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On Evaluating the Importance of Non-Linearity in Large Macroeconometric Models

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  • Fisher, Paul
  • Salmon, Mark

Abstract

Most model builders continue to treat their models as deterministic when forecasting, despite the fact that these models are composed of equations which are stochastic in nature. Deterministic solution methods ignore the stochastic information on the model structure and in addition produce biased forecasts in non-linear models. It is therefore important to investigate whether a given model is significantly non-linear. After commenting on the poor simulation methodology employed in a number of earlier studies, we find significant non-linear effects in two large macro models of the United Kingdom economy. This is confirmed by two tests that we propose for assessing the importance of non-linearity in such models.

Suggested Citation

  • Fisher, Paul & Salmon, Mark, 1985. "On Evaluating the Importance of Non-Linearity in Large Macroeconometric Models," CEPR Discussion Papers 86, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:86
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    Cited by:

    1. Neil R. Ericsson & Jaime R. Marquez, 1990. "Evaluating the predictive performance of trade-account models," International Finance Discussion Papers 377, Board of Governors of the Federal Reserve System (U.S.).
    2. Bianchi, Carlo & Calzolari, Giorgio & Brillet, Jean-Louis, 1987. "Measuring forecast uncertainty : A review with evaluation based on a macro model of the French economy," International Journal of Forecasting, Elsevier, vol. 3(2), pages 211-227.
    3. Calzolari, Giorgio & Panattoni, Lorenzo, 1990. "Mode predictors in nonlinear systems with identities," International Journal of Forecasting, Elsevier, vol. 6(3), pages 317-326, October.
    4. Dennis Ridley & Pierre Ngnepieba, 2014. "Antithetic time series analysis and the CompanyX data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 83-94, January.
    5. Gajda, Jan B. & Markowski, Aleksander, 1998. "Model Evaluation Using Stochastic Simulations: The Case of the Econometric Model KOSMOS," Working Papers 61, National Institute of Economic Research.
    6. Calzolari, Giorgio, 1987. "La varianza delle previsioni nei modelli econometrici [Forecast variance in econometric models]," MPRA Paper 23866, University Library of Munich, Germany.
    7. Mariano, Roberto S, 1985. "Finite-Sample Properties in Stochastic Predictors in Nonlinear Systems : Some Initial Results," The Warwick Economics Research Paper Series (TWERPS) 266, University of Warwick, Department of Economics.
    8. Dag Kolsrud, 2008. "Stochastic Ceteris Paribus Simulations," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 21-43, February.
    9. Filippo Altissimo & Alberto Locarno & Stefano Siviero, 2002. "Dealing with forward-looking expectations and policy rules in quantifying the channels of transmission of monetary policy," Temi di discussione (Economic working papers) 460, Bank of Italy, Economic Research and International Relations Area.
    10. McAdam, Peter & Mestre, Ricardo, 2008. "Evaluating macro-economic models in the frequency domain: A note," Economic Modelling, Elsevier, vol. 25(6), pages 1137-1143, November.
    11. Brillet, Jean-Louis & Calzolari, Giorgio & Panattoni, Lorenzo, 1986. "Coherent optimal prediction with large nonlinear systems: an example based on a French model," MPRA Paper 29057, University Library of Munich, Germany.

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