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Understanding forecast failure of ESTAR models of real exchange rates

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  • Daniel Buncic

Abstract

The forecast performance of the empirical ESTAR model of Taylor, Peel and Sarno (2001) is examined for 4 bilateral real exchange rate series over an out-of-sample eval-uation period of nearly 12 years. Point as well as density forecasts are constructed, considering forecast horizons of 1 to 22 steps head. The study finds that no forecast gains over a simple AR(1) specification exist at any of the forecast horizons that are considered, regardless of whether point or density forecasts are utilised in the evaluation. Non-parametric methods are used in conjunction with simulation techniques to learn about the models and their forecasts. It is shown graphically that the nonlinearity in the point forecasts of the ESTAR model decreases as the forecast horizon increases. The non-parametric methods show also that the multiple steps ahead forecast densities are normal looking with no signs of bi-modality, skewness or kurtosis. Overall, there seems little to be gained from using an ESTAR specification over a simple AR(1) model.

Suggested Citation

  • Daniel Buncic, 2009. "Understanding forecast failure of ESTAR models of real exchange rates," EERI Research Paper Series EERI_RP_2009_18, Economics and Econometrics Research Institute (EERI), Brussels.
    • Handle: RePEc:eei:rpaper:eeri_rp_2009_18
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    3. Addo, Peter Martey & Billio, Monica & Guégan, Dominique, 2014. "The univariate MT-STAR model and a new linearity and unit root test procedure," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 4-19.
    4. Peter Martey Addo & Monica Billio & Dominique Guegan, 2011. "A New Modelling Test: The Univariate MT-STAR Model," Documents de travail du Centre d'Economie de la Sorbonne 11083r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Aug 2013.
    5. Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Understanding Exchange Rates Dynamics," Documents de travail du Centre d'Economie de la Sorbonne 13023, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    6. Buncic, Daniel & Melecky, Martin, 2013. "Macroprudential stress testing of credit risk: A practical approach for policy makers," Journal of Financial Stability, Elsevier, vol. 9(3), pages 347-370.
    7. Peter Martey Addo & Monica Billio & Dominique Guegan, 2011. "A test for a new modelling : The Univariate MT-STAR Model," Post-Print halshs-00659158, HAL.
    8. Buncic, Daniel & Melecky, Martin, 2014. "Equilibrium credit: The reference point for macroprudential supervisors," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 135-154.
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    10. Daniel Buncic, 2019. "Identification and Estimation Issues in Exponential Smooth Transition Autoregressive Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(3), pages 667-685, June.
    11. Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.

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    More about this item

    Keywords

    Purchasing power parity; regime modelling; non-linear real exchange rate models; ESTAR; forecast evaluation; density forecasts; non-parametric methods.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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