{"title":"Analysis of Crisis Effects via Maximum Entropy Adjustment","authors":"Tucker McElroy","doi":"10.1111/jtsa.12808","DOIUrl":"https://doi.org/10.1111/jtsa.12808","url":null,"abstract":"<div>\u0000 \u0000 <p>Crises, such as the Covid-19 epidemic, can affect economic time series by distorting historical trends and seasonal patterns with extreme values. The identification and adjustment of such extremes is important for the production of seasonally adjusted data; an ongoing challenge is the identification of a crisis' end, where the time series returns to more typical pre-crisis dynamics. A secondary challenge is to model and analyze time series data with both missing values and extremes. This paper extends the maximum entropy framework to a generalized class of extreme values, including level shifts, temporary changes, and seasonal outliers. These outliers are described as a particular type of stochastic process that is latent, or unobserved, such that its removal increases the time series entropy. The proposed methods allow one to model and fit time series data using conventional tools in the presence of specified streams of extreme values, as well as missing values. Extreme value adjustment, with quantification of mean squared error, can then be obtained along with the seasonal adjustment; there is also a test statistic to directly compare two specifications of extremes. The techniques are illustrated in weekly employment data.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"106-120"},"PeriodicalIF":1.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706632","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":"A Zero Serial Cross-Correlation Test Before Fitting Heteroscedasticity","authors":"Yaolan Ma, Xiaohong Chen, Liang Peng, Rongmao Zhang","doi":"10.1111/jtsa.12807","DOIUrl":"10.1111/jtsa.12807","url":null,"abstract":"<div>\u0000 \u0000 <p>Many statistical inferences for a multivariate innovation-based copula time series model rely on the serial independence of innovation vectors after filtering the conditional means and conditional covariance. Zero (respectively nonzero) serial cross-correlations for the heteroscedastic noises before filtering the conditional covariance may suggest the use of univariate (respectively multivariate) GARCH models. This paper develops a zero serial cross-correlation test for the heteroscedastic noises with heavy tails before fitting a multivariate heteroscedasticity model. Applications to exchange rates and mutual fund returns show that this critical assumption could be problematic sometimes and conducting such a test is necessary before building and using a multivariate innovation-based copula time series model.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"364-377"},"PeriodicalIF":1.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139636","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":"A Mixture Transition Distribution Modeling for Higher-Order Circular Markov Processes","authors":"Hiroaki Ogata, Takayuki Shiohama","doi":"10.1111/jtsa.12803","DOIUrl":"https://doi.org/10.1111/jtsa.12803","url":null,"abstract":"<p>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.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"304-320"},"PeriodicalIF":1.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139475","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 Mixture Transition Distribution Modeling for Higher-Order Circular Markov Processes","authors":"Hiroaki Ogata, Takayuki Shiohama","doi":"10.1111/jtsa.12803","DOIUrl":"https://doi.org/10.1111/jtsa.12803","url":null,"abstract":"<p>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.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"304-320"},"PeriodicalIF":1.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139474","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":"CAViaR Model Selection via Adaptive Lasso","authors":"Zongwu Cai, Ying Fang, Dingshi Tian","doi":"10.1111/jtsa.12804","DOIUrl":"10.1111/jtsa.12804","url":null,"abstract":"<div>\u0000 \u0000 <p>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& P 500 return series.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"321-330"},"PeriodicalIF":1.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139473","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":"A Stochastic Tree for Bubble Asset Modelling and Pricing","authors":"Christian Gourieroux, Joann Jasiak","doi":"10.1111/jtsa.12801","DOIUrl":"https://doi.org/10.1111/jtsa.12801","url":null,"abstract":"<p>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.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"932-944"},"PeriodicalIF":1.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767372","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":"Cointegrating Polynomial Regressions With Power Law Trends","authors":"Yicong Lin, Hanno Reuvers","doi":"10.1111/jtsa.12805","DOIUrl":"10.1111/jtsa.12805","url":null,"abstract":"<p>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.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"331-344"},"PeriodicalIF":1.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12805","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135906","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":"Exact likelihood for inverse gamma stochastic volatility models","authors":"Roberto Leon-Gonzalez, Blessings Majoni","doi":"10.1111/jtsa.12795","DOIUrl":"https://doi.org/10.1111/jtsa.12795","url":null,"abstract":"<p>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.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 4","pages":"774-795"},"PeriodicalIF":1.2,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273224","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":"The Liquidity Uncertainty Premium Puzzle","authors":"Maria Flora, Ilaria Gianstefani, Roberto Renò","doi":"10.1111/jtsa.12802","DOIUrl":"https://doi.org/10.1111/jtsa.12802","url":null,"abstract":"<p>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.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 2","pages":"286-299"},"PeriodicalIF":1.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143244373","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":"Poisson count time series","authors":"Jiajie Kong, Robert Lund","doi":"10.1111/jtsa.12799","DOIUrl":"10.1111/jtsa.12799","url":null,"abstract":"<p>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.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"279-303"},"PeriodicalIF":1.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140180","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}