The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling
将均值随机波动模型扩展为允许条件均值中的参数随时间变化，并开发了基于带和稀疏矩阵算法的高效MCMC估计方法，应用于美英德三国通胀数据，发现波动系数存在显著时变性且预测效果优于标准基准。
This article generalizes the popular stochastic volatility in mean model to allow for time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional variance, and its coefficient in the former is time-varying. We develop an efficient Markov chain Monte Carlo algorithm based on band and sparse matrix algorithms instead of the Kalman filter to estimate this more general variant. The methodology is illustrated with an application that involves U.S., U.K., and Germany inflation. The estimation results show substantial time-variation in the coefficient associated with the volatility, highlighting the empirical relevance of the proposed extension. Moreover, in a pseudo out-of-sample forecasting exercise, the proposed variant also forecasts better than various standard benchmarks.