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Dynamic shrinkage in time-varying parameter stochastic volatility in mean models.

Florian Huber1, Michael Pfarrhofer1

  • 1Department of Economics Salzburg Centre of European Union Studies University of Salzburg Mönchsberg 2A Salzburg 5020 Austria.

Journal of Applied Econometrics (Chichester, England)
|April 19, 2021
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Summary
This summary is machine-generated.

This study enhances the stochastic volatility in mean (SVM) model with flexible shrinkage priors, improving inflation forecasting accuracy for the US, UK, and Euro Area. The modified model offers better performance while maintaining similar insights to the original.

Keywords:
inflation forecastinginflation uncertaintyreal‐time datareplicationstate‐space models

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Area of Science:

  • Economics
  • Econometrics
  • Time Series Analysis

Background:

  • Forecasting models require a balance between simplicity (parsimony) and adaptability (flexibility).
  • Shrinkage priors are commonly used to manage model complexity and fit.
  • The stochastic volatility in mean (SVM) model is a key tool in time series econometrics.

Purpose of the Study:

  • To modify the SVM model by incorporating advanced time-varying shrinkage techniques.
  • To assess the impact of flexible prior distributions on key parameters in inflation forecasting.
  • To evaluate the forecasting performance of the enhanced SVM model for major economies.

Main Methods:

  • Modification of the standard SVM model.
  • Introduction of state-of-the-art shrinkage priors allowing for time variation.
  • Application of a real-time inflation forecasting exercise for the United States, United Kingdom, and Euro Area.

Main Results:

  • The enhanced SVM model with more flexible priors sometimes improved inflation forecast performance.
  • In-sample comparisons indicated that the proposed model provided qualitatively similar insights to the original SVM.
  • The time-varying nature of shrinkage was shown to be a beneficial modification.

Conclusions:

  • Flexible shrinkage priors can enhance the performance of SVM models for inflation forecasting.
  • The modified SVM model offers a more adaptable approach to time series modeling.
  • The study highlights the importance of dynamic shrinkage in econometric forecasting.