BibTex Citation Data :
@article{J.Gauss35348, author = {Gandhes Linggar Winanti and Dwi Ispriyanti and Sugito Sugito}, title = {PEMODELAN INDEKS HARGA PERDAGANGAN BESAR (IHPB) SEKTOR EKSPOR MENGGUNAKAN ARFIMA-GARCH}, journal = {Jurnal Gaussian}, volume = {12}, number = {1}, year = {2023}, keywords = {WPI; Forecast; Long Memory; ARFIMA; GARCH.}, abstract = { Indonesia's price index serves as a barometer for the nation's economic condition. One of the Indonesia’s price index is Wholesale Price Index (WPI). WPI is a price index that tracks the average change in wholesale prices over time. Time series analysis can be used for forecasting because WPI is one of the time series data. WPI is long memory, which is a condition in which data from different time periods have a high link despite being separated by a large amount of time. The Autoregressive Fractional Integrated Moving Average (ARFIMA) model can be used to overcome this feature when modeling time series data. The assumption of constant error variance is not fulfilled in the IHPB data analysis, indicating that the data is heteroscedastic. The GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) model is one of the models used to overcome heteroscedasticity. The data used is the export sector of WPI from January 2003 to June 2021. The best model for forecasting WPI is ARFIMA(1, b ,2) – GARCH(1,1) with b= 0,7345333, and MAPE value is 3,150875%. }, issn = {2339-2541}, pages = {52--60} doi = {10.14710/j.gauss.12.1.52-60}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/35348} }
Refworks Citation Data :
Indonesia's price index serves as a barometer for the nation's economic condition. One of the Indonesia’s price index is Wholesale Price Index (WPI). WPI is a price index that tracks the average change in wholesale prices over time. Time series analysis can be used for forecasting because WPI is one of the time series data. WPI is long memory, which is a condition in which data from different time periods have a high link despite being separated by a large amount of time. The Autoregressive Fractional Integrated Moving Average (ARFIMA) model can be used to overcome this feature when modeling time series data. The assumption of constant error variance is not fulfilled in the IHPB data analysis, indicating that the data is heteroscedastic. The GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) model is one of the models used to overcome heteroscedasticity. The data used is the export sector of WPI from January 2003 to June 2021. The best model for forecasting WPI is ARFIMA(1,b,2) – GARCH(1,1) with b=0,7345333, and MAPE value is 3,150875%.
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