Practical Methods for Modeling Weak VARMA Processes: Identification, Estimation and Specification With a Macroeconomic Application

Dufour, Jean–Marie; Pelletier, Denis

doi:10.1080/07350015.2021.1904960

Cited by 13 publications

(2 citation statements)

References 47 publications

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“…Feunou & Fontaine, 2009) represented the yield curve with a VARMA model, removing the limits on co-integration. Dufour & Pelletier, 2022) have presented a modified information criterion for determining the VARMA orders, which is merely a modification of the criterion developed by Hannan and Rissanen (1982). As demonstrated by (Mainassara, 2010), selecting a VARMA (p, q) order that is too small results in inconsistent estimators, whereas a VARMA (p, q) order that is too large reduces the accuracy of forecasts.…”

Section: Literature Reviewmentioning

confidence: 99%

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Vector Autoregressive Integrating Moving Average (Varima) Model of COVID-19 Pandemic and Oil Price

Karim¹,

Ahmed²

2023

J. Professional Business Review

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Purpose: A coronavirus associated with severe respiratory syndrome has created Coronavirus Disease 2019 (COVID-19), a highly contagious illness that affects the entire world population. On the other hand, COVID-19 is having a direct impact on human life because of its proliferation. So, the study's goal is to forecast and analyze the impact of the COVID-19 pandemic and the oil price utilizing multiple time series analysis methods (VARIMA model). Theoretical framework: Recent literature has reported that the multivariate time series is robust model for forecasting and analyzing dynamic relationship between series, while the univariate ARIMA model has been generalized to include vector variables, that is an extension of its capabilities. The VAR (p) model analyzes the interdependence between two or more series but does not take into account the impact of shocks at various time variable delays. Design/methodology/approach: This study uses VARMA (p, q) model which links a set of variables to their prior iterations as well as those of other variables and shocks to those same variables. Sample data concerning the COVID-19 pandemic and oil price was globally provided. It contains daily observations of them variables for the years 2020-2022. Findings: The best model is VARIMA (2,1,2), and the results shown that the oil price is not only influenced by itself but also influenced by the Covid-19 pandemic. Moreover, the standard error grows over time of the forecast. Research, Practical & Social implications: The best model is sound for short-term forecasting but unstable for long-term forecasting. Future researchers can integrate factors across areas. Include tourism demand and industry variables in modeling. Originality/value: Collecting COVID-19 pandemic data and oil price series in a modern model that is a multivariate time series model with a high predicted level of model accuracy between these variables in order to predict and analyze the effects between them series and estimate the interaction between these two series with the most recent data is the value of this study, and then offers merchants the chance to comprehend the forecasting of oil price throughout the covid-19 effects as well as the associated risks.

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Section: Literature Reviewmentioning

confidence: 99%

“…Given that the potential white noise processing a . Since 𝐸(𝑎 𝑡+ℎ |𝑍 𝑡 , 𝑍 𝑡−1 , … ) = 0 ℎ > 0, the predictor with the smallest mean square error is Z ̂𝑡(ℎ) = ∑ Ψ 𝑖 a 𝑡+ℎ−𝑖 ∞ 𝑖=1 (Dufour & Pelletier, 2022).…”

Section: Forecasting the Varma Modelmentioning

confidence: 99%