ardl: Estimating autoregressive distributed lag and equilibrium correction models

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

Stata Journal, 2023, vol. 23, issue 4, 983-1019

Abstract: We present a command, ardl, for the estimation of autoregressive distributed lag (ARDL) models in a time-series context. The ardl command can be used to fit an ARDL model with the optimal number of autoregressive and distributed lags based on the Akaike or Bayesian (Schwarz) 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 (cointe- grating) relationship. The popular bounds-testing procedure for the existence of a long-run levels relationship is implemented as a postestimation feature. Compre- hensive critical values and approximate p-values obtained from response-surface regressions facilitate statistical inference.

Keywords: ardl; ardl postestimation; autoregressive distributed lag model; error-correction model; bounds test; long-run relationship; cointegration; time-series data (search for similar items in EconPapers)
Date: 2023
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Related works:
Working Paper: ardl: Estimating autoregressive distributed lag and equilibrium correction models (2022)
Working Paper: ardl: Estimating autoregressive distributed lag and equilibrium correction models (2018)
Working Paper: ardl: Stata module to estimate autoregressive distributed lag models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:23:y:2023:i:4:p:983-1019

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DOI: 10.1177/1536867X231212434

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