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Abstract
Export is a trading activity carried out between countries by bringing or sending goods originating from within the country to foreign countries with the aim of selling or marketing them. Exports as a source of state revenue are needed for the economy because exports can make a major contribution to economic stability and growth. Export values that experience a decrease or increase in the future need to be considered. For this reason, the purpose of this study is to forecast the value of exports in Indonesia for the coming period. Export value data is treated as hierarchical time series data. The top-down method is applied based on historical proportions, so only the total series of export values needs to be modeled. This study implements Autoregressive Integrated Moving Average (ARIMA) to model the total series of export values. The performance of the method is evaluated based on the out-of-sample mean absolute percentage error (MAPE). The results show that the MAPE for out-of-sample is 9.91%. These results indicate that the performance of the method for forecasting export values in Indonesia is highly accurate.
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References
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References
S. Aryanto, Syaparuddin, and S. Aminah, “Analisis Dampak Nilai Tukar dan Penanaman Modal Asing terhadap Nilai Ekspor Indonesia Periode 1990-2018,” J. Ekon. Aktual, vol. 1, no. 1, pp. 11–22, 2021.
Pemerintah Republik Indonesia, Undang-Undang Republik Indonesia Nomor 42 Tahun 2009 tentang Perubahan Ketiga Atas Undang-Undang Nomor 8 Tahun 1983 tentang Pajak Pertambahan Nilai Barang dan Jasa dan Pajak Penjualan Atas Barang Mewah. Indonesia, 2009.
S. Hodijah and G. P. Angelina, “Analisis Pengaruh Ekspor dan Impor Terhadap Pertumbuhan Ekonomi di Indonesia,” J. Manaj. Terap. Dan Keuang., vol. 10, no. 01, pp. 53–62, 2021.
Wikipedia, “Daftar Negara Menurut Jumlah Ekspor,” 2022. https://id.wikipedia.org/wiki/Daftar_negara_menurut_jumlah_ekspor.
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice Second Edition. Melbourne: OTexts, 2018.
G. Athanasopoulos, R. A. Ahmed, and R. J. Hyndman, “Hierarchical Forecasts for Australian Domestic Tourism,” Int. J. Forecast., vol. 25, no. 1, pp. 146–166, 2009.
R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos, and H. L. Shang, “Optimal Combination Forecasts for Hierarchical Time Series,” Comput. Stat. Data Anal., vol. 55, no. 9, pp. 2579–2589, 2011.
I. G. S. A. Prayoga, S. P. Rahayu, and Suhartono, “Hierarchical Forecasting Method Based on ARIMAX and Recurrent Neural Network for Motorcycle Sales Prediction,” Int. J. Appl. Math. Stat., vol. 53, no. 5, pp. 116–124, 2015.
L. Lapide, “Top-Down & Bottom-Up forecasting in S&OP,” J. Bus. Forecast. Methods Syst., vol. 25, no. 2, 2006.
D. C. Montgomery, C. L. Jennings, and M. Kulahci, Time Series Analysis and Forecasting Second Edition. Hoboken, New Jersey: John Wiley & Sons, Inc., 2015.
H. L. Shang and P. W. F. Smith, “Grouped Time-Series Forecasting With an Application to Regional Infant Mortality Counts,” 2013.
J. J. M. Moreno, A. P. Pol, A. S. Abad, and B. C. Blasco, “Using the R-MAPE Index as a Resistant Measure of Forecast Accuracy,” Psicothema, vol. 25, no. 4, pp. 500–506, 2013.