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Development of Short-term Forecasting Models for Non-oil GDP

Author

Listed:
  • Nazrin Ramazanova

    (Central Bank of Azerbaijan Republic)

  • Ogtay Gahramanov

    (Central Bank of Azerbaijan Republic)

Abstract

Short-term forecasting of non-oil GDP is essential for preparing its medium-term projections. This paper evaluates the performance of three key forecasting models—Vector Autoregression (VAR), Mixed Data Sampling (MIDAS), and Dynamic Factor Models (DFM)—in predicting quarterly growth of non-oil GDP. The empirical results show that most models outperform the AR(1) benchmark in quarterly forecasts. To leverage the strengths of each model, the study also examines combined forecast results. A forecast combination based on mean squared error (MSE) weights improves forecast accuracy compared to individual models. Overall, the findings highlight the advantages of a multi-model forecasting strategy for enhancing the accuracy of short-term GDP growth predictions in Azerbaijan’s dynamic economic environment. The results emphasize the practical benefits of using combination methods to strengthen the reliability of short-term forecasts.

Suggested Citation

  • Nazrin Ramazanova & Ogtay Gahramanov, 2024. "Development of Short-term Forecasting Models for Non-oil GDP," Working Papers 2404, Central Bank of Azerbaijan Republic.
    • Handle: RePEc:aze:wpaper:2404
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    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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