A Coincident Index, Common Factors, and Monthly Real Gdp
Publication Type
Journal Article
Publication Date
2-2010
Abstract
The Stock–Watson coincident index and its subsequent extensions assume a static linear one-factor model for the component indicators. This restrictive assumption is unnecessary if one defines a coincident index as an estimate of monthly real gross domestic products (GDP). This paper estimates Gaussian vector autoregression (VAR) and factor models for latent monthly real GDP and other coincident indicators using the observable mixed-frequency series. For maximum likelihood estimation of a VAR model, the expectation-maximization (EM) algorithm helps in finding a good starting value for a quasi-Newton method. The smoothed estimate of latent monthly real GDP is a natural extension of the Stock–Watson coincident index.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Oxford Bulletin of Economics and Statistics
Volume
72
Issue
1
First Page
27
Last Page
46
ISSN
0305-9049
Identifier
10.1111/j.1468-0084.2009.00567.x
Citation
Mariano, Roberto S. and Murasawa, Yasutomo.
A Coincident Index, Common Factors, and Monthly Real Gdp. (2010). Oxford Bulletin of Economics and Statistics. 72, (1), 27-46.
Available at: https://ink.library.smu.edu.sg/soe_research/293