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A note on linear combination of predictors

Author

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  • Ruiz, Edilberto
  • Nieto, Fabio H.

Abstract

An important concern in statistics is the linear combination of predictors of a random variable that are based on several sources of information. In the time-series context the technique is used, for example, to forecast or estimate missing observations. At the practical level, it is well known in the combining forecasts literature that combining is a pragmatic solution to the failure to synthesize all the information into an optimal forecast. In this paper we caution against using this procedure arbitrarily, in particular with weighted averages, to obtain an overall linear predictor of the random quantity. We illustrate the results with examples about estimating the missing observations in time series.

Suggested Citation

  • Ruiz, Edilberto & Nieto, Fabio H., 2000. "A note on linear combination of predictors," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 351-356, May.
  • Handle: RePEc:eee:stapro:v:47:y:2000:i:4:p:351-356
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    References listed on IDEAS

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    1. Palm, F. & Zellner, A., 1991. "To combine or not to combine? issues of combining forecasts," LIDAM Discussion Papers CORE 1991022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Nieto, Fabio H. & Guerrero, Victor M., 1995. "Kalman filter for singular and conditional state-space models when the system state and the observational error are correlated," Statistics & Probability Letters, Elsevier, vol. 22(4), pages 303-310, March.
    3. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    4. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    5. Guerrero, Victor M., 1993. "Combining historical and preliminary information to obtain timely time series data," International Journal of Forecasting, Elsevier, vol. 9(4), pages 477-485, December.
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