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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
Visiting Associate Professor, Graduate School of Economics and Management, Tohoku University

Daniel C. Schneider

Max Planck Institute for Demographic Research, Rostock, Germany

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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

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