Real-Time Nowcasting of Nominal GDP Under Structural Breaks
William Barnett (),
Marcelle Chauvet () and
Danilo Leiva-Leon ()
Staff Working Papers from Bank of Canada
This paper provides a framework for the early assessment of current U.S. nominal GDP growth, which has been considered a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy-makers have available at the time predictions are made. However, real-time information arrives at different frequencies and asynchronously, which poses challenges of mixed frequencies, missing data and ragged edges. This paper proposes a multivariate state-space model that not only takes into account asynchronous information inflow, but also allows for potential parameter instability. We use small-scale confirmatory factor analysis in which the candidate variables are selected based on their ability to forecast nominal GDP. The model is fully estimated in one step using a non-linear Kalman filter, which is applied to obtain optimal inferences simultaneously on both the dynamic factor and parameters. In contrast to principal component analysis, the proposed factor model captures the co-movement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate specifications. The results indicate that the proposed model containing information on real economic activity, inflation, interest rates and Divisia monetary aggregates produces the most accurate real-time nowcasts of nominal GDP growth.
Keywords: Business fluctuations and cycles; Econometric and statistical methods; Inflation and prices (search for similar items in EconPapers)
JEL-codes: C32 E27 E31 E32 (search for similar items in EconPapers)
Pages: 34 pages
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:14-39
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