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Model‐based forecast adjustment: With an illustration to inflation

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  • Philip Hans Franses

Abstract

This paper introduces the idea of adjusting forecasts from a linear time series model where the adjustment relies on the assumption that this linear model is an approximation of a nonlinear time series model. This way of creating forecasts could be convenient when inference for a nonlinear model is impossible, complicated or unreliable in small samples. The size of the forecast adjustment can be based on the estimation results for the linear model and on other data properties such as the first few moments or autocorrelations. An illustration is given for a first‐order diagonal bilinear time series model, which in certain properties can be approximated by a linear ARMA(1, 1) model. For this case, the forecast adjustment is easy to derive, which is convenient as the particular bilinear model is indeed cumbersome to analyze in practice. An application to a range of inflation series for low‐income countries shows that such adjustment can lead to some improved forecasts, although the gain is small for this particular bilinear time series model.

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  • Philip Hans Franses, 2019. "Model‐based forecast adjustment: With an illustration to inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(2), pages 73-80, March.
    • Handle: RePEc:wly:jforec:v:38:y:2019:i:2:p:73-80
    DOI: 10.1002/for.2557
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    References listed on IDEAS

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    1. Bos, Charles S. & Franses, Philip Hans & Ooms, Marius, 2002. "Inflation, forecast intervals and long memory regression models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 243-264.
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    4. Shiqing Ling & Liang Peng & Fukang Zhu, 2015. "Inference For A Special Bilinear Time-Series Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 61-66, January.
    5. Franses,Philip Hans, 2014. "Expert Adjustments of Model Forecasts," Cambridge Books, Cambridge University Press, number 9781107441613, June.
    6. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2014. "Misspecification Testing: Non-Invariance of Expectations Models of Inflation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 553-574, August.
    7. Poskitt, D. S. & Tremayne, A. R., 1986. "The selection and use of linear and bilinear time series models," International Journal of Forecasting, Elsevier, vol. 2(1), pages 101-114.
    8. Charemza, Wojciech W. & Lifshits, Mikhail & Makarova, Svetlana, 2005. "Conditional testing for unit-root bilinearity in financial time series: some theoretical and empirical results," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 63-96, January.
    9. Won Kyung Kim & L. Billard & I. V. Basawa, 1990. "Estimation For The First‐Order Diagonal Bilinear Time Series Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(3), pages 215-229, May.
    10. Weiss, Andrew A, 1986. "ARCH and Bilinear Time Series Models: Comparison and Combination," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 59-70, January.
    11. Franses,Philip Hans, 2014. "Expert Adjustments of Model Forecasts," Cambridge Books, Cambridge University Press, number 9781107081598, June.
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    More about this item

    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

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