The xtpqardl package provides functions for estimating Panel Quantile Autoregressive Distributed Lag (PQARDL) models. It combines the panel ARDL methodology of Pesaran, Shin, and Smith (1999) with quantile regression to allow for heterogeneous effects across the conditional distribution of the response variable.
xtpqardl: Estimate PQARDL model
summary.xtpqardl: Detailed results summary
wald_test: Test parameter equality across quantiles
compute_irf: Compute impulse response function
Merwan Roudane merwanroudane920@gmail.com
The main function is xtpqardl, which estimates PQARDL models
using Pooled Mean Group (PMG), Mean Group (MG), or Dynamic Fixed Effects
(DFE) estimators. Key features include:
Estimation at multiple quantiles simultaneously
Long-run cointegrating parameter estimation
Error correction term (ECT) speed of adjustment
Half-life of adjustment computation
Wald tests for parameter equality across quantiles
Impulse response function computation
Automatic lag selection using BIC or AIC
Pesaran MH, Shin Y, Smith RP (1999). "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels." Journal of the American Statistical Association, 94(446), 621-634. tools:::Rd_expr_doi("10.1080/01621459.1999.10474156")
Cho JS, Kim TH, 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.2015.02.030")
Bildirici M, Kayikci F (2022). "Uncertainty, Renewable Energy, and CO2 Emissions in Top Renewable Energy Countries: A Panel Quantile Regression Approach." Energy, 247, 124303. tools:::Rd_expr_doi("10.1016/j.energy.2022.124303")
Koenker R, Bassett G (1978). "Regression Quantiles." Econometrica, 46(1), 33-50. tools:::Rd_expr_doi("10.2307/1913643")