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ardl: Estimating autoregressive distributed lag and equilibrium correction models

Author

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  • Sebastian Kripfganz

    (University of Exeter Business School)

  • Daniel Schneider

    (Max Planck Institute for Demographic Research)

Abstract

Autoregressive distributed lag (ARDL) models are often used to analyse dynamic relationships with time series data in a single-equation framework. The current value of the dependent variable is allowed to depend on its own past realisations – the autoregressive part – as well as current and past values of additional explanatory variables – the distributed lag part. The variables can be stationary, nonstationary, or a mixture of the two types. In its equilibrium correction (EC) representation, the ARDL model can be used to separate the long-run and short-run effects, and to test for cointegration or, more generally, for the existence of a long-run relationship among the variables of interest. This talk serves as a tutorial for the ardl Stata command that can be used to estimate an ARDL or EC model with the optimal number of lags based on the Akaike or Schwarz/Bayesian information criteria. Frequently asked questions will be addressed and a step-by-step instruction for the Pesaran, Shin, and Smith (2001 Journal of Applied Econometrics) bounds test for the existence of a long-run relationship will be provided. This test is implemented as the postestimation command estat ectest which features newly computed finite-sample critical values and approximate p-values. These critical values cover a wide range of model configurations and supersede previous tabulations available in the literature. They account for the sample size, the chosen lag order, the number of explanatory variables, and the choice of unrestricted or restricted deterministic model components. The ardl command uses Stata’s regress command to estimate the model. As a consequence, specification tests can be carried out with the standard postestimation commands for linear (time series) regressions and the forecast command suite can be used to obtain dynamic forecasts.

Suggested Citation

  • Sebastian Kripfganz & Daniel Schneider, 2018. "ardl: Estimating autoregressive distributed lag and equilibrium correction models," London Stata Conference 2018 09, Stata Users Group.
    • Handle: RePEc:boc:usug18:09
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    References listed on IDEAS

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    1. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
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    6. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    7. Sebastian Kripfganz & Daniel C. Schneider, 2021. "Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1456-1481, December.
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    9. Sebastian Kripfganz & Daniel C. Schneider, 2020. "Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1456-1481, December.
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