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Fast ML Estimation of Dynamic Bifactor Models: An Application to European Inflation

Gabriele Fiorentini (), Alessandro Galesi () and Enrique Sentana ()

A chapter in Dynamic Factor Models, 2016, vol. 35, pp 215-282 from Emerald Publishing Ltd

Abstract: Abstract 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 many 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 a global factor and three regional ones to capture inflation dynamics across 25 European countries over 1999–2014.

Keywords: Euro area; inflation convergence; spectral maximum likelihood; Wiener–Kolmogorov filter; C32; C38; E37; F45 (search for similar items in EconPapers)
Date: 2016
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Related works:
Working Paper: Fast ML estimation of dynamic bifactor models: an application to European inflation (2015) Downloads
Working Paper: Fast ML Estimation of Dynamic Bifactor Models: An Application to European Inflation (2015) Downloads
Working Paper: Fast ML estimation of dynamic bifactor models: an application to European inflation (2015) Downloads
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