计方法
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-20 DOI: 10.1111/jtsa.12750
Shuping Shi, Jun Yu, Chen Zhang
{"title":"Fractional gaussian noise: Spectral density and estimation methods","authors":"Shuping Shi, Jun Yu, Chen Zhang","doi":"10.1111/jtsa.12750","DOIUrl":"https://doi.org/10.1111/jtsa.12750","url":null,"abstract":"The fractional Brownian motion (fBm) process, governed by a fractional parameter , is a continuous‐time Gaussian process with its increment being the fractional Gaussian noise (fGn). This article first provides a computationally feasible expression for the spectral density of fGn. This expression enables us to assess the accuracy of a range of approximation methods, including the truncation method, Paxson's approximation, and the Taylor series expansion at the near‐zero frequency. Next, we conduct an extensive Monte Carlo study comparing the finite sample performance and computational cost of alternative estimation methods for under the fGn specification. These methods include two semi‐parametric methods (based on the Taylor series expansion), two versions of the Whittle method (utilising either the computationally feasible expression or Paxson's approximation of the spectral density), a time‐domain maximum likelihood (ML) method (employing a recursive approach for its likelihood calculation), and a change‐of‐frequency method. Special attention is paid to highly anti‐persistent processes with close to zero, which are of empirical relevance to financial volatility modelling. Considering the trade‐off between statistical and computational efficiency, we recommend using either the Whittle ML method based on Paxson's approximation or the time‐domain ML method. We model the log realized volatility dynamics of 40 financial assets in the US market from 2012 to 2019 with fBm. Although all estimation methods suggest rough volatility, the implied degree of roughness varies substantially with the estimation methods, highlighting the importance of understanding the finite sample performance of various estimation methods.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123002","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
Estimation for Markov Chains with Periodically Missing Observations 具有周期性缺失观测数据的马尔可夫链的估计
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-17 DOI: 10.1111/jtsa.12747
Ursula U. Müller, Anton Schick, Wolfgang Wefelmeyer
{"title":"Estimation for Markov Chains with Periodically Missing Observations","authors":"Ursula U. Müller,&nbsp;Anton Schick,&nbsp;Wolfgang Wefelmeyer","doi":"10.1111/jtsa.12747","DOIUrl":"10.1111/jtsa.12747","url":null,"abstract":"<p>When we observe a stationary time series with observations missing at periodic time points, we can still estimate its marginal distribution well, but the dependence structure of the time series may not be recoverable at all, or the usual estimators may have much larger variance than in the fully observed case. We show how non-parametric estimators can often be improved by adding unbiased estimators. We focus on a simple setting, first-order Markov chains on a finite state space, and an observation pattern in which a fixed number of consecutive observations is followed by an observation gap of fixed length, say workdays and weekends. The new estimators perform astonishingly well in some cases, as illustrated with simulations. The approach extends to continuous state space and to higher-order Markov chains.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059171","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
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