Rényi entropy and divergence for VARFIMA processes based on characteristic and impulse response functions

Contreras-Reyes, Javier E.

doi:10.1016/j.chaos.2022.112268

Cited by 25 publications

(4 citation statements)

References 32 publications

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“…We should conclude with possible extensions of our results. There have been papers on the asymptotic information matrix for extended models like ARMA models with periodic coefficients [45,46], state-space models [46], Markov switching VARMA models [47], or VARFIMA models [48]. It is possible that the approach described in this paper can be extended to these extended models.…”

Section: Discussionmentioning

confidence: 99%

An Algorithm for the Fisher Information Matrix of a VARMAX Process

Klein,

Mélard

2023

Algorithms

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In this paper, an algorithm for Mathematica is proposed for the computation of the asymptotic Fisher information matrix for a multivariate time series, more precisely for a controlled vector autoregressive moving average stationary process, or VARMAX process. Meanwhile, we present briefly several algorithms published in the literature and discuss the sufficient condition of invertibility of that matrix based on the eigenvalues of the process operators. The results are illustrated by numerical computations.

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Section: Discussionmentioning

confidence: 99%

An Algorithm for the Fisher Information Matrix of a VARMAX Process

Klein,

Mélard

2023

Algorithms

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“…With respect to the proposed models, further studies might focus on other indexes, such as a daily S&P500 index, the Hang-Seng index (Shum 2020), foreign exchange rates (Ramírez-Parietti et al 2021), or another time series. Moreover, economic uncertainty could be modeled by a multivariate approach (Arnold and Günther 2001;Contreras-Reyes 2022). The proposed models may be applied to similar economies of other Latin American countries by adapting the relevant variants, such as the external factors.…”

Section: Discussionmentioning

confidence: 99%

A Threshold GARCH Model for Chilean Economic Uncertainty

Chávez

Contreras-Reyes

Idrovo-Aguirre

2022

JRFM

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In this paper, an autoregressive moving average (ARMA) model with threshold generalized autoregressive conditional heteroscedasticity (TGARCH) innovations is considered to model Chilean economic uncertainty time series. Uncertainty is measured through the Business Confidence Index (BCI) and Consumer Perception Index (CPI). The BCI time series provide useful information about industry; commerce; the finance, mining, construction, and agricultural sectors; and the global economic situation and the general business situation. As a counterpart, the CPI time series measure the perception of consumers regarding the state of the Chilean economy, evaluating their economic situation and expectations. The ARMA-TGARCH model is compared with the classical seasonal ARIMA and threshold AR ones. The results show that the ARMA-TGARCH model explains the regime changes in economic uncertainty better than the others, given that negative shocks are associated with statistically significant and quantitatively larger levels of volatility produced by the COVID-19 pandemic. In addition, a diagnostic analysis and prediction performance illustrates the suitability of the proposed model. Using a cross-validation analysis for the forecasting performance, a proposed heteroscedastic model may effectively help improve the forecasting accuracy for observations related to pessimism periods like the social uprising and the COVID-19 crisis which produced volatility in the Chilean uncertainty indexes.

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“…Prediction methods impute the dataset by predicting the value of missing data via prediction models, which are usually based on historical data [29]. Previously, one-way prediction methods, including heuristic techniques (HT) (historical average, weighted average) [30], Kalman filter (KF) [31], autoregressive integrated moving average (ARIMA) [32], data augmentation (DA) [30], seasonal ARIMA [33,34], feed-forward neural network (FFNN) [35] and fractionally integrated vector autoregressive and moving average (VARFIMA) [36], have been based mostly on temporal neighboring information. However, these methods assume that the value of the missing interval of a day is similar to the latest intervals or the same interval in neighboring days.…”

Section: Methodsmentioning

confidence: 99%

Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms

Sun

Zhu²,

Hao

et al. 2022

Mathematics

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A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.

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Rényi entropy and divergence for VARFIMA processes based on characteristic and impulse response functions

Cited by 25 publications

References 32 publications

An Algorithm for the Fisher Information Matrix of a VARMAX Process

An Algorithm for the Fisher Information Matrix of a VARMAX Process

A Threshold GARCH Model for Chilean Economic Uncertainty

Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms

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