By the same authors

Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Published copy (DOI)



Publication details

Title of host publicationThe Festschrift in Honor of Peter Schmidt.
DateAccepted/In press - 2013
DatePublished (current) - 2014
Number of pages34
Place of PublicationNew York
EditorsWilliam Horrace, Robin Sickles
Original languageEnglish
ISBN (Electronic)9781489980083
ISBN (Print)9781489980076


This paper develops a cointegrating nonlinear autoregressive distributed lag (NARDL) model in which short- and long-run nonlinearities are introduced via positive and negative partial sum decompositions of the explanatory variables. We demonstrate that the model is estimable by OLS and that reliable long-run inference can be achieved by bounds-testing regardless of the integration orders of the variables. Furthermore, we derive asymmetric dynamic multipliers that graphically depict the traverse between the short- and the long-run. The salient features of the model are illustrated using the example of the nonlinear unemployment-output relationship in the US, Canada and Japan.

    Research areas

  • Asymmetric Cointegrating Relationships, Asymmetric Dynamic Multipliers, Nonlinear ARDL (NARDL) ECM-based Estimation and Tests, Nonlinear Unemployment-Output Relationship.

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