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ardl: Stata module to estimate autoregressive distributed lag models

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  • Sebastian Kripfganz

    () (University of Exeter Business School, Department of Economics)

  • Daniel C. Schneider

    (Max Planck Institute for Demographic Research)

Abstract

We present a new Stata package for the estimation of autoregressive distributed lag (ARDL) models in a time-series context. The ardl command can be used to estimate an ARDL model with the optimal number of autoregressive and distributed lags based on the Akaike or Schwarz/Bayesian 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 bounds testing procedure for the existence of a long-run levels relationship suggested by Pesaran, Shin, and Smith (2001, Journal of Applied Econometrics) is implemented as a postestimation feature. As an alternative to their asymptotic critical values, the small-sample critical values provided by Narayan (2005, Applied Economics) are available as well.

Suggested Citation

  • Sebastian Kripfganz & Daniel C. Schneider, 2016. "ardl: Stata module to estimate autoregressive distributed lag models," 2016 Stata Conference 18, Stata Users Group.
  • Handle: RePEc:boc:scon16:18
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    File URL: http://fmwww.bc.edu/repec/chic2016/chicago16_kripfganz.pdf
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    Cited by:

    1. Adeleye, Ngozi & Osabuohien, Evans & Bowale, Ebenezer & Matthew, Oluwatoyin & Oduntan, Emmanuel, 2017. "Financial reforms and credit growth in Nigeria: Empirical insights from ARDL and ECM techniques," MPRA Paper 85351, University Library of Munich, Germany.

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