Title: Bootstrapping the ARDL Tests for Cointegration
Author: Gianmarco Vacca, Stefano Bertelli
Description: The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - <doi:10.1002/jae.616>) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.
Imports: Rcpp, pracma, aod, ARDL, dynamac, vars, gtools, dplyr,
Packaged: 2022-09-27 09:37:48 UTC; gianmarco.vacca

Packaged: 2022-07-18 07:33:51 UTC; gianmarco.vacca

Title: Combine Parameter Estimates via Parametric Bootstrap
Authors@R: person("Marc", "Henrion", email = "mhenrion@mlw.mw", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1242-839X"))
Description: Propagate uncertainty from several estimates when combining these estimates via a function.
This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter.
Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage.
References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.
Author: Marc Henrion [aut, cre] (<https://orcid.org/0000-0003-1242-839X>)

Gaussian copulas are used for when parameters are assumed to be dependent / correlated.

Author: Daniel Gerlanc <dgerlanc@enplusadvisors.com> and Kris Kirby
Description: Functions for calculating bootstrap effect sizes

Title: Bootstrap Confidence Intervals on Effect Sizes
Description: Calculate robust measures of effect sizes using the bootstrap.