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Real-time factor model forecasting and the effects of instability

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  • Clements, Michael P.

Abstract

Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.

Suggested Citation

  • Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
  • Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:661-675
    DOI: 10.1016/j.csda.2015.01.011
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    5. Kuusela, Annika & Hännikäinen, Jari, 2017. "What do the shadow rates tell us about future inflation?," MPRA Paper 80542, University Library of Munich, Germany.

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    More about this item

    Keywords

    Factor models; Robust approaches; Financial crisis;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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