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Selecting models with judgment

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  • Manganelli, Simone

Abstract

A statistical decision rule incorporating judgment does not perform worse than a judgmental decision with a given probability. Under model misspecification, this probability is unknown. The best model is the least misspecified, as it is the one whose probability of underperforming the judgmental decision is closest to the chosen probability. It is identified by the statistical decision rule incorporating judgment with lowest in sample loss. Averaging decision rules according to their asymptotic performance results in decisions which are weakly better than the best decision rule. The model selection criterion is applied to a vector autoregression model for euro area inflation. JEL Classification: C1, C11, C12, C13

Suggested Citation

  • Manganelli, Simone, 2018. "Selecting models with judgment," Working Paper Series 2188, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20182188
    Note: 196912
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2188.en.pdf
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    References listed on IDEAS

    as
    1. Marie Diron & Benoit Mojon, 2008. "Are inflation targets good inflation forecasts?," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 32(Q II), pages 33-45.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    4. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, November.
    5. Manganelli, Simone, 2009. "Forecasting With Judgment," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 553-563.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    inflation forecasting; model selection criteria; statistical decision theory;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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