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Model Selection Criteria for Factor‐Augmented Regressions *
Author(s) -
Groen Jan J. J.,
Kapetanios George
Publication year - 2013
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.2012.00721.x
Subject(s) - selection (genetic algorithm) , regression , econometrics , model selection , regression analysis , dimension (graph theory) , estimation , factor analysis , statistics , factor (programming language) , computer science , mathematics , machine learning , economics , management , pure mathematics , programming language
Existing dynamic factor selection criteria determine the appropriate number of factors in a large‐dimensional panel of explanatory variables, but not all of these have to be relevant for modeling a specific dependent variable within a factor‐augmented regression. We develop theoretical conditions that selection criteria have to meet in order to get consistent estimates of the relevant factor dimension for such a regression. These incorporate factor estimation error and do not depend on specific factor estimation methodologies. Using this framework, we modify standard model selection criteria, and simulation and empirical applications indicate that these are useful in determining appropriate factor‐augmented regressions.