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Moving average stochastic volatility models with application to inflation forecast

Date

Authors

Chan, Chi Chun (Joshua)

Publisher

Elsevier

Abstract

We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation ar

Keywords

Keywords: Density forecast; Measurement equations; Out-of-sample forecast; Precision; Sparse; State space representation; Stochastic Volatility Model; Unobserved components; Forecasting; Measurement errors; State space methods; Economic analysis Density forecast; Precision; Sparse; State space; Unobserved components model

URI

http://hdl.handle.net/1885/74628

Collections

ANU Research Publications

Source

Journal of Econometrics

Type

Journal article

DOI

10.1016/j.jeconom.2013.05.003

Restricted until

2037-12-31

Downloads

File

Description

01_Chan_Moving_average_stochastic_2013.pdf (564.61 KB)