ardl: Estimating autoregressive distributed lag and equilibrium correction models

Sebastian Kripfganz

307 citations0/0 files storedThe Stata Journal Promoting communications on statistics and Stata

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Spatial and Panel Data Analysis Monetary Policy and Economic Impact Data Analysis with R

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 (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.

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References

1987Co-Integration and Error Correction: Representation, Estimation, and Testing 2001Bounds testing approaches to the analysis of level relationships 1981Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root 1979Distribution of the Estimators for Autoregressive Time Series With a Unit Root 1995Likelihood-Based Inference in Cointegrated Vector Autoregressive Models 1990Statistical Inference in Instrumental Variables Regression with I(1) Processes 2012An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis 2014Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework

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