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Granger causality tests based on reduced variable information 基于约简变量信息的Granger因果检验
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-03 DOI: 10.1111/jtsa.12720
Neng-Fang Tseng, Ying-Chao Hung, Junji Nakano
{"title":"Granger causality tests based on reduced variable information","authors":"Neng-Fang Tseng,&nbsp;Ying-Chao Hung,&nbsp;Junji Nakano","doi":"10.1111/jtsa.12720","DOIUrl":"10.1111/jtsa.12720","url":null,"abstract":"<p>Granger causality is a classical and important technique for measuring predictability from one group of time series to another by incorporating information of the variables described by a full vector autoregressive (VAR) process. However, in some applications economic forecasts need to be made based on information provided merely by a portion of variates (e.g., removal of a listed stock due to halting, suspension or delisting). This requires a new formulation of forecast based on an embedded subprocess of VAR, whose theoretical properties are often difficult to obtain. To avoid the issue of identifying the VAR subprocess, we propose a computation-based approach so that sophisticated predictions can be made by utilizing a reduced variable information set estimated from sampled data. Such estimated information set allows us to develop a suitable statistical hypothesis testing procedure for characterizing all designated Granger causal relationships, as well as a useful graphical tool for presenting the causal structure over the prediction horizon. Finally, simulated data and a real example from the stock markets are used to illustrate the proposed method.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49077711","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
Stationary Jackknife 固定式升降刀
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-25 DOI: 10.1111/jtsa.12714
Weilian Zhou, Soumendra Lahiri
{"title":"Stationary Jackknife","authors":"Weilian Zhou,&nbsp;Soumendra Lahiri","doi":"10.1111/jtsa.12714","DOIUrl":"10.1111/jtsa.12714","url":null,"abstract":"<p>Variance estimation is an important aspect in statistical inference, especially in the dependent data situations. Resampling methods are ideal for solving this problem since these do not require restrictive distributional assumptions. In this paper, we develop a novel resampling method in the Jackknife family called the <span>stationary jackknife</span>. It can be used to estimate the variance of a statistic in the cases where observations are from a general stationary sequence. Unlike the moving block jackknife, the <span>stationary jackknife</span> computes the jackknife replication by deleting a variable length block and the length has a truncated geometric distribution. Under appropriate assumptions, we can show the <span>stationary jackknife</span> variance estimator is a consistent estimator for the case of the sample mean and, more generally, for a class of nonlinear statistics. Further, the <span>stationary jackknife</span> is shown to provide reasonable variance estimation for a wider range of expected block lengths when compared with the moving block jackknife by simulation.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45567502","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
Additive autoregressive models for matrix valued time series 矩阵值时间序列的加性自回归模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-25 DOI: 10.1111/jtsa.12718
Hong-Fan Zhang
{"title":"Additive autoregressive models for matrix valued time series","authors":"Hong-Fan Zhang","doi":"10.1111/jtsa.12718","DOIUrl":"10.1111/jtsa.12718","url":null,"abstract":"<p>In this article, we develop additive autoregressive models (Add-ARM) for the time series data with matrix valued predictors. The proposed models assume separable row, column and lag effects of the matrix variables, attaining stronger interpretability when compared with existing bilinear matrix autoregressive models. We utilize the Gershgorin's circle theorem to impose some certain conditions on the parameter matrices, which make the underlying process strictly stationary. We also introduce the alternating least squares estimation method to solve the involved equality constrained optimization problems. Asymptotic distributions of the parameter estimators are derived. In addition, we employ hypothesis tests to run diagnostics on the parameter matrices. The performance of the proposed models and methods is further demonstrated through simulations and real data analysis.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49545023","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
Wasserstein distance bounds on the normal approximation of empirical autocovariances and cross-covariances under non-stationarity and stationarity 非平稳性和平稳性条件下经验自协方差和互协方差正态逼近的Wasserstein距离界
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-18 DOI: 10.1111/jtsa.12716
Andreas Anastasiou, Tobias Kley
{"title":"Wasserstein distance bounds on the normal approximation of empirical autocovariances and cross-covariances under non-stationarity and stationarity","authors":"Andreas Anastasiou,&nbsp;Tobias Kley","doi":"10.1111/jtsa.12716","DOIUrl":"10.1111/jtsa.12716","url":null,"abstract":"<p>The autocovariance and cross-covariance functions naturally appear in many time series procedures (e.g. autoregression or prediction). Under assumptions, empirical versions of the autocovariance and cross-covariance are asymptotically normal with covariance structure depending on the second- and fourth-order spectra. Under non-restrictive assumptions, we derive a bound for the Wasserstein distance of the finite-sample distribution of the estimator of the autocovariance and cross-covariance to the Gaussian limit. An error of approximation to the second-order moments of the estimator and an <span></span><math>\u0000 <mrow>\u0000 <mi>m</mi>\u0000 </mrow></math>-dependent approximation are the key ingredients to obtain the bound. As a worked example, we discuss how to compute the bound for causal autoregressive processes of order 1 with different distributions for the innovations. To assess our result, we compare our bound to Wasserstein distances obtained via simulation.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41744961","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
On vector linear double autoregression 关于向量线性双自回归
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-15 DOI: 10.1111/jtsa.12717
Yuchang Lin, Qianqian Zhu
{"title":"On vector linear double autoregression","authors":"Yuchang Lin,&nbsp;Qianqian Zhu","doi":"10.1111/jtsa.12717","DOIUrl":"10.1111/jtsa.12717","url":null,"abstract":"<p>This article proposes a vector linear double autoregressive (VLDAR) model with the constant conditional correlation specification, which can capture the co-movement of multiple series and jointly model their conditional means and volatilities. The strict stationarity of the new model is discussed, and a self-weighted Gaussian quasi-maximum likelihood estimator (SQMLE) is proposed for estimation. To reduce the computational cost, especially when the series dimension is large, a block coordinate descent (BCD) algorithm is provided to calculate the SQMLE. Moreover, a Bayesian information criterion is introduced for order selection, and a multi-variate mixed portmanteau test is constructed for checking the adequacy of fitted models. All asymptotic properties for estimation, model selection, and portmanteau test are established without any moment restrictions imposed on the data process, which makes the new model and its inference tools applicable for heavy-tailed data. Simulation experiments are conducted to evaluate the finite-sample performance of the proposed methodology, and an empirical example on analyzing S&amp;P 500 sector indices is presented to illustrate the usefulness of the new model in contrast with competitors.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47982701","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
Margin-closed vector autoregressive time series models 边际闭合向量自回归时间序列模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-09 DOI: 10.1111/jtsa.12712
Lin Zhang, Harry Joe, Natalia Nolde
{"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":null,"pages":null},"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}
引用次数: 0
Goodness-of-fit tests for the multivariate Student-t distribution based on i.i.d. data, and for GARCH observations 基于 i.i.d. 数据的多元 Student-t 分布和 GARCH 观察结果的拟合优度检验
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-08-03 DOI: 10.1111/jtsa.12713
Simos Meintanis, Bojana Milošević, Marko Obradović, Mirjana Veljović
{"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":null,"pages":null},"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}
引用次数: 0
Editorial Announcement 编辑公告
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-07-30 DOI: 10.1111/jtsa.12715
Robert Taylor
{"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":null,"pages":null},"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}
引用次数: 0
Adjustment coefficients and exact rational expectations in cointegrated vector autoregressive models 协整向量自回归模型的调整系数与精确理性期望
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-07-24 DOI: 10.1111/jtsa.12705
Søren Johansen, Anders Rygh Swensen
{"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":null,"pages":null},"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}
引用次数: 0
Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates 含协变量的半参数泛函系数模型的常参数检验
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-07-20 DOI: 10.1111/jtsa.12709
Shan Dai, Ngai Hang Chan
{"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":null,"pages":null},"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}
引用次数: 0
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