R: Linear and Nonlinear Autoregressive Distributed Lag Models: General-to-Specific Approach

# Linear and Nonlinear Autoregressive Distributed Lag Models: General-to-Specific Approach

## Documentation for package ‘ardl.nardl’ version 1.3.0

- DESCRIPTION file
- Package NEWS

## Help Pages

ardl.nardl-package | Linear and Nonlinear Autoregressive Distributed Lag Models: General-to-Specific Approach
ArchTest | Import from package 'nardl'
ardl.nardl | Linear and Nonlinear Autoregressive Distributed Lag Models: General-to-Specific Approach
ardl_uecm | Estimate the ARDL and ARDL Error Correction Model.
auto_case_ardl | Obtain the best ARDL model specification and bounds test.
cumsq | Import from package 'nardl'
cusum | Import from package 'nardl'
dynamac_pkg_bounds_test | A function in 'dynamac' package to Perform Pesaran, Shin, and Smith (2001) cointegration test
expectation | Inflation Expectation Dataset
fuel_price | Time series data from 2000M01 through 2021M03
gets_ardl_uecm | General-to-specific approach for the autoregressive distributed lag model
gets_nardl_uecm | Parsimonious NARDL model
lagm | Lag a matrix
nardl_auto_case | Obtain the best NARDL model specification and bounds test.
nardl_mdv | A NARDL model with two decomposed variables
nardl_uecm | Estimate the nonlinear ARDL (NARDL) Error Correction Model
nardl_uecm_sym | Short-run symmetry restrictions (SRSR) and the longrun symmetry restrictions (LRSR).
output_ren | Rename the coefficient of the nardl_uecm summary output
ssa | data on health expenditure, per capita income and life expectancy in Sub-Saharan countries
syg_data | Time series data on output and unemployment in Canada, Japan and United States from 1981M01 to 2022M07