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Combined Density Nowcasting in an uncertain economic environment

Author

Listed:
  • Knut Are Aastveit

    (Norges Bank (Central Bank of Norway))

  • Francesco Ravazzolo

    (Norges Bank (Central Bank of Norway) and BI Norwegian Business School)

  • Herman K. van Dijk

    (Erasmus University Rotterdam, VU University Amsterdam and Tinbergen Institute,)

Abstract

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian Sequential Monte Carlo method which re-balances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on US real-time data of 120 leading indicators, indicate that CDN gives more accurate density nowcasts of US GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two rst months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.

Suggested Citation

  • Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    • Handle: RePEc:bno:worpap:2014_17
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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