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Using Macro Data To Obtain Better Micro Forecasts

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  • Magnus, Jan R.
  • Vasnev, Andrey L.

Abstract

We consider the problem of combining forecasts from two different levels (called “macro” and “micro”), where we have access to the forecasts and their precisions but not to the full data set. We develop a theoretical framework and provide Monte Carlo evidence in the cases of both perfect and imperfect aggregation. Our proposed procedure is simple and robust. We also extend the procedure to time series and propose a forecast model for the European zero rates, combining quarterly and monthly observations. We show that forecast accuracy is improved at both levels.We are grateful to the editors of this special issue and to two referees for their constructive comments, which greatly helped improve the paper. Earlier versions of this paper were presented at the NAKE workshop in Amsterdam, at Tilburg University, and at ESEM 2006 in Vienna. We thank the participants for their useful comments. In addition, we thank Steffan Berridge, John Einmahl, and Hans Schumacher for their constructive comments.

Suggested Citation

  • Magnus, Jan R. & Vasnev, Andrey L., 2008. "Using Macro Data To Obtain Better Micro Forecasts," Econometric Theory, Cambridge University Press, vol. 24(2), pages 553-579, April.
    • Handle: RePEc:cup:etheor:v:24:y:2008:i:02:p:553-579_08
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    Cited by:

    1. Nobuyuki Hanaki & Jan R. Mangus & Donghoon Yoo, 2021. "Statistics and common sense," ISER Discussion Paper 1150, Institute of Social and Economic Research, Osaka University.
    2. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.

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