Accurate Confidence Regions for Principal Components Factors

Bibliographic Data

ID21512840
AuthorsJavier Maldonado (0000-0003-4481-5917, Department of Statistics Universidad Carlos III de Madrid C/Madrid 126 Getafe Madrid 28903 Spain, corresponding author), Esther Ruiz (0000-0002-5944-9449, Department of Statistics Universidad Carlos III de Madrid C/Madrid 126 Getafe Madrid 28903 Spain, corresponding author)
Year2021
Volume83
Issue6
Pages1432-1453
Publication date2021-12-01
Open AccessYes
TypeARTICLE
VenueOxford Bulletin of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0305-9049 • E-ISSN: 1468-0084
PublisherWiley (PUBLISHER • GB)
DOI10.1111/obes.12436
OpenAlexW3160642839
LanguageEN
Citations received1
References cited44

In dynamic factor models, factors are often extracted using principal components with their asymptotic confidence regions having empirical coverages below the nominal ones when the temporal dimension is small. We propose a subsampling procedure to compute the factor loadings uncertainty and correct the asymptotic covariance matrix of the extracted factors. We show that the empirical coverages of the modified confidence regions are closer to the nominal ones than those of asymptotic regions and asymptotically valid bootstrap regions. The results are empirically illustrated obtaining confidence intervals of the underlying factor in a system of Spanish macroeconomic variables

Combinatorics · Confidence interval · Confidence region · Covariance matrix · Econometrics · Factor analysis · Principal component analysis · Statistics · Financial Risk and Volatility Modeling · Mathematics · Monetary Policy and Economic Impact · Spatial and Panel Data Analysis · Applied Mathematics

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