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Testing Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients J. Time Ser. Anal. (IF 0.9) Pub Date : 2024-02-29 Francesco Giordano, Marcella Niglio, Maria Lucia Parrella
Spatio‐temporal data are often analysed by means of spatial dynamic panel data (SDPD) models. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an SDPD model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure
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On distributional autoregression and iterated transportation J. Time Ser. Anal. (IF 0.9) Pub Date : 2024-02-21 Laya Ghodrati, Victor M. Panaretos
We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of ℝ. 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
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Ridge regularized estimation of VAR models for inference J. Time Ser. Anal. (IF 0.9) Pub Date : 2024-02-19 Giovanni Ballarin
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
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Statistical analysis of irregularly spaced spatial data in frequency domain J. Time Ser. Anal. (IF 0.9) Pub Date : 2024-02-11 Shibin Zhang
Central limit theorems (CLTs) for frequency-domain statistics are fundamental tools in frequency-domain analysis. However, for irregularly spaced data, they are still limited. In both the pure increasing domain and the mixed increasing domain asymptotic frameworks, three CLTs of frequency-domain statistics are established for the observations at uniformly distributed sampling locations over a rectangular
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A residual-based nonparametric variance ratio no-cointegration test J. Time Ser. Anal. (IF 0.9) Pub Date : 2024-02-01 Karsten Reichold
It is prominently stated in the literature that local asymptotic power properties serve as a useful indicator for the performance of residual-based no-cointegration tests in finite samples. However, this article comes to an opposing conclusion. In particular, we show that Breitung's (2002, Journal of Econometrics 108, 343–363) nonparameteric variance ratio unit root test applied to regression residuals
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A note on the embeddability conditions in the case of integrated carma (2, 1) stochastic process with single and double zero roots J. Time Ser. Anal. (IF 0.9) Pub Date : 2024-01-08 Vladimir Andric, Sanja Nenadovic
We derive embeddability conditions for the integrated CARMA (2, 1) stochastic process with single and double zero roots in the case of stock variables.
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Time Series Quantile Regression Using Random Forests J. Time Ser. Anal. (IF 0.9) Pub Date : 2024-01-02 Hiroshi Shiraishi, Tomoshige Nakamura, Ryotato Shibuki
We discuss an application of Generalized Random Forests (GRF) proposed to quantile regression for time series data. We extended the theoretical results of the GRF consistency for i.i.d. data to time series data. In particular, in the main theorem, based only on the general assumptions for time series data and trees, we show that the tsQRF (time series Quantile Regression Forest) estimator is consistent
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Count network autoregression J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-12-19 Mirko Armillotta, Konstantinos Fokianos
We consider network autoregressive models for count data with a non-random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi-likelihood inference provides consistent and asymptotically normally distributed
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Statistical inference for GQARCH-Itô-jumps model based on the realized range volatility J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-12-19 Jin Yu Fu, Jin Guan Lin, Guangying Liu, Hong Xia Hao
This article introduces a novel approach that unifies two types of models: one is the continuous-time jump-diffusion used to model high-frequency market financial data, and the other is discrete-time GQARCH for modeling low-frequency financial data by embedding the discrete GQARCH structure with jumps in the instantaneous volatility process. This model is named GQARCH-Itô-Jumps model. Quasi-likelihood
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High-Frequency-Based Volatility Model with Network Structure J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-12-03 Huiling Yuan, Kexin Lu, Guodong Li, Junhui Wang
This paper introduces a novel multi-variate volatility model that can accommodate appropriately defined network structures based on low-frequency and high-frequency data. The model offers substantial reductions in the number of unknown parameters and computational complexity. The model formulation, along with iterative multi-step-ahead forecasting and targeting parameterization are discussed. Quasi-likelihood
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Asymptotic Normality of Bias Reduction Estimation for Jump Intensity Function in Financial Markets J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-11-14 Yuping Song, Min Zhu, Jiawei Qiu
Continuous-time diffusion models with jumps, especially the jump intensity coefficient, can depict the impact of sudden and large shocks to financial markets. It is possible to disentangle, from the discrete observations, the contributions given by the jumps and those by the diffusion part through threshold functions. Based on this threshold technique, we employ non-parametric local linear threshold
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Non-crossing quantile double-autoregression for the analysis of streaming time series data J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-10-11 Rong Jiang, Siu Kai Choy, Keming Yu
Many financial time series not only have varying structures at different quantile levels and exhibit the phenomenon of conditional heteroscedasticity at the same time but also arrive in the stream. Quantile double-autoregression is very useful for time series analysis but faces challenges with model fitting of streaming data sets when estimating other quantiles in subsequent batches. This article proposes
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Test of change point versus long-range dependence in functional time series J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-09-20 Changryong Baek, Piotr Kokoszka, Xiangdong Meng
In the context of functional time series, we propose a significance test to distinguish between short memory with a change point and long range dependence. The test is based on coefficients of projections onto an optimal direction that captures the dependence structure of the latent stationary functions that are not observable due to a potential change point. The optimal direction must be estimated
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Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-09-18 Haeran Cho, Piotr Fryzlewicz
We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion-based model selection strategy termed gappy Schwarz algorithm. The former is well-suited to separating shifts in the mean from fluctuations due to
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Editorial announcement: Journal of Time Series Analysis Distinguished Authors 2023 J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-09-17 Robert Taylor
In recognition of authors who have made significant contributions to this Journal, the Journal of Time Series Analysis runs a scheme to honour 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, ½ point for each double-authored article, 1/3 point
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Smooth transition moving average models: Estimation, testing, and computation J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-09-07 Xinyu Zhang, Dong Li
The article introduces a new subclass of nonlinear moving average model, called the smooth transition moving average (STMA) model, and studies its probabilistic properties. It is shown that, under some mild conditions, the least squares estimation (LSE) is strongly consistent and asymptotically normal. A powerful score-based goodness-of-fit test for the STMA model is presented. A different parametrization
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Local Whittle estimation with (quasi-)analytic wavelets J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-09-04 Sophie Achard, Irène Gannaz
In the general setting of long-memory multivariate time series, the long-memory characteristics are defined by two components. The long-memory parameters describe the autocorrelation of each time series. And the long-run covariance measures the coupling between time series, with general phase parameters. It is of interest to estimate the long-memory, long-run covariance and general phase parameters
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Granger causality tests based on reduced variable information J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-09-03 Neng-Fang Tseng, Ying-Chao Hung, Junji Nakano
Granger causality is a classical and important technique for measuring predictability from one group of time series to another by incorporating information of the variables described by a full vector autoregressive (VAR) process. However, in some applications economic forecasts need to be made based on information provided merely by a portion of variates (e.g., removal of a listed stock due to halting
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Stationary Jackknife J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-08-25 Weilian Zhou, Soumendra Lahiri
Variance estimation is an important aspect in statistical inference, especially in the dependent data situations. Resampling methods are ideal for solving this problem since these do not require restrictive distributional assumptions. In this paper, we develop a novel resampling method in the Jackknife family called the stationary jackknife. It can be used to estimate the variance of a statistic in
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Additive autoregressive models for matrix valued time series J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-08-25 Hong-Fan Zhang
In this article, we develop additive autoregressive models (Add-ARM) for the time series data with matrix valued predictors. The proposed models assume separable row, column and lag effects of the matrix variables, attaining stronger interpretability when compared with existing bilinear matrix autoregressive models. We utilize the Gershgorin's circle theorem to impose some certain conditions on the
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Wasserstein distance bounds on the normal approximation of empirical autocovariances and cross-covariances under non-stationarity and stationarity J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-08-18 Andreas Anastasiou, Tobias Kley
The autocovariance and cross-covariance functions naturally appear in many time series procedures (e.g. autoregression or prediction). Under assumptions, empirical versions of the autocovariance and cross-covariance are asymptotically normal with covariance structure depending on the second- and fourth-order spectra. Under non-restrictive assumptions, we derive a bound for the Wasserstein distance
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On vector linear double autoregression J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-08-15 Yuchang Lin, Qianqian Zhu
This article proposes a vector linear double autoregressive (VLDAR) model with the constant conditional correlation specification, which can capture the co-movement of multiple series and jointly model their conditional means and volatilities. The strict stationarity of the new model is discussed, and a self-weighted Gaussian quasi-maximum likelihood estimator (SQMLE) is proposed for estimation. To
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Margin-closed vector autoregressive time series models J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-08-09 Lin Zhang, Harry Joe, Natalia Nolde
Conditions are obtained for a Gaussian vector autoregressive time series of order k, VAR(k), to have univariate margins that are autoregressive of order k or lower-dimensional margins that are also VAR(k). This can lead to d-dimensional VAR(k) models that are closed with respect to a given partition {S1,…,Sn} of {1,…,d} by specifying marginal serial dependence and some cross-sectional dependence parameters
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Goodness-of-fit tests for the multivariate Student-t distribution based on i.i.d. data, and for GARCH observations J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-08-03 Simos Meintanis, Bojana Milošević, Marko Obradović, Mirjana Veljović
We consider goodness-of-fit tests for the multivariate Student's t-distribution with i.i.d. data and for the innovation distribution in a generalized autoregressive conditional heteroskedasticity model. The methods are based on the empirical characteristic function and are relatively easy to implement, invariant under linear transformations, and globally consistent. Asymptotic properties of the proposed
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Editorial Announcement J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-07-30 Robert Taylor
On behalf of the editorial board of the Journal of Time Series Analysis, I am delighted to welcome Professors Liudas Giraitis (Queen Mary University of London), Robert Lund (University of California, Santa Cruz), and Neil Shephard (Harvard University) as Associate Editors of the journal, each with immediate effect. I would also like to thank Professors Konstantinos Fokianos (University of Cyprus) and
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Adjustment coefficients and exact rational expectations in cointegrated vector autoregressive models J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-07-24 Søren Johansen, Anders Rygh Swensen
In this article, we consider the cointegrated vector autoregressive model with adjustment parameters α $$ \alpha $$ and cointegration vectors β $$ \beta $$ . We discuss estimation of the model under the exact linear rational expectations, when we also have linear restrictions on the adjustment parameters α $$ \alpha $$ . In particular we consider the same restriction on all vectors in α $$ \alpha $$
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Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-07-20 Shan Dai, Ngai Hang Chan
Cointegration has been widely used in macroeconomics and financial time series analysis, but traditional linear cointegration relationship is often rejected in empirical applications. Many constant parameters testing methods in semi-parametric functional coefficient cointegrated framework have been developed accordingly. However, there are little studies on constant parameters testing problem for the
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Special Issue of the Journal of Time Series Analysis in Honor of Professor Masanobu Taniguchi J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-07-12 Marc Hallin, Yoshihide Kakizawa, Hira Koul
Taniguchi Sensei – our colleague and friend Masanobu Taniguchi – retired from Waseda University in Tokyo at the end of March 2022 after a long and productive career that put Waseda on the international map of time series analysis and mathematical statistics. Masanobu arrived at Waseda from Osaka some 20 years ago and rapidly developed a powerful team of students (in total 19 theses defended) and researchers
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Functional principal component analysis for cointegrated functional time series J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-06-23 Won-Ki Seo
Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool. Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also
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A testing approach to clustering scalar time series J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-06-17 Daniel Peña, Ruey S. Tsay
This article considers clustering stationary scalar time series using their marginal properties and a hierarchical method. Two major issues involved are to detect the existence of clusters and to determine their number. We propose a new test statistic for detecting whether a data set consists of multiple clusters and a new procedure to determine the number of clusters. The proposed method is based
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Weighted l1-Penalized Corrected Quantile Regression for High-Dimensional Temporally Dependent Measurement Errors J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-06-15 Monika Bhattacharjee, Nilanjan Chakraborty, Hira L. Koul
This article derives some large sample properties of weighted l 1 -penalized corrected quantile estimators of the regression parameter vector in a high-dimensional errors in variables (EIVs) linear regression model. In this model, the number of predictors p depends on the sample size n and tends to infinity, generally at a faster rate than n , as n tends to infinity. Moreover, the measurement errors
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Estimation on unevenly spaced time series J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-06-15 Liudas Giraitis, Fulvia Marotta
In many different fields realizations of stationary time series might be recorded at irregular points in time, resulting in observed unevenly spaced samples. These missing observations can happen for several reasons, depending on the mechanisms that record the data or external conditions that force the missing observations. In this article, we first focus on the question if we can estimate the mean
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Testing for symmetric correlation matrices with applications to factor models J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-06-14 Nan-Jung Hsu, Lai Heng Sim, Ruey S. Tsay
Factor models have been widely used in recent years to model high-dimensional spatio-temporal data. However, the validity of employing factor models in a specific application has received less attention. This article proposes test statistics for testing the symmetry in cross-correlation matrices of a high-dimensional stochastic process implied by exact factor models. A rejection of symmetry indicates
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Nonlinear kernel mode-based regression for dependent data J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-05-30 Tao Wang
Under stationary α-mixing dependent samples, we in this article develop a novel nonlinear regression based on mode value for time series sequences to achieve robustness without sacrificing estimation efficiency. The estimation process is built on a kernel-based objective function with a constant bandwidth (tuning parameter) that is independent of sample size and can be adjusted to maximize efficiency
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Correcting the bias of the sample cross-covariance estimator J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-05-29 Yifan Li
We derive the finite sample bias of the sample cross-covariance estimator based on a stationary vector-valued time series with an unknown mean. This result leads to a bias-corrected estimator of cross-covariances constructed from linear combinations of sample cross-covariances, which can in theory correct for the bias introduced by the first h lags of cross-covariance with any h not larger than the
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Corrigendum: Error bounds and asymptotic expansions for Toeplitz product functionals of unbounded spectra J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-05-08 Tetsuya Takabatake
We investigate error orders for integral limit approximations to traces of products of Toeplitz matrices generated by integrable functions on [ − π , π ] having some singularities at the origin. Even though a sharp error order of the above approximation is derived in Theorem 2 of Lieberman and Phillips (2004, Journal of Time Series Analysis, 25(5) 733–753), its proof contains an inaccuracy. In the
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Stochastic local and moderate departures from a unit root and its application to unit root testing J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-05-05 Mikihito Nishi, Eiji Kurozumi
Local-to-unity and moderate-deviations specifications have been popular alternatives to unit root modeling. This article considers another kind of departures from a unit root, of the form c v t / T β , where v t is random and β determines the distance from a unit root. We classify the stochastic departures into two types: local and moderate. This classification task is completed by investigating the
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Portmanteau tests for periodic ARMA models with dependent errors J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-05-03 Y. Boubacar Maïnassara, A. Ilmi Amir
In this article, we derive the asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations of (parsimonious) periodic autoregressive moving-average (PARMA) models under the assumption that the errors are uncorrelated but not necessarily independent. We then deduce the modified portmanteau statistics. We establish the asymptotic behavior of the proposed
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Editorial announcement J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-27 Robert Taylor
I am delighted to welcome Sam Astill, James Duffy and Liudas Giraitis to the editorial board of the Journal of Time Series Analysis. All three join as Associate Editors with effect from 1 March 2023. At the same time, I would like to thank Professor Konstantinos Fokianos, who steps down as an Associate Editor with effect from 1 March 2023, for his work for the journal in this capacity since 2013. Sam
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A new estimator for LARCH processes J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-27 Jean-Marc Bardet
The aim of this article is to provide a new estimator of parameters for LARCH ( ∞ ) processes, and thus also for LARCH ( p ) or GLARCH ( p , q ) processes. This estimator results from minimizing a contrast leading to a least squares estimator for the absolute values of the process. Strong consistency and asymptotic normality are shown, and convergence occurs at the rate n as well in short or long memory
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Clustering multivariate time series using energy distance J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-27 Richard A. Davis, Leon Fernandes, Konstantinos Fokianos
A novel methodology is proposed for clustering multivariate time series data using energy distance defined in Székely and Rizzo (2013). Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure the separation between the finite-dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is calculated, a hierarchical clustering
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Inference for high-dimensional linear models with locally stationary error processes J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-21 Jiaqi Xia, Yu Chen, Xiao Guo
Linear regression models with stationary errors are well studied but the non-stationary assumption is more realistic in practice. An estimation and inference procedure for high-dimensional linear regression models with locally stationary error processes is developed. Combined with a proper estimator for the autocovariance matrix of the non-stationary error, the desparsified lasso estimator is adopted
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A first order continuous time VAR with random coefficients J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-16 Milena Hoyos
This article considers a first order continuous time vector autoregression with random coefficients. We discuss some difficulties that arise when the exact discrete analogue is used for estimating the continuous time parameters and provide an estimation method based on an approximate discrete model. Some expressions for the estimator of the drift parameter matrix, for its approximated bias and for
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Optimal estimating function for weak location-scale dynamic models J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-12 Christian Francq, Jean-Michel Zakoïan
Estimating functions provide a very general framework for the statistical inference of dynamic models under weak assumptions. We consider a class of time series models consisting in the parametrization of the first two conditional moments which – by contrast with classical location-scale dynamic models – do not impose further constraints on the conditional distribution/moments. Quasi-likelihood estimators
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Multi-purpose open-end monitoring procedures for multivariate observations based on the empirical distribution function J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-06 Mark Holmes, Ivan Kojadinovic, Alex Verhoijsen
We propose non-parametric open-end sequential testing procedures that can detect all types of changes in the contemporary distribution function of possibly multivariate observations. Their asymptotic properties are theoretically investigated under stationarity and under alternatives to stationarity. Monte Carlo experiments reveal their good finite-sample behavior in the case of continuous univariate
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A multiplicative thinning-based integer-valued GARCH model J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-05 Abdelhakim Aknouche, Manuel G. Scotto
In this article, we introduce a multiplicative integer-valued time series model, which is defined as the product of a unit-mean integer-valued independent and identically distributed (i.i.d.) sequence, and an integer-valued dependent process. The latter is defined as a binomial thinning operation of its own past and of the past of the observed process. Furthermore, it combines some features of the
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Editorial announcement J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-03-03
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 Steve Leybourne and Professor Dag Tjøstheim very much for their dedicated service as Co-Editors of the Journal of Time Series Analysis since January 2013, and as Associate Editors of the journal prior to that. Both have stepped down with effect from
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Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-01-19 Abdelhakim Aknouche, Stefanos Dimitrakopoulos
We propose a multiplicative autoregressive conditional proportion (ARCP) model for (0,1)-valued time series, in the spirit of GARCH (generalized autoregressive conditional heteroscedastic) and ACD (autoregressive conditional duration) models. In particular, our underlying process is defined as the product of a (0,1)-valued independent and identically distributed (i.i.d.) sequence and the inverted conditional
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Geometric ergodicity and conditional self-weighted M-estimator of a GRCAR(p) model with heavy-tailed errors J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-01-19 Xiaoyan Li, Jiazhu Pan, Anchao Song
We establish the geometric ergodicity for general stochastic functional autoregressive (linear and nonlinear) models with heavy-tailed errors. The stationarity conditions for a generalized random coefficient autoregressive model (GRCAR( p )) are presented as a corollary. And then, a conditional self-weighted M-estimator for parameters in the GRCAR( p ) is proposed. The asymptotic normality of this
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Regime switching models for circular and linear time series J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-01-16 Andrew Harvey, Dario Palumbo
The score-driven approach to time series modelling is able to handle circular data and switching regimes with intra-regime dynamics. Furthermore it enables a dynamic model to be fitted to a linear and a circular variable when their joint distribution is a cylinder. The viability of the new method is illustrated by estimating models for hourly data on wind direction and speed in Galicia, north-west
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Some recent trends in embeddings of time series and dynamic networks J. Time Ser. Anal. (IF 0.9) Pub Date : 2023-01-15 Dag Tjøstheim, Martin Jullum, Anders Løland
We give a review of some recent developments in embeddings of time series and dynamic networks. We start out with traditional principal components and then look at extensions to dynamic factor models for time series. Unlike principal components for time series, the literature on time-varying nonlinear embedding is rather sparse. The most promising approaches in the literature is neural network based
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Factor models for high-dimensional functional time series II: Estimation and forecasting J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-12-17 Shahin Tavakoli, Gilles Nisol, Marc Hallin
This article is the second one in a set of two laying the theoretical foundations for a high-dimensional functional factor model approach in the analysis of large cross-sections (panels) of functional time series (FTS). Part I establishes a representation result by which, under mild assumptions on the covariance operator of the cross-section, any FTS admits a canonical representation as the sum of
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Factor models for high-dimensional functional time series I: Representation results J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-12-17 Marc Hallin, Gilles Nisol, Shahin Tavakoli
In this article, which consists of two parts (Part I: representation results; Part II: estimation and forecasting methods), we set up the theoretical foundations for a high-dimensional functional factor model approach in the analysis of large cross-sections (panels) of functional time series (FTS). In Part I, we establish a representation result stating that, under mild assumptions on the covariance
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Detecting relevant changes in the spatiotemporal mean function J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-12-11 Holger Dette, Pascal Quanz
For a spatiotemporal process { X j ( s , t ) ∣ s ∈ S , t ∈ T } j = 1 , … , n , where S denotes the set of spatial locations and T the time domain, we consider the problem of testing for a change in the sequence of mean functions { μ j ( s , t ) ∣ s ∈ S , t ∈ T } j = 1 , … , n . In contrast to most of the literature, we are not interested in arbitrarily small changes but only in changes with a norm
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Editorial Announcement: Journal of Time Series Analysis Distinguished Authors 2022 J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-12-07 Robert Taylor
In recognition of authors who have made significant contributions to this Journal, the Journal of Time Series Analysis runs a scheme to honour 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, ½ point for each double-authored article, 1/3 point
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On highly skewed fractional log-stable noise sequences and their application J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-11-13 Harry Pavlopoulos, George Chronis
Considering log-LFSN (log-linear fractional stable noise) sequences { Y n = e δ · X n + ε } n ∈ ℤ , driven by non-Gaussian one-sided LFSN { X n } n ∈ ℤ with constant skewness intensity β 0 ∈ [ − 1 , 1 ], for any δ ∈ ℝ − { 0 } and ε ∈ ℝ, we show that the auto-covariance function (ACVF) { γ Y ( h ) } h ∈ ℤ exists if and only if { X n } n ∈ ℤ is persistent, with stability index α ∈ ( 1 , 2 ), Hurst exponent
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On the asymptotic behavior of bubble date estimators J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-11-13 Eiji Kurozumi, Anton Skrobotov
In this study, we extend the three-regime bubble model of Pang et al. (2021, Journal of Econometrics, 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
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Corrigendum to the article “Regular multidimensional stationary time series” J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-11-02 Tamás Szabados
In Theorem 2.1 which was the main result of the article it was implicitly assumed that for any regular d -dimensional weakly stationary time series { X t } of rank r , 1 ≤ r ≤ d , there exists an analytic spectral factor Φ ( z ) of the form Φ ( e − i ω ) = 2 π U ˜ ( ω ) Λ r 1 / 2 ( ω ) , where Λ r ( ω ) is the r × r diagonal matrix of the positive eigenvalues of the spectral density matrix f ( ω )
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System identification using autoregressive Bayesian neural networks with nonparametric noise models J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-10-17 Christos Merkatas, Simo Särkkä
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
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A nonparametric predictive regression model using partitioning estimators based on Taylor expansions J. Time Ser. Anal. (IF 0.9) Pub Date : 2022-10-17 Jose Olmo
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