Working Paper: CEPR ID: DP10461
Authors: Gabriele Fiorentini; Alessandro Galesi; Enrique Sentana
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
JEL Codes: C32; C38; E37; F45
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| global factor (F29) | inflation rates (E31) |
| regional factors (R11) | inflation rates (E31) |
| inflation rates (E31) | inflation convergence (E31) |
| regional factors (R11) | inflation rates (specific subsets) (E31) |
| inflation rates (same region) (E31) | inflation rates (non-regional countries) (E31) |