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A parametric estimation method for dynamic factor models of large dimensions
Author(s) -
Kapetanios George,
Marcellino Massimiliano
Publication year - 2009
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2009.00607.x
Subject(s) - parametric statistics , factor (programming language) , estimation , dynamic factor , mathematics , factor analysis , set (abstract data type) , parametric model , state space , space (punctuation) , data set , econometrics , mathematical optimization , computer science , data mining , algorithm , statistics , management , economics , programming language , operating system
Abstract. The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, because of the increased availability of large data sets. In this article we propose a new parametric methodology for estimating factors from large data sets based on state–space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also conduct a set of simulation experiments that show that our approach compares well with existing alternatives.