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Bilinear Forecast Risk Assessment for Non-systematic Inflation: Theory and Evidence

In: Advances in Non-linear Economic Modeling

Author

Listed:
  • Wojciech W. Charemza

    (University of Leicester
    AFiBV)

  • Yuriy Kharin

    (Belarusian State University)

  • Vladislav Maevskiy

    (EPAM-Systems)

Abstract

The paper aims at assessing the forecast risk and the maximum admissible forecast horizon for the non-systematic component of inflation modeled autoregressively, where a distortion is caused by a simple nonlinear (bilinear) process. The concept of the guaranteed upper risk of forecasting and the δ-admissible distortion level is defined. In order to make this concept operational we propose a method of evaluation of the p-maximum admissible forecast risk, on the basis of the maximum likelihood estimates of the bilinear coefficient. It has been found that for the majority of developed countries (in terms of average GDP per capita) the maximum admissible forecast horizon is between 5 and 12 months, while for the poorer countries it is either shorter than 5 or longer than 12. There is also a negative correlation of the maximum admissible forecast horizon with the average GDP growth.

Suggested Citation

  • Wojciech W. Charemza & Yuriy Kharin & Vladislav Maevskiy, 2014. "Bilinear Forecast Risk Assessment for Non-systematic Inflation: Theory and Evidence," Dynamic Modeling and Econometrics in Economics and Finance, in: Frauke Schleer-van Gellecom (ed.), Advances in Non-linear Economic Modeling, edition 127, pages 205-232, Springer.
    • Handle: RePEc:spr:dymchp:978-3-642-42039-9_6
    DOI: 10.1007/978-3-642-42039-9_6
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    Cited by:

    1. is not listed on IDEAS
    2. Roberto Leon-Gonzalez & Fuyu Yang, 2017. "Bayesian inference and forecasting in the stationary bilinear model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(20), pages 10327-10347, October.

    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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