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Implementing an approximate dynamic factor model to nowcast GDP using sensitivity analysis

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  • Duarte, Pablo
  • Süßmuth, Bernd

Abstract

Dynamic factor models based on Kalman Filter techniques are frequently used to nowcast GDP. This study deals with the selection of indicators for this practice. We propose a two-tiered mechanism which is shown in a case study to produce more accurate nowcasts than a benchmark stochastic process and a standard model including extreme bounds fragile indicators. Nowcasting accuracy nearly measures up to the one of real-time forecasts by an institution with an interest in high-quality nowcasts.

Suggested Citation

  • Duarte, Pablo & Süßmuth, Bernd, 2018. "Implementing an approximate dynamic factor model to nowcast GDP using sensitivity analysis," Working Papers 152, University of Leipzig, Faculty of Economics and Management Science.
  • Handle: RePEc:zbw:leiwps:152
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    Keywords

    dynamic factor; Kalman Filter; extreme bounds analysis;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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