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Does the dependent variable have to be measured?
Dependent variables are the measured behaviors of participants. They are dependent because they “depend on” what the participants do.
What is an ARDL model?
An autoregressive distributed lag (ARDL) model is an ordinary least square (OLS) based model which is applicable for both non-stationary time series as well as for times series with mixed order of integration.
What is the purpose of Ardl model?
The ARDL / EC model is useful for forecasting and to disentangle long-run relationships from short-run dynamics. Long-run relationship: Some time series are bound together due to equilibrium forces even though the individual time series might move considerably.
Which is an advantage of the ARDL model?
The advantage of ARDL that order of integration does not matter is refereed in the case of independent variables. Autoregressive distributed lag models (ARDL) do not require nonstationary data at all. Note that AR (p) models are often stationary.
When to use or not to use ARDL?
To use or not to use an estimation method (like ARDL) should not depend on the data, but mainly on the model (specification) one thinks is best for ananlysing (economic) relations. I have the impression that ARDL is often chosen because it is modern and one has a comfortable econometric package.
When to use ARDL in a cointegration?
ARDL is applicable for variables with a mixture of integration of order 1 and 0. Whether DV with integration of order 0 (stationary) will affect the results will need to relook at the whole idea of doing a cointegration using ARDL.
When to use dynamic ordinary least squares ( ARDL )?
In many cases, ARDL may be unnessesary, at all. If integrated of order 2, you might as well try Dynamic Ordinary Least Squares (DOLS) for cointegration. ARDL is applicable for variables with a mixture of integration of order 1 and 0.