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:	0.10.3
Depends:	R (≥ 3.5.0)
Imports:	Rcpp
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	testthat (≥ 3.0.0)
Published:	2024-08-28
DOI:	10.32614/CRAN.package.BTtest
Author:	Paul Haimerl [aut, cre]
Maintainer:	Paul Haimerl <p.haimerl at student.maastrichtuniversity.nl>
BugReports:	https://github.com/Paul-Haimerl/BTtest/issues
License:	GPL (≥ 3)
URL:	https://github.com/Paul-Haimerl/BTtest
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	BTtest results

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