BTtest: Estimate the Number of Factors in Large Nonstationary Datasets

Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.

Version: 0.10.1
Imports: Rcpp
LinkingTo: Rcpp, RcppArmadillo
Published: 2024-01-11
Author: Paul Haimerl ORCID iD [aut, cre]
Maintainer: Paul Haimerl <paul.haimerl at maastrichtuniversity.nl>
BugReports: https://github.com/Paul-Haimerl/BTtest/issues
License: MIT + file LICENSE
URL: https://github.com/Paul-Haimerl/BTtest
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: BTtest results

Documentation:

Reference manual: BTtest.pdf

Downloads:

Package source: BTtest_0.10.1.tar.gz
Windows binaries: r-devel: BTtest_0.10.1.zip, r-release: BTtest_0.10.1.zip, r-oldrel: BTtest_0.10.1.zip
macOS binaries: r-release (arm64): BTtest_0.10.1.tgz, r-oldrel (arm64): BTtest_0.10.1.tgz, r-release (x86_64): BTtest_0.10.1.tgz
Old sources: BTtest archive

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