Application of Vector Autoregressive with Exogenous Variable: Case Study of Closing Stock Price of PT INDF.Tbk and PT ICBP.Tbk

Putri, Almira Rizka; Usman, Mustofa; Warsono,; Widiarti,; Virginia, Erica

doi:10.1088/1742-6596/1751/1/012012

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…e Bayesian vector autoregressive model [6] uses the statistical properties of variables as the prior information of the VAR model parameters to overcome the overparameterization defect of the VAR model to a certain extent. From a theoretical point of view, the BVAR model has certain advantages in China's regional economic forecasting.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

Shang

Zhang

2021

Complexity

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The Bayesian vector autoregressive (BVAR) model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive (VAR) model, which can overcome the problem of too little freedom. The BVAR model established in this paper can overcome the problem of short time series data by using prior statistical information. In theory, it should have a good effect in China’s regional economic forecasting. Most regional forecasting model literature lacks out-of-sample forecasting error evaluation research in the real sense, but our early forecasts of major economic indicators provide an excellent opportunity for this paper to evaluate the actual forecast errors of the BVAR model in detail. The analysis in this paper shows that the prediction error of the BVAR model is very small and the prediction ability is very satisfactory. At the same time, this article also analyzes and points out the direction of efforts to further improve the prediction accuracy of the BVAR model.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

Shang

Zhang

2021

Complexity

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“…The limitation of the VAR model is the lack of built-on economic theory that imposes a theoretical structure on the equation; and this makes a direct interpretation of the estimated coefficients difficult (Gupta et al, 2020). Despite this limitation, the VAR model is applied in various fields such as economics (Nicholson et al, 2017;Hamzah et al, 2020;Muschilati & Irsalinda, 2020;Putri et al, 2021), ecology (Lui et al, 2007), energy (Usman et al, 2019;Farih & Prastyo, 2022), health ( (Sukono et al, 2023) and environment (Damon & Guillas, 2002;(Gweba et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Lag length selection is determining the number of previous observations in a time series which should be used as predictors of the VAR model. Lag length selection is one of the important aspects of VARX(p,s) model specification whereby the optimal VARX(p,s) model is determined by using Akaike (AIC), Schwarz-Bayesian (BIC), or Hannan-Quinn (HQ) lag selection criteria (Sathianandan, 2006;(Warsono et al, n.d.;Wei, 2019;Putri et al, 2021). , as stated in (Sathianandan, 2006)); Where: ∑ ̃𝑟is the maximum likelihood estimate of the innovation dispersion matrix ∑; r is the number of parameters estimated;…”

Section: Lag Length Selectionmentioning

confidence: 99%

A Mixed Method Approach for Analyzing Drivers of Land Use Land Cover Change in the Northwestern Ethiopian Highlands

Bogale,

Damene,

Seyoum

et al. 2024

Preprint

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This study gives empirical evidence on the drivers of land use land cover change by conducting qualitative assessment first, and then making use of time series data for quantifying the relationship between land use land cover change and factors that cause the change. Analyzing the drivers of land use land cover change employing a mixed method approach gives good ground for the selection of exogenous variables as well as justification of the results of the quantitative analysis. Temporal changes of land use land cover in a given time are interdependent with changes in the previous years which needs time series data analysis. Vector autoregressive models with exogenous variables were used to analyze the time series data. The findings revealed demographic and environmental factors were the main causes of land use and land cover change. Population growth was found among the key drivers for losses of the natural vegetation on the one hand and rehabilitation of bare lands and grazing lands on the other hand, through contributing productive labor force for rehabilitating gullies, construction of soil and water conservation structures, and plantations of trees on degraded lands. This implies population growth can be an opportunity or a threat for sustainable natural resources management depending on how the available labor force is used. Climatic factors like maximum temperature and precipitation were also important causes of change in land use land cover. The study has important contributions to improving land use practices through designing appropriate land resources management policies.

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“…Te signifcance levels and the degrees of freedom used are 0.05 and 7, respectively. Te test statistic is determined using equation (18). Te test statistics of e 1,t and e 2,t obtained are 3.3610 and 3.4270, respectively.…”

Section: Varx (7 1) Parameter Estimation Using ML Methodmentioning

confidence: 99%

“…Te general form of the vector autoregressive with exogenous variables (VARX) model with the order of the endogenous variables p and the order of the endogenous variables q, VARX (p, q), is expressed as follows [17][18][19][20][21]:…”

Section: Vector Autoregressive With Exogenous Variablesmentioning

confidence: 99%

Forecasting the Effectiveness of COVID-19 Vaccination Using Vector Autoregressive with an Exogenous Variable: On the Cases of COVID-19 in Indonesia

Ibrahim

Riaman

Hertini

et al. 2023

Discrete Dynamics in Nature and Society

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This study aims to forecast the COVID-19 spread in Indonesia involving vaccination factors using vector autoregressive with exogenous variables (VARX). The COVID-19 spread represented by active, recovered, and death case rate indicators acts as endogenous variables, while the COVID-19 vaccination represented by second-dose vaccination rates acts as exogenous variables. Because the sum of three COVID-19 spread indicators in one day is one, only two indicators with the highest correlation rates are involved in VARX modelling. The other indicator is practically projected by subtracting one from the sum of two indicator projection results. Based on the analysis results, the active and recovered case rates are two indicators chosen in VARX modelling. Using Akaike information criterion, the most suitable VARX model to project the case and recovered case rates are VARX (7, 1). This model is expected to help the Indonesian government project the COVID-19 spread in Indonesia.

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Application of Vector Autoregressive with Exogenous Variable: Case Study of Closing Stock Price of PT INDF.Tbk and PT ICBP.Tbk

Cited by 7 publications

References 4 publications

[Retracted] Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

[Retracted] Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

A Mixed Method Approach for Analyzing Drivers of Land Use Land Cover Change in the Northwestern Ethiopian Highlands

Forecasting the Effectiveness of COVID-19 Vaccination Using Vector Autoregressive with an Exogenous Variable: On the Cases of COVID-19 in Indonesia

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