TY - JOUR
T1 - bootUR: An R Package for Bootstrap Unit Root Tests
AU - Smeekes, Stephan
AU - Wilms, Ines
N1 - Data Source:
1) McCracken, M.W. & Ng, S. (2020). FRED-QD: A Quarterly Database for Macroeconomic Research. Version 2020-06. Retrieved from https://research.stlouisfed.org/econ/mccracken/fred-databases/
2) Eurostat (2020). Macroeconomic data on GDP, consumption, inflation and unemployment for Belgium, Germany, France, the Netherlands and the United Kingdom. Retrieved from https://ec.europa.eu/eurostat/data/database
3) Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Dataset retrieved from http://qed.econ.queensu.ca/jae/datasets/pesaran002/
PY - 2023/5/8
Y1 - 2023/5/8
N2 - Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in the R package bootUR. The package's backbone is the popular augmented Dickey-Fuller test paired with a union of rejections principle, which can be performed directly on single time series or multiple (including panel) time series. Accurate inference is ensured through the use of bootstrap methods. The package addresses the needs of both novice users, by providing user-friendly and easy-to-implement functions with sensible default options, as well as expert users, by giving full user-control to adjust the tests to one's desired settings. Our parallelized C++ implementation ensures that all unit root tests are scalable to datasets containing many time series.
AB - Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in the R package bootUR. The package's backbone is the popular augmented Dickey-Fuller test paired with a union of rejections principle, which can be performed directly on single time series or multiple (including panel) time series. Accurate inference is ensured through the use of bootstrap methods. The package addresses the needs of both novice users, by providing user-friendly and easy-to-implement functions with sensible default options, as well as expert users, by giving full user-control to adjust the tests to one's desired settings. Our parallelized C++ implementation ensures that all unit root tests are scalable to datasets containing many time series.
UR - https://www.jstatsoft.org/index.php/jss/article/view/v106i12/4483
U2 - 10.18637/jss.v106.i12
DO - 10.18637/jss.v106.i12
M3 - Article
SN - 1548-7660
VL - 106
SP - 1
EP - 39
JO - Journal of Statistical Software
JF - Journal of Statistical Software
IS - 12
ER -