Package: BTtest 0.10.3
Paul Haimerl
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.
Authors:
BTtest_0.10.3.tar.gz
BTtest_0.10.3.tar.gz(r-4.5-noble)BTtest_0.10.3.tar.gz(r-4.4-noble)
BTtest_0.10.3.tgz(r-4.4-emscripten)BTtest_0.10.3.tgz(r-4.3-emscripten)
BTtest.pdf |BTtest.html✨
BTtest/json (API)
NEWS
| # Install 'BTtest' in R: |
| install.packages('BTtest', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/paul-haimerl/bttest/issues
Last updated 3 months agofrom:3c37ebecb4. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 27 2024 |
| R-4.5-linux-x86_64 | OK | Nov 27 2024 |
Dependencies:RcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bai (2004) IPC | BaiIPC |
| Barigozzi & Trapani (2022) Test | BTtest |
| Simulate a Nonstationary Panel With Common Trends | sim_DGP |