Friday, May 19, 2017

The EViews Blog on ARDL - Part 3

As I mentioned in this recent post, the EViews team had a third blog post on ARDL modelling up their sleeves. The said post appeared a few days ago, here.

It's a real gem! The flow-chart and the detailed application are fabulous - I wish I could have come up with this myself.

Read it, read it................

© 2017, David E. Giles

5 comments:

  1. Dear Prof Dave, may I seek your kind advise on ARDL which is a better model in your view when the overall model for both options are statistically significant (pvalue<0.05):
    Opt 1 - less number of regressors, with longer lags
    Opt 2 - more regressors, with shorter lags

    Is there anything I need to be caution of? Many thanks.

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    1. That's an interesting question - and it's one that could be asked of any regression model, really, not just an ARDL model. The over-riding consideration will be to avoid under-specifying the model, because this would have serious consequences for estimator properties, etc. If certain variables are significant, why not include them - even if this means shorter lags? Presumably you think that these extra variables are relevant, on theoretical grounds, in the first place? The lag structure is going to alter the dynamics of the model, and this may be important if you are going to use the model for forecasting etc. On balance, without knowing the exact context here, I'd be inclined to go for Option 2. What do you need to be careful about? Probably the most important thing in a model of this type will be possible autocorrelation in the error term. watch out for that. And in the end it may be that you need extra lags to deal with it -in that case, you may be pushed back towards Option 1.

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  2. Prof Dave, a million thanks for your insightful advise to select the one with more practical implications by including the variables. I was doing a research related to innovation in developing countries. So was at crossroad when came across this result. Thank you so much again, I will be careful on the issue of autocorrelation.

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  3. Dear Prof Dave, I used the Option 2 with more practical implications variables and shorter lags, but more CointEq(-1) is smaller than -1 i.e. -1.12, although negative and is significant (p-value 0.0002). I knew smaller than -1 is not good as speed of adjustment seems too fast? Prof, is there any good advise you may give me whether I can adopt this result, or should I drop one variable instead?

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    1. This is really up to you. Sorry, but I can't advise you in such detail out of context. I hope that you understand.

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