The paper develops a method for producing current quarter forecasts of gross domestic product growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to gross domestic product, we consider versions of the model with both constant variances and stochastic volatility. We use Bayesian methods to estimate the model, to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of realtime gross domestic product growth in the USA from 1985 through 2011. In terms of point forecasts, our proposal improves significantly on auto-regressive models and performs comparably with survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful.

中文翻译：

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使用具有随机波动性的贝叶斯混合频率模型进行实时预报。


该论文开发了一种方法，通过一系列（可能很大）的季度内经济指标（例如就业和工业生产）以及金融指标（例如股票价格）的月度观察数据来预测当前季度的国内生产总值增长和利率。鉴于国内生产总值冲击方差随时间变化的现有证据，我们考虑具有恒定方差和随机波动性的模型版本。我们使用贝叶斯方法来估计模型，以便于提供（可能很大的）模型参数集的收缩并方便地生成预测密度。我们提供了 1985 年至 2011 年美国实时国内生产总值增长的即时预测的准确性结果。在点预测方面，我们的建议在自回归模型上显着改进，并且与调查预测相当。此外，它还提供可靠的密度预测，其中随机波动率规范非常有用。