Sebastian Kripfganz

Lecturer (assistant professor) in Economics,
University of Exeter Business School, Department of Economics
 
© Sebastian Kripfganz
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Stata Programs
Title and Abstract
ardl: Stata module to estimate autoregressive distributed lag models
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
Kripfganz, S. and D. C. Schneider (2016). ardl: Stata module to estimate autoregressive distributed lag models. Presented July 29, 2016, at the Stata Conference, Chicago.
Authors
Sebastian Kripfganz
University of Exeter
Daniel C. Schneider
Max Planck Institute for Demographic Research
Presentation
Presentation (July 29, 2016, Stata Conference, Chicago)
Stata Program
ardl

www.kripfganz.de