Autoregressive Distributed Lag (ARDL) models are linear time series models (Pesaran, 1998 and 2001) in which the dependent and independent variables are related contemporaneously and across historical (lagged) values.

EViews offers powerful time-saving tools for estimating and examining the properties of Autoregressive Distributed Lag (ARDL) models. ARDLs are standard least squares regressions that include lags of both the dependent variable and explanatory variables as regressors (Greene, 2008).

Although ARDL models have been used in econometrics for decades, they have gained popularity in recent years as a method of examining cointegrating relationships through the work of Pesaran and Shin (PS 1998) and Pesaran, Shin and Smith (PSS 2001), and through extensions to QARDL modeling of conditional quantiles of the distributed lag process (Cho, Kim, and Shin, 2015).

While it is possible to use a standard least squares or quantile regression equation to estimate an ARDL, the specialized ARDL estimator in EViews offers a number of useful features including model selection and the computation of post-estimation diagnostics.