在过去的 30 年中，时间序列分析已成为数理统计中最重要和最广泛使用的分支之一。其应用领域从神经生理学到天体物理学，涵盖经济预测、生物数据研究、控制系统、信号处理与通信和振动工程等知名领域。《时间序列分析杂志》创刊于 1980 年，现已成为该领域的领先期刊，发表关于基础理论和应用的论文，以及涉及该主题主要领域最新进展的评论论文和关于理论发展的简短交流。编委由许多世界领先的时间序列分析专家组成。

Over the past 30 years, time series analysis has become one of the most important and widely used branches of mathematical statistics. Its applications span various fields, from neurophysiology to astrophysics, including economic forecasting, biological data research, control systems, signal processing and communication, and vibration engineering. The Journal of Time Series Analysis was established in 1980 and has since become a leading journal in this field, publishing research papers on fundamental theories and applications, as well as review articles on the latest developments in major areas related to the topic and brief communications on theoretical advancements. The editorial board consists of many of the world's leading experts in time series analysis.

在过去的30年中，时间序列分析已成为数理统计中最重要和最广泛使用的分支之一。它的应用领域涵盖了从神经生理学到天体物理学的多个领域，包括经济预测、生物数据研究、控制系统、信号处理与通信以及振动工程等。《Journal of Time Series Analysis》创刊于1980年，现已成为该领域的领先期刊，发表关于基础理论和应用的研究论文，以及涉及该主题主要领域最新进展的评论论文和关于理论发展的简短交流。期刊的编委会由许多世界领先的时间序列分析专家组成。

Journal of Time Series Analysis

This article considers a structural‐factor approach to modeling high‐dimensional time series and space‐time data by decomposing individual series into trend, seasonal, and irregular components. For ease in analyzing many time series, we employ a time polynomial for the trend, a linear combination of trigonometric series for the seasonal component, and a new factor model for the irregular components. The new factor model simplifies the modeling process and achieves parsimony in parameterization. We propose a Bayesian information criterion to consistently select the order of the polynomial trend and the number of trigonometric functions, and use a test statistic to determine the number of common factors. The convergence rates for the estimators of the trend and seasonal components and the limiting distribution of the test statistic are established under the setting that the number of time series tends to infinity with the sample size, but at a slower rate. We study the finite‐sample performance of the proposed analysis via simulation, and analyze two real examples. The first example considers modeling weekly PM2.5data of 15 monitoring stations in the southern region of Taiwan and the second example consists of monthly value‐weighted returns of 12 industrial portfolios.

Zhaoxing Gao

Ruey S. Tsay

Journal Time Series Analysis

A Structural‐Factor Approach to Modeling High‐Dimensional Time Series and Space‐Time Data

高维时间序列与时空数据建模的结构因子方法

ABSTRACT The estimation and model selection of the conditional autoregressive value at risk (CAViaR) model may be computationally intensive and even impractical when the true order of the quantile autoregressive components or the dimension of the other regressors are high. On the other hand, conventional automatic variable selection methods cannot be directly applied to this problem because the quantile lag components are latent. In this paper, we propose a two‐step approach to select the optimal CAViaR model. The estimation procedure consists of an approximation of the conditional quantile in the first step, followed by an adaptive Lasso penalized quantile regression of the regressors as well as the estimated quantile lag components in the second step. We show that under some regularity conditions, the proposed adaptive Lasso penalized quantile estimators enjoy the oracle properties. Finally, the proposed method is illustrated by a Monte Carlo simulation study and applied to analyzing the daily data of the S&amp; P 500 return series.

Zongwu Cai

Ying Fang

Dingshi Tian

CAViaR Model Selection via Adaptive Lasso

通过自适应Lasso选择CAViaR模型

ABSTRACT This study considers the stationary higher‐order Markov process for circular data by employing the mixture transition distribution modeling. The underlying circular transition distribution is based on Wehrly and Johnson's bivariate joint circular models. The structures of the circular autocorrelation function, together with the circular partial autocorrelation function, are investigated. They are found to be similar to those of the autocorrelation and partial autocorrelation functions of the real‐valued autoregressive process when the underlying binding density has zero sine moments. The validity of the model is assessed by applying it to some Monte Carlo simulations and real directional data.

Hiroaki Ogata

Takayuki Shiohama

A Mixture Transition Distribution Modeling for Higher‐Order Circular Markov Processes

高阶循环马尔可夫过程的混合转移分布建模

This article reviews and compares popular methods, some old and some recent, that produce time series having Poisson marginal distributions. The article begins by narrating ways where time series with Poisson marginal distributions can be produced. Modeling nonstationary series with covariates motivates consideration of methods where the Poisson parameter depends on time. Here, estimation methods are developed for some of the more flexible methods. The results are used in the analysis of (1) a count sequence of tropical cyclones occurring in the North Atlantic Basin since 1970, and (2) the number of no‐hitter games pitched in Major League Baseball since 1893. Tests for whether the Poisson marginal distribution is appropriate are included.

Jiajie Kong

Robert Lund

Poisson count time series

泊松计数时间序列

ABSTRACT The puzzling negative relation between liquidity uncertainty and asset returns, originally put forward by Chordia, Subrahmanyam, and Anshuman (2001) and confirmed by the subsequent empirical literature up to date, is neither robust to the aggregation period, nor to the observation frequency used to compute the volatility of trading volume. We demonstrate that their procedure involves an estimation bias due to the persistence and skewness of volumes. When using an alternative approach based on high‐frequency data to measure liquidity uncertainty, the relationship turns out to be positive. However, portfolio strategies based on liquidity uncertainty do not appear to be profitable.

Maria Flora

Ilaria Gianstefani

Roberto Renò

The Liquidity Uncertainty Premium Puzzle

流动性不确定性溢价之谜

ABSTRACT We introduce a new stochastic tree representation of a strictly stationary submartingale process for modelling, forecasting, and pricing speculative bubbles on commodity and cryptocurrency markets. The model is compared to other trees proposed in the literature on bubble asset modelling and stochastic volatility approximation. We show that the proposed model is an extension of the well‐known Blanchard‐Watson bubble. The model provides (quasi) closed‐form pricing formulas for European options, which are derived and illustrated.

Christian Gourieroux

Joann Jasiak

A Stochastic Tree for Bubble Asset Modelling and Pricing

泡沫资产建模与定价的随机树方法

ABSTRACT The common practice in cointegrating polynomial regressions (CPRs) often confines nonlinearities in the variable of interest to stochastic trends, thereby overlooking the possibility that they may be caused by deterministic components. As an extension, we propose univariate and multivariate CPRs that incorporate power law deterministic trends. Conventional fully modified estimation is demonstrated to be inadequate for valid asymptotic inference. As a solution, we employ simulation‐based methods. Building on this concept, we also introduce a simulation‐based procedure to combine subsampling KPSS tests. This approach significantly improves empirical power compared to the existing Bonferroni procedure. Applying our framework to the environmental Kuznets curve, we find reduced evidence that recent environmental improvement can be attributed solely to economic growth.

Yicong Lin

Hanno Reuvers

Cointegrating Polynomial Regressions With Power Law Trends

具有幂律趋势的协整多项式回归

We obtain a novel analytic expression of the likelihood for a stationary inverse gamma stochastic volatility (SV) model. This allows us to obtain the maximum likelihood estimator for this nonlinear non‐Gaussian state space model. Further, we obtain both the filtering and smoothing distributions for the inverse volatilities as mixtures of gammas, and therefore, we can provide the smoothed estimates of the volatility. We show that by integrating out the volatilities the model that we obtain has the resemblance of a GARCH in the sense that the formulas are similar, which simplifies computations significantly. The model allows for fat tails in the observed data. We provide empirical applications using exchange rates data for seven currencies and quarterly inflation data for four countries. We find that the empirical fit of our proposed model is overall better than alternative models for four countries currency data and for two countries inflation data.

Abbreviation	J. Time Ser. Anal.
Journal Impact	1.23
Quartiles(Global)	STATISTICS & PROBABILITY(Q2)

ISSN	0143-9782, 1467-9892
h-index	61

Publication Information	Publisher: Wiley-Blackwell，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1980，Proportion of original research papers: 100.00%，Self Citation Rate:8.30%， Gold OA Rate: 26.50%
Average review cycle	网友分享经验：较慢,6-12周
Average recruitment ratio	网友分享经验：容易

Journal of Time Series Analysis

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A Structural‐Factor Approach to Modeling High‐Dimensional Time Series and Space‐Time Data

CAViaR Model Selection via Adaptive Lasso

A Mixture Transition Distribution Modeling for Higher‐Order Circular Markov Processes

Poisson count time series

The Liquidity Uncertainty Premium Puzzle

CAViaR Model Selection via Adaptive Lasso

A Mixture Transition Distribution Modeling for Higher‐Order Circular Markov Processes

A Stochastic Tree for Bubble Asset Modelling and Pricing

Cointegrating Polynomial Regressions With Power Law Trends

Exact likelihood for inverse gamma stochastic volatility models