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Hybrids ARIMA-ANN models for GDP forecasting in Nepal

Author

Listed:
  • Satish Chaudhary

    (Nepal Rastra Bank, Foreign Exchange Management Department)

  • Dipika Uprety

    (Nepal Rastra Bank, Biratnagar Office)

Abstract

Forecasting Nepal's Gross Domestic Product (GDP) holds paramount importance for effective resource planning and allocation. In this research, Artificial Neural Networks (ANNs) have been introduced to predict the GDP time series, wherein the data have been dissected into linear and nonlinear components. The linear aspects have been handled by the ARIMA model, while the ANNs managed the nonlinear elements. Additionally, the study has delved into hybrid models, resulting in additive and multiplicative combinations of ARIMA and ANN. These hybrid models have aimed to enhance forecasting performance, minimize errors, and improve accuracy compared to standalone models. The findings revealed that both ANN and hybrid models surpassed other approaches in terms of prediction accuracy.

Suggested Citation

  • Satish Chaudhary & Dipika Uprety, 2023. "Hybrids ARIMA-ANN models for GDP forecasting in Nepal," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, vol. 35(1-2), pages 22-53, December.
    • Handle: RePEc:nrb:journl:v:35:y:2024:i:1:p:22
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    JEL classification:

    • B22 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Macroeconomics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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