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

 0 Người đánh giá. Xếp hạng trung bình 0

Tác giả: Florian Huber, Michael Pfarrhofer

Ngôn ngữ: eng

Ký hiệu phân loại: 003.76 Stochastic systems

Thông tin xuất bản: 2020

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 164438

 Comment: JEL: C11, C32, C53, E31
  Keywords: state-space models, inflation forecasting, inflation uncertainty, real time data, replicationSuccessful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state-of-the-art shrinkage techniques that allow for time-variation in the degree of shrinkage. Using a real-time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters slightly improves forecast performance for the United States (US), the United Kingdom (UK) and the Euro Area (EA). Comparing in-sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
Tạo bộ sưu tập với mã QR

THƯ VIỆN - TRƯỜNG ĐẠI HỌC CÔNG NGHỆ TP.HCM

ĐT: (028) 36225755 | Email: tt.thuvien@hutech.edu.vn

Copyright @2024 THƯ VIỆN HUTECH