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ardl: Estimating autoregressive distributed lag and equilibrium correction models

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Kripfganz, Sebastian, Schneider, Daniel C.·2023·The Stata Journal: Promoting communications on statistics and Stata

Abstract

We present a command, ardl, for the estimation of autoregressive distributed lag (ARDL) models in a time-series context. The ardl command can be used to fit an ARDL model with the optimal number of autoregressive and distributed lags based on the Akaike or Bayesian (Schwarz) information criterion. The regression results can be displayed in the ARDL levels form or in the error-correction representation of the model. The latter separates long-run and short-run effects and is available in two different parameterizations of the long-run (cointegrating) relationship. The popular bounds-testing procedure for the existence of a long-run levels relationship is implemented as a postestimation feature. Comprehensive critical values and approximate p-values obtained from response-surface regressions facilitate statistical inference.

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Metadata

Authors
Kripfganz, S., & Schneider, D. C.
Venue
The Stata Journal: Promoting communications on statistics and Stata
Published
2023-12
Volume
23
Issue
4
Pages
983-1019
ISSN
  • 1536-867X (ISSN-L)
  • 1536-8734
OpenAlex
W2904812166
Cited by
299
References
53
FWCI
48.42
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: ardl: Estimating autoregressive distributed lag and equilibrium correction models. https://app.cochat.ai/papers/10.1177/1536867x231212434
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Kripfganz, S., & Schneider, D. C. (2023). ardl: Estimating autoregressive distributed lag and equilibrium correction models. The Stata Journal: Promoting communications on statistics and Stata. https://doi.org/10.1177/1536867x231212434

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.