In this paper we assess whether flexible modelling of innovations impact the predictive performance of the dividend price ratio for returns and dividend growth. Using Bayesian vector autoregressions we allow for stochastic volatility, heavy tails and skewness in the innovations. Our results suggest that point forecasts are barely affected by these features, suggesting that workhorse models on predictability are sufficient. For density forecasts, however, we find that stochastic volatility substantially improves the forecasting performance.

中文翻译：

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预测回报和股息增长——非高斯创新的作用

在本文中，我们评估了创新的灵活建模是否会影响股息价格比率对回报和股息增长的预测性能。使用贝叶斯向量自回归，我们允许创新中的随机波动、重尾和偏度。我们的结果表明，点预测几乎不受这些特征的影响，这表明关于可预测性的主力模型就足够了。然而，对于密度预测，我们发现随机波动显着提高了预测性能。