Fast ML estimation of dynamic bifactor models: an application to European inflation
Gabriele Fiorentini (),
Alessandro Galesi () and
Enrique Sentana ()
No 1525, Working Papers from Banco de España
We generalise the spectral EM algorithm for dynamic factor models in Fiorentini, Galesi and Sentana (2014) to bifactor models with pervasive global factors complemented by regional ones. We exploit the sparsity of the loading matrices so that researchers can estimate those models by maximum likelihood with numerous series from multiple regions. We also derive convenient expressions for the spectral scores and information matrix, which allows us to switch to the scoring algorithm near the optimum. We explore the ability of a model with one global factor and three regional factors to capture inflation dynamics across 25 European countries in the period 1999-2014.
Keywords: euro area; inflation convergence; spectral maximum likelihood; Wiener-Kolmogorov filter. (search for similar items in EconPapers)
JEL-codes: C32 C38 E37 F45 (search for similar items in EconPapers)
Pages: 64 pages
New Economics Papers: this item is included in nep-mac
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Chapter: Fast ML Estimation of Dynamic Bifactor Models: An Application to European Inflation (2016)
Working Paper: Fast ML Estimation of Dynamic Bifactor Models: An Application to European Inflation (2015)
Working Paper: Fast ML estimation of dynamic bifactor models: an application to European inflation (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:1525
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