• Corpus ID: 215737102

Forecasts with Bayesian vector autoregressions under real time conditions

@article{Pfarrhofer2020ForecastsWB,
  title={Forecasts with Bayesian vector autoregressions under real time conditions},
  author={Michael Pfarrhofer},
  journal={arXiv: Econometrics},
  year={2020}
}
This paper investigates the sensitivity of forecast performance measures to taking a real time versus pseudo out-of-sample perspective. We use monthly vintages for the United States (US) and the Euro Area (EA) and estimate a set of vector autoregressive (VAR) models of different sizes with constant and time-varying parameters (TVPs) and stochastic volatility (SV). Our results suggest differences in the relative ordering of model performance for point and density forecasts depending on whether… 

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