The Performance of ARIMAX Model and Vector Autoregressive (VAR) Model in Forecasting Strategic Commodity Price in Indonesia

Anggraeni, Wiwik; Andri, Kuntoro Boga; Sumaryanto,; Mahananto, Faizal

doi:10.1016/j.procs.2017.12.146

Cited by 35 publications

(15 citation statements)

References 8 publications

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“…The VAR model is a linear predictive model, which allows the variables to be forecasted by past values. According to previous studies [84][85][86], the VAR model is widely used in…”

Section: Var Analysismentioning

confidence: 99%

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Zhu

Zhang

et al. 2021

PLoS ONE

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This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.

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“…The VAR model is a linear predictive model, which allows the variables to be forecasted by past values. According to previous studies [84][85][86], the VAR model is widely used in…”

Section: Var Analysismentioning

confidence: 99%

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Zhu

Zhang

et al. 2021

PLoS ONE

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“…First, we used forecasting‐based ARIMA models (Wang et al, 2013 ) for predicting the observations of the second halves of the observations based on the first halves (2020 from 2019 and pandemic from pre‐pandemic observations). Second, we used regression with ARIMA errors and external regressors over the entire time series (Anggraeni et al, 2017 ; Ling et al, 2019 ; Pankratz, 1991 ) in order to test whether there was a statistically significant change in the processes before and after the two specified breakpoints.…”

Section: Methodsmentioning

confidence: 99%

Spontaneous charitable donations in Sweden before and after COVID: A natural experiment

Erlandsson

Nilsson

Alì

et al. 2022

Journal of Philanthropy and Marketing

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Did the outbreak of COVID‐19 influence spontaneous donation behavior? To investigate this, we conducted a natural experiment on real donation data. We analyzed the absolute amount, and the proportion of total payments, donated by individuals to charitable organizations via Swish—a widely used mobile online payment application through which most Swedes prefer to make their donations to charity—each day of 2019 and 2020. Spontaneous charitable donations were operationalized as Swish‐payments to numbers starting with 90, as this number is a nationally acknowledged quality control label that is provided to all fundraising operations that are monitored by the Swedish Fundraising Control. The results show that the Swish‐donations fluctuated substantially depending on season (less donations in January–February and during the summer months, and more donations in April–May and during the last months of the year) and specific events (peaks in Swish‐donations often coincided with televised charity fundraising galas). Interrupted time‐series analyses revealed that spontaneous donations were overall unaffected by the pandemic outbreak.

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“…Data mining analysis for forecasting the total delivery of goods using data from the last 5 periods and producing accuracy using MAPE as a testing method with a yield of 34% [21] and researching forecasting to predict rice prices using the autoregressive integrated moving average with exogeneous (ARIMAX) model and the vector autoregressive (VAR) model involving several variables, including consumer rice price, production, dry milled rice, harvested area and rice prices in Thailand. The results of the study show that the ARIMAX model can predict the consumer price of rice with a MAPE of 0.15% and this is 15.27% better than the VAR model [22]. The study also made a model comparison method for forecast rice production in Indonesia using exponential smoothing and neural networks and then evaluated the error using MAPE and mean square error (MSE) which resulted in MAPE and MSE values that were lower by 6.7% and 1.5% compared to using statistical methods [23].…”

Section: Introductionmentioning

confidence: 93%

Comparison of the trend moment and double moving average methods for forecasting the number of dengue hemorrhagic fever patients

et al. 2021

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Spread of Dengue Hemorrhagic Fever (DHF) is influenced by an increase in air temperature due to changes in weather and population density so that there is a lot of exchange of dengue virus through the bite of the Aedes aegypti mosquito. Forecasting models are needed to predict the number of DHF patients in the future so that monitoring of the number of DHF patients can be carried out as anticipation and consideration of decision making. Forecasting the number of patients is based on actual data within 2 (two) previous years by comparing the two methods, namely trend moment and double moving average. To measure the accuracy of the forecasting results from the two forecasting methods, tracking signal and moving range are used. Based on the test results, it shows that the forecasting results are said to be good because no one has passed the upper control limit and lower control limit values so that the difference between the actual data and the forecasting results is not too significant and the trend moment more recommended because the difference in actual data and forecasting results are approached and shown in the pattern graph by looking at the data difference in each period.

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The Performance of ARIMAX Model and Vector Autoregressive (VAR) Model in Forecasting Strategic Commodity Price in Indonesia

Cited by 35 publications

References 8 publications

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Investor attention and cryptocurrency: Evidence from the Bitcoin market

Spontaneous charitable donations in Sweden before and after COVID: A natural experiment

Comparison of the trend moment and double moving average methods for forecasting the number of dengue hemorrhagic fever patients

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