Journal of Time Series Analysis最新文献

{"title":"On the asymptotic behavior of bubble date estimators","authors":"Eiji Kurozumi,&nbsp;Anton Skrobotov","doi":"10.1111/jtsa.12672","DOIUrl":"10.1111/jtsa.12672","url":null,"abstract":"<p>In this study, we extend the three-regime bubble model of Pang et al. (2021, <i>Journal of Econometrics</i>, 221(1):227–311) to allow the forth regime followed by the unit root process after recovery. We provide the asymptotic and finite sample justification of the consistency of the collapse date estimator in the two-regime AR(1) model. The consistency allows us to split the sample before and after the date of collapse and to consider the estimation of the date of exuberation and date of recovery separately. We have also found that the limiting behavior of the recovery date varies depending on the extent of explosiveness and recovering.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49449711","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":"On highly skewed fractional log-stable noise sequences and their application","authors":"Harry Pavlopoulos,&nbsp;George Chronis","doi":"10.1111/jtsa.12671","DOIUrl":"10.1111/jtsa.12671","url":null,"abstract":"<p>Considering log-LFSN (log-linear fractional stable noise) sequences <math>\u0000 <msub>\u0000 <mrow>\u0000 <mo>{</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>Y</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>=</mo>\u0000 <msup>\u0000 <mrow>\u0000 <mi>e</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>δ</mi>\u0000 <mo>·</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>+</mo>\u0000 <mi>ε</mi>\u0000 </mrow>\u0000 </msup>\u0000 <mo>}</mo>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 <mo>∈</mo>\u0000 <mi>ℤ</mi>\u0000 </mrow>\u0000 </msub></math>, driven by non-Gaussian one-sided LFSN <math>\u0000 <msub>\u0000 <mrow>\u0000 <mo>{</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>}</mo>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 <mo>∈</mo>\u0000 <mi>ℤ</mi>\u0000 </mrow>\u0000 </msub></math> with constant skewness intensity <math>\u0000 <msub>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 </msub>\u0000 <mo>∈</mo>\u0000 <mo>[</mo>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 <mo>,</mo>\u0000 <mn>1</mn>\u0000 <mo>]</mo></math>, for any <math>\u0000 <mi>δ</mi>\u0000 <mo>∈</mo>\u0000 <mi>ℝ</mi>\u0000 <mo>−</mo>\u0000 <mo>{</mo>\u0000 <mn>0</mn>\u0000 <mo>}</mo></math> and <math>\u0000 <mi>ε</mi>\u0000 <mo>∈</mo>\u0000 <mi>ℝ</mi></math>, we show that the auto-covariance function (ACVF) <math>\u0000 <msub>\u0000 <mrow>\u0000 <mo>{</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>γ</mi>\u0000 </mrow>\u0000 ","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48740120","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":"Corrigendum to the article “Regular multidimensional stationary time series”","authors":"Tamás Szabados","doi":"10.1111/jtsa.12670","DOIUrl":"10.1111/jtsa.12670","url":null,"abstract":"<p>In Theorem 2.1 which was the main result of the article it was implicitly assumed that for any regular <math>\u0000 <mrow>\u0000 <mi>d</mi>\u0000 </mrow></math>-dimensional weakly stationary time series <math>\u0000 <mrow>\u0000 <mo>{</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>}</mo>\u0000 </mrow></math> of rank <math>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow></math>, <math>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>≤</mo>\u0000 <mi>r</mi>\u0000 <mo>≤</mo>\u0000 <mi>d</mi>\u0000 </mrow></math>, there exists an analytic spectral factor <math>\u0000 <mrow>\u0000 <mi>Φ</mi>\u0000 <mo>(</mo>\u0000 <mi>z</mi>\u0000 <mo>)</mo>\u0000 </mrow></math> of the form <math>\u0000 <mrow>\u0000 <mi>Φ</mi>\u0000 <mo>(</mo>\u0000 <msup>\u0000 <mrow>\u0000 <mi>e</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mi>i</mi>\u0000 <mi>ω</mi>\u0000 </mrow>\u0000 </msup>\u0000 <mo>)</mo>\u0000 <mo>=</mo>\u0000 <msqrt>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mi>π</mi>\u0000 </mrow>\u0000 </msqrt>\u0000 <mspace></mspace>\u0000 <mover>\u0000 <mrow>\u0000 <mi>U</mi>\u0000 </mrow>\u0000 <mo>˜</mo>\u0000 </mover>\u0000 <mo>(</mo>\u0000 <mi>ω</mi>\u0000 <mo>)</mo>\u0000 <msubsup>\u0000 <mrow>\u0000 <mi>Λ</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>/</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msubsup>\u0000 <mo>(</mo>\u0000 <mi>ω</mi>\u0000 <mo>)</mo>\u0000 </mrow></math>, where <math>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>Λ</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>(</mo>\u0000 <mi>ω</mi>\u0000 <mo>)</mo>\u0000 </mrow></math> is the <math>\u0000 <mrow>\u0000 ","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47948551","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":"System identification using autoregressive Bayesian neural networks with nonparametric noise models","authors":"Christos Merkatas,&nbsp;Simo Särkkä","doi":"10.1111/jtsa.12669","DOIUrl":"10.1111/jtsa.12669","url":null,"abstract":"<p>System identification is of special interest in science and engineering. This article is concerned with a system identification problem arising in stochastic dynamic systems, where the aim is to estimate the parameters of a system along with its unknown noise processes. In particular, we propose a Bayesian nonparametric approach for system identification in discrete time nonlinear random dynamical systems assuming only the order of the Markov process is known. The proposed method replaces the assumption of Gaussian distributed error components with a flexible family of probability density functions based on Bayesian nonparametric priors. Additionally, the functional form of the system is estimated by leveraging Bayesian neural networks, which leads to flexible uncertainty quantification. Hamiltonian Monte Carlo sampler within a Gibbs sampler for posterior inference is proposed and its effectiveness is illustrated in real time series.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12669","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49302262","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":"A nonparametric predictive regression model using partitioning estimators based on Taylor expansions","authors":"Jose Olmo","doi":"10.1111/jtsa.12668","DOIUrl":"10.1111/jtsa.12668","url":null,"abstract":"<p>This article proposes a nonparametric predictive regression model. The unknown function modeling the predictive relationship is approximated using polynomial Taylor expansions applied over disjoint intervals covering the support of the predictor variable. The model is estimated using the theory on partitioning estimators that is extended to a stationary time series setting. We show pointwise and uniform convergence of the proposed estimator and derive its asymptotic normality. These asymptotic results are applied to test for the presence of predictive ability. We develop an asymptotic pointwise test of predictive ability using the critical values of a Normal distribution, and a uniform test with asymptotic distribution that is approximated using a <i>p</i>-value transformation and Wild bootstrap methods. These theoretical insights are illustrated in an extensive simulation exercise and also in an empirical application to forecasting high-frequency based realized volatility measures. Our results provide empirical support to the presence of nonlinear autoregressive predictability of these measures for the constituents of the Dow Jones index.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41718162","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":"Tempered functional time series","authors":"Farzad Sabzikar,&nbsp;Piotr Kokoszka","doi":"10.1111/jtsa.12667","DOIUrl":"10.1111/jtsa.12667","url":null,"abstract":"<p>We propose a broad class of models for time series of curves (functions) that can be used to quantify near long-range dependence or near unit root behavior. We establish fundamental properties of these models and rates of consistency for the sample mean function and the sample covariance operator. The latter plays a role analogous to sample cross-covariances for multivariate time series, but is far more important in the functional setting because its eigenfunctions are used in principal component analysis, which is a major tool in functional data analysis. It is used for dimension reduction of feature extraction. We also establish a central limit theorem for functions following our model. Both the consistency rates and the normalizations in the Central Limit Theorem (CLT) are nonstandard. They reflect the local unit root behavior and the long memory structure at moderate lags.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43416610","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":"Volatility models for stylized facts of high-frequency financial data","authors":"Donggyu Kim,&nbsp;Minseok Shin","doi":"10.1111/jtsa.12666","DOIUrl":"10.1111/jtsa.12666","url":null,"abstract":"<p>This article introduces novel volatility diffusion models to account for the stylized facts of high-frequency financial data such as volatility clustering, intraday U-shape, and leverage effect. For example, the daily integrated volatility of the proposed volatility process has a realized GARCH structure with an asymmetric effect on log returns. To further explain the heavy-tailedness of the financial data, we assume that the log returns have a finite <math>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mi>b</mi>\u0000 </mrow></math>th moment for <math>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 <mo>∈</mo>\u0000 <mo>(</mo>\u0000 <mn>1</mn>\u0000 <mo>,</mo>\u0000 <mn>2</mn>\u0000 <mo>]</mo>\u0000 </mrow></math>. Then, we propose a Huber regression estimator that has an optimal convergence rate of <math>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mn>1</mn>\u0000 <mo>−</mo>\u0000 <mi>b</mi>\u0000 <mo>)</mo>\u0000 <mo>/</mo>\u0000 <mi>b</mi>\u0000 </mrow>\u0000 </msup>\u0000 </mrow></math>. We also discuss how to adjust bias coming from Huber loss and show its asymptotic properties.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43268282","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":"New associate editors","authors":"","doi":"10.1111/jtsa.12665","DOIUrl":"https://doi.org/10.1111/jtsa.12665","url":null,"abstract":"<p>We welcome Dr Tucker McElroy and Professor Stathis Paparoditis to the editorial board of the <i>Journal of Time Series Analysis</i>, who both join as Associate Editors with immediate effect.</p><p>Tucker McElroy is senior time series mathematical statistician at the US Census Bureau. His research interests are seasonal adjustment, signal extraction, and frequency domain methodology. He has several projects on GitHub, including the R package Ecce Signum for multivariate time series, and has published his research in <i>Annals of Statistics</i>, <i>JASA</i>, <i>Biometrika</i>, <i>JRSSB</i>, and <i>JTSA</i>, among others.</p><p>Stathis Paparoditis is Professor of Mathematical Statistics at the University of Cyprus. His research interests cover nonparametric methods for univariate, multivariate and functional time series, including bootstrap and resampling methods, tests of stationarity, goodness-of-fit tests, and prediction. He has published his research in <i>Annals of Statistics</i>, <i>JRSSB</i>, <i>JASA</i>, <i>Biometrika</i>, <i>Bernoulli</i>, <i>Econometrica</i>, <i>Econometric Theory</i>, <i>Journal of Econometrics</i>, and <i>JTSA</i>, among others.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12665","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72323085","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":"Issue Information","authors":"","doi":"10.1002/smi.3064","DOIUrl":"https://doi.org/10.1002/smi.3064","url":null,"abstract":"","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41663371","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":"Directed graphs and variable selection in large vector autoregressive models","authors":"Dominik Bertsche,&nbsp;Ralf Brüggemann,&nbsp;Christian Kascha","doi":"10.1111/jtsa.12664","DOIUrl":"10.1111/jtsa.12664","url":null,"abstract":"<p>We represent the dynamic relation among variables in vector autoregressive (VAR) models as directed graphs. Based on these graphs, we identify so-called strongly connected components. Using this graphical representation, we consider the problem of variable choice. We use the relations among the strongly connected components to select variables that need to be included in a VAR if interest is in impulse response analysis of a given set of variables. Our theoretical contributions show that the set of selected variables from the graphical method coincides with the set of variables that is multi-step causal for the variables of interest by relating the paths in the graph to the coefficients of the ‘direct’ VAR representation. An empirical application illustrates the usefulness of the suggested approach: Including the selected variables into a small US monetary VAR is useful for impulse response analysis as it avoids the well-known ‘price-puzzle’.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45500397","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}