qardlar — NOTICE
================

This product includes software derived from the GAUSS QARDL library,
version 3.1.1, developed and published by Aptech Systems, Inc.

Upstream project
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    Name       : GAUSS QARDL library
    Version    : 3.1.1
    Author     : Eric Clower
    Publisher  : Aptech Systems, Inc.
    URL        : https://github.com/aptech/gauss-qardl
    Licence    : MIT

Upstream licence text (reproduced in full as required by the MIT Licence)
-------------------------------------------------------------------------

MIT License

Copyright (c) 2026 Aptech Systems, Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Relationship of this port to the upstream library
--------------------------------------------------
`qardlar` re-implements the algorithms of the GAUSS QARDL library in Python.
No GAUSS source code is redistributed in this package. The GAUSS procedure
names, argument orders, default values, output-structure field names and
numerical conventions are deliberately mirrored so that researchers can move
between the two implementations; the mapping is documented in
`docs/GAUSS_PYTHON_COMPATIBILITY.md`.

Underlying econometric methodology is due to the authors cited in
`docs/REFERENCES.md` and in the docstrings of the relevant functions,
principally:

  * 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. <doi:10.1016/j.jeconom.2015.05.003>

  * Pesaran, M. H., Shin, Y. and Smith, R. J. (2001), "Bounds testing
    approaches to the analysis of level relationships", Journal of Applied
    Econometrics 16(3), 289-326. <doi:10.1002/jae.616>

  * Shin, Y., Yu, B. and Greenwood-Nimmo, M. (2014), "Modelling asymmetric
    cointegration and dynamic multipliers in a nonlinear ARDL framework",
    in Festschrift in Honor of Peter Schmidt, Springer, 281-314.
    <doi:10.1007/978-1-4899-8008-3_9>

  * Chudik, A. and Pesaran, M. H. (2015), "Common correlated effects
    estimation of heterogeneous dynamic panel data models with weakly
    exogenous regressors", Journal of Econometrics 188(2), 393-420.
    <doi:10.1016/j.jeconom.2015.03.007>

Neither Aptech Systems, Inc. nor the methodology authors endorse this port.
