ardldml: Bounds Testing for Cointegration with Many Persistent Controls
An implementation of the DML-Bounds procedure of Villena (2026)
<doi:10.2139/ssrn.6472826> for testing cointegration in data-rich
time-series settings. The Autoregressive Distributed Lag (ARDL) bounds test
of Pesaran, Shin and Smith (2001) <doi:10.1002/jae.616> avoids pretesting
the integration order of the regressors but is not designed for a
high-dimensional conditioning set. Residualising the lagged levels against
persistent controls can absorb stochastic trends and thereby change the
finite-sample null distribution, so what governs the null is the effective
number of stochastic trends surviving residualisation rather than the
integration order of the original regressors. The procedure combines
h-block cross-fitting, a balanced nuisance projection in the Double Machine
Learning (DML) style of Chernozhukov and others (2018)
<doi:10.1111/ectj.12097>, adaptive weighting after Zou (2006)
<doi:10.1198/016214506000000735>, and a restricted system wild bootstrap
that regenerates the dependent variable and the focal regressor jointly.
No critical-value table is shipped: the classical bracket is regenerated by
simulation and the operational critical value is bootstrapped. A
trend-absorption diagnostic and a penalty-sensitivity sweep report whether a
verdict survives a change of conditioning set. Monthly United States
macroeconomic series from the 'FRED-MD' database of McCracken and Ng (2016)
<doi:10.1080/07350015.2015.1086655> are bundled so every example runs
offline.
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