Deflationary Dynamics in Hong Kong: Evidence from Linear and Neural Network Regime Switching Models

HKIMR Working Paper No. 21/2004

21 Pages Posted: 23 Aug 2007

See all articles by Paul D. McNelis

Paul D. McNelis

Georgetown University - Department of Economics

Date Written: November 2004

Abstract

This paper examines Deflationary dynamics in Hong Kong with a linear and a nonlinear neural-network regime-switching (NNRS) model. The NNRS model is superior to the linear model in terms of in-sample specification tests as well as out-of-sample forecasting accuracy. As befitting a small and highly open economy, the most important variables affecting inflation and deflation turn out to be the growth rates of import prices and wealth (captured by the rates of growth of residential property prices). The NNRS model indicates that the likelihood of moving out of deflation has been steadily increasing.

Keywords: deflation, neural networks, regime-switching models

JEL Classification: E0, E3, E5

Suggested Citation

McNelis, Paul D., Deflationary Dynamics in Hong Kong: Evidence from Linear and Neural Network Regime Switching Models (November 2004). HKIMR Working Paper No. 21/2004, Available at SSRN: https://ssrn.com/abstract=1009038 or http://dx.doi.org/10.2139/ssrn.1009038

Paul D. McNelis (Contact Author)

Georgetown University - Department of Economics ( email )

Washington, DC 20057
United States
202-687 5573 (Phone)
202-687 6102 (Fax)

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