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Forecasting inflation in post-oil boom years: A case for non-linear models?

Author

Listed:
  • Vugar Ahmadov

    () (Central Bank of Azerbaijan Republic)

  • Shaig Adigozalov

    () (Central Bank of Azerbaijan Republic)

  • Salman Huseynov

    () (Central Bank of Azerbaijan Republic)

  • Fuad Mammadov

    () (Central Bank of Azerbaijan Republic)

  • Vugar Rahimov

    () (Central Bank of Azerbaijan Republic)

Abstract

In this study, we investigate relative performance of various non-linear models against that of an autoregressive model in forecasting future inflation. We find that non-linear models have trivial forecast superiority over the univariate autoregressive model in terms of central forecast accuracy. They also perform poorly when their forecasts are measured against those of the 3 variables VAR model. In addition, we also show that non-linear models cannot beat the random walk in terms of central forecast accuracy which is in line with the previous literature on Azerbaijan during the post-oil boom years. However, we also demonstrate that non-linear models still have clear forecast advantage over both linear and random walk models in predicting forecast density.

Suggested Citation

  • Vugar Ahmadov & Shaig Adigozalov & Salman Huseynov & Fuad Mammadov & Vugar Rahimov, 2016. "Forecasting inflation in post-oil boom years: A case for non-linear models?," Working Papers 1601, Central Bank of Azerbaijan Republic.
  • Handle: RePEc:aze:wpaper:1601
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    References listed on IDEAS

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    1. Huseynov, Salman & Ahmadov, Vugar & Adigozalov, Shaig, 2014. "Beating a Random Walk: “Hard Times” for Forecasting Inflation in Post-Oil Boom Years?," MPRA Paper 63515, University Library of Munich, Germany.
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    Cited by:

    1. Huseynov, Salman & Mammadov, Fuad, 2016. "A small scale forecasting and simulation model for Azerbaijan (FORSAZ)," MPRA Paper 76348, University Library of Munich, Germany.

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    More about this item

    Keywords

    Forecasting; Bayesian methods; Non-linear models;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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