TUPD-2022-006
TITLE | ardl: Estimating autoregressive distributed lag
and equilibrium correction models
|
AUTHORS | Sebastian Kripfganz
Lecturer (assistant professor) in Economics, University of Exeter Business School, Exeter, United Kingdom
Max Planck Institute for Demographic Research, Rostock, Germany |
P D F | |
ABSTRACT | We present a 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 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. |
KEYWORDS | Autoregressive distributed lag model, Error-correction model, Bounds test, Long-run relationship, Cointegration, Time-series data |
ISSUED | April 2022 |