Tools to download and merge data files on sub-national conflict, violence and protests from <http://www.x-sub.org>.

Implements the bootstrap slope heterogeneity test for panel data 
    based on Blomquist and Westerlund (2015) <doi:10.1007/s00181-015-0978-z>.
    Tests the null hypothesis that slope coefficients are homogeneous across
    cross-sectional units. Provides both standard and adjusted Delta statistics
    with bootstrap p-values. Supports partialling out of control variables and
    cross-sectional averages for dealing with cross-sectional dependence.

Implements panel cointegration tests allowing for structural breaks
    and cross-section dependence following the methodology of Banerjee and
    Carrion-i-Silvestre (2015) <doi:10.1002/jae.2348>. The package provides
    iterative factor-break estimation, individual ADF tests on defactored
    residuals, standardized panel test statistics, and the Bai and Ng (2004)
    <doi:10.1111/j.1468-0262.2004.00528.x> MQ test for identifying common
    stochastic trends. Supports five model specifications with varying
    deterministic components and break structures.

Implements the Cross-Sectionally Augmented Panel Quantile 
    Autoregressive Distributed Lag (CS-PQARDL) model and the Quantile 
    Common Correlated Effects Mean Group (QCCEMG) estimator for panel data 
    with cross-sectional dependence. The package handles unobserved common 
    factors through cross-sectional averages following Pesaran (2006) 
    <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran (2015) 
    <doi:10.1016/j.jeconom.2015.03.007>. Quantile regression for dynamic 
    panels follows Harding, Lamarche, and Pesaran (2018) 
    <doi:10.1016/j.jeconom.2018.07.010>. The ARDL approach to cointegration 
    testing is based on Pesaran, Shin, and Smith (2001) 
    <doi:10.1002/jae.616>.

Implements the Durbin-Hausman panel cointegration tests of 
    Westerlund (2008) <doi:10.1002/jae.963>. The tests are robust to 
    cross-sectional dependence through common factor extraction using 
    principal components. Provides both group-mean (DHg) and panel (DHp) 
    test statistics with automatic factor number selection via information 
    criteria.