发布求助
文献互助 智能选刊 最新文献

Journal of Time Series Analysis最新文献

筛选
英文 中文
A new portmanteau test for predictive regression models with possible embedded endogeneity 针对可能存在嵌入内生性的预测回归模型的新波特曼检验
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-12 DOI: 10.1111/jtsa.12745
Yao Rao, Yawen Fan, Huimin Ao, Xiaohui Liu
{"title":"A new portmanteau test for predictive regression models with possible embedded endogeneity","authors":"Yao Rao,&nbsp;Yawen Fan,&nbsp;Huimin Ao,&nbsp;Xiaohui Liu","doi":"10.1111/jtsa.12745","DOIUrl":"10.1111/jtsa.12745","url":null,"abstract":"<p>In the widely used predictive regression model, any possible serial correlation in innovations leads to estimation bias and statistical inference distortions. Hence, it is important to pretest the existence of such serial correlation. Nevertheless, in the presence of embedded endogeneity, which is a common problem in the predictive regression setting, traditional serial correlation tests such as Box–Pierce (BP) and Ljung–Box (LB) tests are found to perform poorly. Motivated by this, we develop a new portmanteau test in this article as a pretest for serial correlation in predictive regression under possible embedded endogeneity. This test is based on the sample splitting idea and the jackknife empirical likelihood method. The asymptotic distribution of the proposed test has been derived, and the Monte Carlo simulations confirm good finite sample performances. As an illustration, we apply our proposed test in pretesting the serial correlation in predictive regression, where financial variables are used to predict the excess return of S&amp;P 500.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 6","pages":"953-979"},"PeriodicalIF":1.2,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933103","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
Inference for calendar effects in microstructure noise 微观结构噪声中的日历效应推断
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-05 DOI: 10.1111/jtsa.12744
Yingwen Tan, Zhiyuan Zhang
{"title":"Inference for calendar effects in microstructure noise","authors":"Yingwen Tan,&nbsp;Zhiyuan Zhang","doi":"10.1111/jtsa.12744","DOIUrl":"10.1111/jtsa.12744","url":null,"abstract":"<p>We develop a statistical inference procedure for the ubiquitous calendar effects in microstructure noise using high frequency data. This is, to the best of our knowledge, the first inference theory ever built for <i>noise calendar effect</i> under the general semi-martingale-plus-noise setup for prices contaminated with non-stationary, endogenous, and serially dependent microstructure noise. We devise a noise-calendar-effect estimator by an appropriately scaled average of high-frequency returns that precede a time of day across a large number of trading days. Feasible central limit theorem for the estimator is established under a joint infill and long-span asymptotics. Monte Carlo simulations corroborate our theoretical findings. An empirical study on the high-frequency data of the e-mini S&amp;P 500 futures and a Chinese stock demonstrates that the noise calendar effect has undergone significant changes over time for the latter, yet remains stable for the former.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 6","pages":"931-952"},"PeriodicalIF":1.2,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140887034","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
Spectral Density Estimation for a Class of Spectrally Correlated Processes 一类频谱相关过程的频谱密度估计
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-26 DOI: 10.1111/jtsa.12742
Anna E. Dudek, Bartosz Majewski, Antonio Napolitano
{"title":"Spectral Density Estimation for a Class of Spectrally Correlated Processes","authors":"Anna E. Dudek,&nbsp;Bartosz Majewski,&nbsp;Antonio Napolitano","doi":"10.1111/jtsa.12742","DOIUrl":"10.1111/jtsa.12742","url":null,"abstract":"<p>We study the estimation problem of the spectral density function for harmonizable non-stationary processes. More precisely, we consider spectrally correlated processes whose spectral measure has the support contained in the union of unknown lines with possibly non-unit slopes. We propose the frequency-smoothed periodogram along the estimated support line as an estimator of the spectral density function. We show the mean-square consistency of the proposed estimator. Additionally, we discuss the estimation of the support line in a specific model with its applications in locating a moving source. Finally, we present simulations confirming the proven results.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 6","pages":"884-909"},"PeriodicalIF":1.2,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806114","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
Quasi-Likelihood Estimation in Volatility Models for Semi-Continuous Time Series 半连续时间序列波动模型中的准概率估计
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-17 DOI: 10.1111/jtsa.12741
Šárka Hudecová, Michal Pešta
{"title":"Quasi-Likelihood Estimation in Volatility Models for Semi-Continuous Time Series","authors":"Šárka Hudecová,&nbsp;Michal Pešta","doi":"10.1111/jtsa.12741","DOIUrl":"10.1111/jtsa.12741","url":null,"abstract":"<p>Time series containing non-negligible portion of possibly dependent zeros, whereas the remaining observations are positive, are considered. They are regarded as GARCH processes consisting of non-negative values. Our first aim lies in estimation of the omnibus model parameters taking into account the semi-continuous distribution. The hurdle distribution together with dependent zeros cause that the classical GARCH estimation techniques fail. Two different quasi-likelihood approaches are employed. Both estimators are proved to be strongly consistent and asymptotically normal. The second goal consists in the proposed predictions with bootstrap add-ons. The considered class of models can be reformulated as multiplicative error models. The empirical properties are illustrated in a simulation study, which demonstrates computational efficiency of the employed methods. The developed techniques are presented through an actuarial problem concerning insurance claims.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 6","pages":"859-883"},"PeriodicalIF":1.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140610649","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
Inference in Coarsened Time Series via Generalized Method of Moments 通过广义矩法推断粗化时间序列
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-10 DOI: 10.1111/jtsa.12740
Man Fai Ip, Kin Wai Chan
{"title":"Inference in Coarsened Time Series via Generalized Method of Moments","authors":"Man Fai Ip,&nbsp;Kin Wai Chan","doi":"10.1111/jtsa.12740","DOIUrl":"10.1111/jtsa.12740","url":null,"abstract":"<p>We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi-variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long-run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real-data application on air quality in the USA is investigated.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 5","pages":"823-846"},"PeriodicalIF":1.2,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603304","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
Bootstrap prediction inference of nonlinear autoregressive models 非线性自回归模型的引导预测推断
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-01 DOI: 10.1111/jtsa.12739
Kejin Wu, Dimitris N. Politis
{"title":"Bootstrap prediction inference of nonlinear autoregressive models","authors":"Kejin Wu,&nbsp;Dimitris N. Politis","doi":"10.1111/jtsa.12739","DOIUrl":"10.1111/jtsa.12739","url":null,"abstract":"<p>The nonlinear autoregressive (NLAR) model plays an important role in modeling and predicting time series. One-step ahead prediction is straightforward using the NLAR model, but the multi-step ahead prediction is cumbersome. For instance, iterating the one-step ahead predictor is a convenient strategy for linear autoregressive (LAR) models, but it is suboptimal under NLAR. In this article, we first propose a simulation and/or bootstrap algorithm to construct optimal point predictors under an <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow></math> or <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow></math> loss criterion. In addition, we construct bootstrap prediction intervals in the multi-step ahead prediction problem; in particular, we develop an asymptotically valid quantile prediction interval as well as a pertinent prediction interval for future values. To correct the undercoverage of prediction intervals with finite samples, we further employ predictive – as opposed to fitted – residuals in the bootstrap process. Simulation and empirical studies are also given to substantiate the finite sample performance of our methods.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 5","pages":"800-822"},"PeriodicalIF":1.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564371","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 Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients 测试具有异质性空间系数和回归系数的空间动态面板数据模型
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-02-28 DOI: 10.1111/jtsa.12738
Francesco Giordano, Marcella Niglio, Maria Lucia Parrella
{"title":"Testing Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients","authors":"Francesco Giordano,&nbsp;Marcella Niglio,&nbsp;Maria Lucia Parrella","doi":"10.1111/jtsa.12738","DOIUrl":"10.1111/jtsa.12738","url":null,"abstract":"<p>Spatio-temporal data are often analysed by means of <i>spatial dynamic panel data (SDPD) models</i>. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an <i>SDPD</i> model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure of the <i>SDPD</i> model through a multiple testing procedure that allows to choose between a general version of the model and a nested version derived from the general one by imposing restrictions on the parameters. Our proposal can be used to test the homogeneity of the model parameters as well as the absence of specific components, such as spatial effects, dynamic effects or exogenous regressors. It is also possible to use the proposed testing procedure for the identification of relevant locations. The theoretical results highlight the consistency of the testing procedure in the high-dimensional setup, when the number of spatial units goes to infinity and exceeds the number of time-observations per spatial unit. Further, we also conduct a Monte Carlo simulation study, which gives empirical evidence of the good performance of the testing procedure in finite samples.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 5","pages":"771-799"},"PeriodicalIF":1.2,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140007683","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
On distributional autoregression and iterated transportation 关于分布自回归和迭代运输
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-02-21 DOI: 10.1111/jtsa.12736
Laya Ghodrati, Victor M. Panaretos
{"title":"On distributional autoregression and iterated transportation","authors":"Laya Ghodrati,&nbsp;Victor M. Panaretos","doi":"10.1111/jtsa.12736","DOIUrl":"10.1111/jtsa.12736","url":null,"abstract":"<p>We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of <span></span><math>\u0000 <mrow>\u0000 <mi>ℝ</mi>\u0000 </mrow></math>. An order-1 autoregressive model in this context is to be understood as a Markov chain, where one specifies a certain structure (regression) for the one-step conditional Fréchet mean with respect to a natural probability metric. We construct and explore different models based on iterated random function systems of optimal transport maps. While the properties and interpretation of these models depend on how they relate to the iterated transport system, they can all be analyzed theoretically in a unified way. We present such a theoretical analysis, including convergence rates, and illustrate our methodology using real and simulated data. Our approach generalizes or extends certain existing models of transportation-based regression and autoregression, and in doing so also provides some additional insights on existing models.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 5","pages":"739-770"},"PeriodicalIF":1.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12736","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946093","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
Ridge regularized estimation of VAR models for inference 用于推理的 VAR 模型的岭正则化估计
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-02-18 DOI: 10.1111/jtsa.12737
Giovanni Ballarin
{"title":"Ridge regularized estimation of VAR models for inference","authors":"Giovanni Ballarin","doi":"10.1111/jtsa.12737","DOIUrl":"10.1111/jtsa.12737","url":null,"abstract":"<p>Ridge regression is a popular method for dense least squares regularization. In this article, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed, and a comparison is made with Bayesian ridge-type estimators. The asymptotic distribution and the properties of cross-validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 2","pages":"235-257"},"PeriodicalIF":1.2,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926586","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
Consistency of averaged impulse response estimators in vector autoregressive models 向量自回归模型中平均脉冲响应估计器的一致性
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-02-13 DOI: 10.1111/jtsa.12733
Jan Lohmeyer, Franz Palm, Jean-Pierre Urbain
{"title":"Consistency of averaged impulse response estimators in vector autoregressive models","authors":"Jan Lohmeyer,&nbsp;Franz Palm,&nbsp;Jean-Pierre Urbain","doi":"10.1111/jtsa.12733","DOIUrl":"10.1111/jtsa.12733","url":null,"abstract":"<p>We show root-T consistency of the smoothed AIC and smoothed BIC model averaging estimators (sAIC, sBIC) of impulse response coefficients in stationary vector autoregressive models of finite lag order. We also show that there is not one unique way to define the sAIC and sBIC estimators, but that instead there is a whole class of each of these defined by a weight scaling factor that allows the averaging estimator to become more similar to either its model selection counterpart or the equal weights averaging estimator. We also show asymptotic validity of a bootstrap method for estimating the averaging estimators' distributions. Simulations illustrate the benefits of using sAIC instead of AIC estimators.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"45 5","pages":"691-713"},"PeriodicalIF":1.2,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781615","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信