This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we f...This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).展开更多
Based on the X-13-ARIMA-SEATS model, aiming at the problem of mobile holidays in China’s economic data, this paper introduces a new method of seasonal adjustment based on the AICC criterion to objectively select the ...Based on the X-13-ARIMA-SEATS model, aiming at the problem of mobile holidays in China’s economic data, this paper introduces a new method of seasonal adjustment based on the AICC criterion to objectively select the parameters of dummy variables of mobile holidays. Taking the current total value of China’s import and export as an example, we expound</span><span style="font-family:""> </span><span style="font-family:Verdana;">a new method for seasonal adjustment of mobile holidays such as Spring Festival, Dragon Boat Festival and Mid-Autumn Festival. Finally, the model is used to predict the total value of China’s import and export in and out of the sample. The prediction results show that the relative error of the out of sample data is less than 5%. The new method has advantages in the processing of macroeconomic data.展开更多
文摘This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).
文摘Based on the X-13-ARIMA-SEATS model, aiming at the problem of mobile holidays in China’s economic data, this paper introduces a new method of seasonal adjustment based on the AICC criterion to objectively select the parameters of dummy variables of mobile holidays. Taking the current total value of China’s import and export as an example, we expound</span><span style="font-family:""> </span><span style="font-family:Verdana;">a new method for seasonal adjustment of mobile holidays such as Spring Festival, Dragon Boat Festival and Mid-Autumn Festival. Finally, the model is used to predict the total value of China’s import and export in and out of the sample. The prediction results show that the relative error of the out of sample data is less than 5%. The new method has advantages in the processing of macroeconomic data.
