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Forecasting with Misspecified Models

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  • N. Davies
  • P. Newbold

Abstract

In this paper we examine the situation where, for a single time series, an incorrect model is assumed. The cost of this misspecification, in terms of increased expected‐squared‐error of prediction, is derived. The case where the assumed model is autoregressive is examined in detail with estimation error taken into account. Our main conclusions are that, for sample sizes that often occur in practice, fitted autoregressive models of high order can yield grossly sub‐optimal forecasts.

Suggested Citation

  • N. Davies & P. Newbold, 1980. "Forecasting with Misspecified Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 87-92, March.
    • Handle: RePEc:bla:jorssc:v:29:y:1980:i:1:p:87-92
    DOI: 10.2307/2346415
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    Cited by:

    1. Ismael Sanchez & Daniel Pena, 2001. "Properties of Predictors in Overdifferenced Nearly Nonstationary Autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(1), pages 45-66, January.
    2. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    3. Gonçalves Mazzeu, Joao Henrique & Ruiz Ortega, Esther & Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.

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