The qardlr package implements the Quantile Autoregressive Distributed Lag (QARDL) model of Cho, Kim and Shin (2015). It provides tools for estimating quantile-specific long-run equilibrium relationships and short-run dynamics.
qardl: Estimate QARDL model
qardl_wald: Wald tests for parameter constancy
qardl_rolling: Rolling window QARDL estimation
qardl_simulate: Monte Carlo simulation
qardl_table: Publication-ready tables
qardl_bic_select: BIC-based lag selection
Quantile regression across multiple tau values
BIC-based automatic lag selection (p, q)
Error Correction Model (ECM) parameterization
Long-run (beta), short-run AR (phi), and impact (gamma) parameters
Wald tests for parameter constancy across quantiles
Rolling/recursive QARDL estimation
Monte Carlo simulation for finite-sample properties
Publication-ready output tables (text, LaTeX, HTML)
Maintainer: Muhammad Alkhalaf muhammedalkhalaf@gmail.com (ORCID)
Other contributors:
Merwan Roudane (Original Stata implementation) [contributor]
Jin Seo Cho (Original methodology) [contributor]
Tae-Hwan Kim (Original methodology) [contributor]
Yongcheol Shin (Original methodology) [contributor]
Cho, J.S., Kim, T.-H., and Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics, 188(1), 281-300. tools:::Rd_expr_doi("10.1016/j.jeconom.2015.01.003")