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Indicator Based Forecasting of Business Cycles

Author

Listed:
  • Fuad Mammadov

    (Central Bank of Azerbaijan Republic)

  • Shaig Adigozalov

    (Central Bank of Azerbaijan Republic)

Abstract

This paper has attempted to construct leading indicator systems and based on that to predict future contraction period of the Azerbaijan non-oil economy using more than 100 publicly available economic and financial data. Our results show plausible and significant performance of composite leading indicator system with average leading time of 7.2 months. We found that between January of 2000 and May of 2014, there were 6 turning points in Azerbaijan non-oil economy, consisting of three peaks and three troughs corresponding three expansion and four contraction periods. It turns out that the average duration of expansion and contraction phases is 43 and 10 month, respectively. Based on selected leading indicators we constructed composite indicator is found to be able to predict all the six turning points. Using dynamic probit model we estimated contraction probability of non-oil output gap for the future period. Out-of sample as well as in-sample forecast performance suggest that the leading indicator systems have significant predictive power and could be used as a useful tool for economic forecasting.

Suggested Citation

  • Fuad Mammadov & Shaig Adigozalov, 2015. "Indicator Based Forecasting of Business Cycles," Working Papers 1503, Central Bank of Azerbaijan Republic.
    • Handle: RePEc:aze:wpaper:1503
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    Keywords

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    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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