ardl: Stata module to estimate autoregressive distributed lag models

Sebastian Kripfganz and Daniel C. Schneider
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Daniel C. Schneider: Max Planck Institute for Demographic Research

2016 Stata Conference from Stata Users Group

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.

Date: 2016-08-10
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Citations: View citations in EconPapers (34)

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http://fmwww.bc.edu/repec/chic2016/chicago16_kripfganz.pdf

Related works:
Working Paper: ardl: Estimating autoregressive distributed lag and equilibrium correction models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:scon16:18

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