ARIMA-ARCH-Based Economic Growth Prediction for Lampung Province

  • Gitara Dhea Pramudita Universitas lampung
  • Dedy Yuliawan Universitas Lampung
Keywords: Economic Growth, Forecasting, ARCH, ARIMA, Lampung Province

Abstract

Economic growth is an important indicator of regional economic performance and is influenced by dynamic changes over time. This study aims to analyze the forecasting of economic growth in Lampung Province using the ARIMA-ARCH method. The study uses quarterly secondary data on Lampung Province's economic growth derived from Gross Regional Domestic Product (GRDP) at constant prices for the period 2010Q1–2025Q4. The analysis was conducted using the EViews 10 software through several stages, including stationarity testing, ARIMA model identification, parameter estimation, diagnostic testing, ARCH-LM testing, and the formation of the ARIMA-ARCH model. The results indicate that the ARIMA(1,0,0) model is the most appropriate model for describing the mean process of economic growth. The ARCH-LM test indicates the presence of conditional heteroskedasticity in the ARIMA residuals, so the analysis is continued using the ARCH model. The ARIMA(1,0,0)-ARCH(1) model is selected as the final model because the parameters are statistically significant and the model provides a parsimonious representation of the residual volatility. The resulting model is then used to forecast Lampung Province's economic growth for the period 2026Q1–2027Q4. The findings are expected to provide information that can support regional economic planning and policy formulation.

References

Badan Pusat Statistik Provinsi Lampung. (2026). Laju pertumbuhan y to y PDRB triwulanan (persen). Badan Pusat Statistik Provinsi Lampung. https://lampung.bps.go.id/id/statistics-table/2/ODkwIzI=/laju-pertumbuhan-y-on-y-produk-domestik-regional-bruto-pdrb-triwulanan-...

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley. Link akses: https://www.wiley.com/

Emalia, Z. (2018). Telaah peran sektor pertanian dalam perekonomian Propinsi Lampung: Sebuah eksplorasi dengan data input-output. Jurnal Ekonomi Pembangunan, 7(1), 51–74.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. Link akses: https://onlinelibrary.wiley.com/journal/14680262

Fatmawati, A., Yuliawan, D., Emalia, Z., & MK, I. F. (2025). Analisis faktor-faktor yang mempengaruhi kualitas lingkungan Indonesia. E-Journal Field of Economics, Business and Entrepreneurship (EFEBE), 3(3), 478–488. https://doi.org/10.23960/efebe.v3i3.269

Enders, W. (2015). Applied econometric time series (4th ed.). Wiley. Link akses: https://onlinelibrary.wiley.com/

Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill. Link akses: https://www.mheducation.com/

Halvis, H., & Emalia, Z. (2019). Potensi ekonomi lokal di daerah tertinggal dan ketimpangan antar wilayah di Provinsi Lampung. Jurnal Ekonomi Pembangunan, 8(1), 56–70. https://doi.org/10.23960/jep.v8i1.34

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting (2nd ed.). Wiley. Link akses: https://www.wiley.com/

Rosadi, D. (2012). Ekonometrika & analisis runtun waktu terapan dengan EViews. Andi. Link akses: https://inlislite.ipdn.ac.id/opac/detail-opac?id=21754

Wei, W. W. S. (2006). Time series analysis: Univariate and multivariate methods (2nd ed.). Pearson. Link akses: https://www.pearson.com/
Published
2026-08-11
How to Cite
Gitara Dhea Pramudita, & Dedy Yuliawan. (2026). ARIMA-ARCH-Based Economic Growth Prediction for Lampung Province. International Journal of Business and Quality Research, 4(03), 1060-1078. https://doi.org/10.63922/ijbqr.v4i03.5516