Title(题名):  The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Mo
Authors(作者):  Joshua C.C. Chan
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
(卷,期,年):
 January2017,Vol35,Issue1
Pages(页码):  p17-28
ISSN:  0735-0015
Abstract(摘要):  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.
Key words(关键词):  Inflation forecasting; Inflation uncertainty; Nonlinear’, State-space.
Where(馆藏地):  304外文报刊阅览室
Available online
(提交时间):
 2018/1/6 8:32:42
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