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BTtest
View on CRAN: Click
here
Download and install BTtest package within the R console
Install from CRAN:
install.packages("BTtest")
Install from Github:
library("remotes")
install_github("cran/BTtest")
Install by package version:
library("remotes")
install_version("BTtest", "0.10.3")
Attach the package and use:
library("BTtest")
Maintained by
Paul Haimerl
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.BTtest . https://github.com/Paul-Haimerl/BTtest/issues . https://github.com/Paul-Haimerl/BTtest . BTtest results . BTtest.pdf . BTtest_0.10.3.tar.gz . BTtest_0.10.3.zip . BTtest_0.10.3.zip . BTtest_0.10.3.zip . BTtest_0.10.3.tgz . BTtest_0.10.3.tgz . BTtest_0.10.3.tgz . BTtest_0.10.3.tgz . BTtest_0.10.3.tgz . BTtest_0.10.3.tgz . BTtest archive . https://CRAN.R-project.org/package=BTtest .
First Published: 2023-12-10
Latest Update: 2024-01-11
Description:
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) 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) that provide a complementary measure for the number of factors.
How to cite:
Paul Haimerl (2023). BTtest: Estimate the Number of Factors in Large Nonstationary Datasets. R package version 0.10.3, https://cran.r-project.org/web/packages/BTtest. Accessed 21 Feb. 2025.
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