In recognition of authors who have made significant contributions to this Journal, the Journal of Time Series Analysis runs a scheme to honor those authors by naming them as a Journal of Time Series Analysis Distinguished Author. The qualifying criterion for this award is 3.5 points where authors are awarded 1 point for each single-authored article, 1/2 point for each double-authored article, 1/3 point for each triple-authored article, and so on, that they have published in the Journal of Time Series Analysis since its inception. Distinguished Authors are entitled to a 1-year free on-line subscription to the Journal to mark the award. They also receive a certificate commemorating the award.
In addition to the lists of Distinguished Authors announced previously in Volume 41 Issue 4 (July 2020), Volume 42 Issue 1 (January 2021), Volume 43 Issue 1 (January 2022), Volume 44 Issue 1 (January 2023), Volume 45 Issue 1 (January 2024), and Volume 46 Issue 2 (March 2025), the Journal of Time Series Analysis is very pleased to welcome
Joann Jasiak
Daniel Peña
Peter C.B. Phillips
Fukang Zhu
to the list of Journal of Time Series Analysis Distinguished Authors for 2025, based on their publications in the Journal appearing up to and including Volume 46 Issue 6 (November 2025).
In addition to the list of Distinguished Authors announced in Volume 45 Issue 1 (January 2024), the Journal of Time Series Analysis is very pleased to welcome
Christian Gouriéroux
to the list of Journal of Time Series Analysis Distinguished Authors for 2023 based on his publications in the Journal appearing up to and including Volume 44, Issues 5–6 (September–November 2023).
We apologize to Christian for his omission from the original list which was due to an administrative error.
On behalf of both the editorial board and the readership of the Journal of Time Series Analysis, I would like to take this opportunity to thank Professor Marcus Chambers very much for his long and dedicated service to the Journal of Time Series Analysis. Marcus first served as an Associate Editor of the journal from January 2013 until October 2020 and then subsequently as a Co-Editor of the journal, a role which he held until 31 December 2025 when he formally stepped down.
I am delighted to welcome Robert Lund as a new Co-Editor of the Journal of Time Series Analysis, effective from 1 January 2026.
Harmonizable processes are a class of nonstationary time series, that are characterized by their dependence between different frequencies of a time series. The covariance between two frequencies is the dual frequency spectral density, an object analogous to the spectral density function. Local stationarity is another popular form of nonstationarity, though thus far, little attention has been paid to the dual frequency spectral density of a locally stationary process. The focus of this paper is on the dual frequency spectral density of local stationary time series and locally periodic stationary time series, its natural extension. We show that there are some subtle but important differences between the dual frequency spectral density of an almost periodic stationary process and a locally periodic stationary time series. Estimation of the dual frequency spectral density is typically done by smoothing the dual frequency periodogram. We study the sampling properties of this estimator under the assumption of locally periodic stationarity. In particular, we obtain a Gaussian approximation for the smoothed dual frequency periodogram over a group of frequencies, allowing for the number of frequency lags to grow with sample size. These results are used to test for correlation between different frequency bands in the time series. The variance of the smooth dual frequency periodogram is quite complex. However, by identifying which covariances are the most pertinent we propose a nonparametric method for consistently estimating the variance. This is necessary for constructing confidence intervals or testing aspects of the dual frequency spectral density. Simulations are given to illustrate our results.
Independent Component Analysis (ICA) is a popular tool used for blind source separation and has found application in fields such as financial time series, signal processing, feature extraction, and brain imaging. Inspired by modeling a macroeconomic time series that has components with heavy tails, we consider the ICA problem with an infinite variance source. Many of the ICA procedures require the existence of a finite second or even fourth moment. Distance covariance is a measure of dependence that has become an increasingly popular choice as an objective function in the ICA setting. Unfortunately, the standard weight function used in distance covariance requires a finite variance assumption when applied in the ICA framework. The objective of this paper is to derive consistency when using the distance covariance applied to the infinite variance case. Extensions to the ICA model with noise, which has a direct application to time series models when testing independence of residuals based on their estimated counterparts, are also considered.
State-space models are widely used by statisticians because they allow a useful interpretation of some components of interest. Their efficient estimation, as well as the computation of forecasts or nonlinear functions of the observables, depends crucially on the correct specification of the error terms. Gaussianity is a common assumption explicitly used in the Kalman filter recursion, but departing from Gaussianity is of particular interest in fields such as finance, where there is a need for leptokurtic and/or asymmetric distributions to capture some features of the data. We introduce an approach based on the characteristic function or Laplace transform of the observed process and show that, for a large class of state-space models (with finite second-order moments and non-zero higher-order cumulants), it is possible to recover the cumulants of the structural shocks and the measurement errors from the cumulants and cross-cumulants of the observed process and the first-order parameters. This allows the statistician to design specification tests related to the properties of the structural shocks or measurement errors, separately or jointly, or of the data generating process (DGP) of the observed time series. In a non-Gaussian framework, we design a test for the property that the DGP of the observed time series is a state-space model with different shocks versus an ARMA DGP with only a single innovation process, but with the same second-order properties. We illustrate the size and power properties of this test applied to a simple stochastic volatility model.
Block resampling methods provide useful nonparametric inference with time series by applying data blocks to capture dependence and develop distributional approximations for statistics. Among such methods, the block bootstrap (BB) and subsampling (SS) represent distinct approaches, where SS has advantages over bootstrap in more general applicability and computation, though bootstrap, when viable, can be more accurate. Recently, convolved subsampling (CS) has emerged as a hybrid method between SS and BB, though its accuracy has remained an open question, and its performance depends intricately on the choice of two tuning parameters (i.e., block length and convolution level). This paper establishes the formal accuracy properties of CS for a broad class of smooth-model statistics and time processes, showing that CS can be second-order correct like the BB, offering improvements over SS or normal approximations. However, the success of CS can depend heavily on the convolution level chosen and on how the CS approximation is centered or de-biased. Indeed, without such consideration, CS can even become invalid. In developing accuracy properties, this work also provides important guideposts for the convolution level needed to implement the CS. Numerical evidence suggests the method achieves good coverage accuracy and compares favorably with other block resampling approaches.
This work considers estimation and forecasting in a multivariate, possibly high-dimensional count time series model constructed from a transformation of a latent Gaussian dynamic factor series. The estimation of the latent model parameters is based on second-order properties of the count and underlying Gaussian time series, yielding estimators of the underlying covariance matrices for which standard principal component analysis applies. Theoretical consistency results are established for the proposed estimation, building on certain concentration results for the models of the type considered. They also involve the memory of the latent Gaussian process, quantified through a spectral gap, shown to be suitably bounded as the model dimension increases, which is of independent interest. In addition, novel cross-validation schemes are suggested for model selection. The forecasting is carried out through a particle-based sequential Monte Carlo, leveraging Kalman filtering techniques. A simulation study and an application are also considered.
I am delighted to welcome Dr Ke-Li Xu to the editorial board of the Journal of Time Series Analysis. Ke-Li joins as an Associate Editor with effect from 1st July 2025.
Ke-Li obtained his PhD from Yale University in 2007 and is currently Professor of Economics at Indiana University Bloomington, a position he has held since 2021. The main theme of his research is to design statistical estimation and inference methods for economic models that accommodate features such as endogeneity, nonlinearity, heterogeneity, and persistence, without imposing strong constraints on the underlying data generating process. Before joining Indiana University, Ke-Li held positions at Texas A&M University and at the University of Alberta, Canada. Ke-Li is a Fellow of the Journal of Econometrics and a recipient of the Multa Scripsit Award from Econometric Theory. He is currently an Associate Editor of the Journal of Business and Economic Statistics and of Econometric Reviews. He also served as a Panelist for the National Science Foundation (NSF), Economics Program.
The author declares no conflicts of interest.
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