Hello,
In order to make my data stationary, I had to take the second difference of the natural log of my data. Ex: logy=dlog(y,2).
I used this transformed data to produce a forecast and get values in those terms. I wish to compare my forecasted values to the original values for y in my dataset, so my question is how do I reverse the second order differencing in order to get my forecasted values back into the same terms as my actual values?
Any help would be appreciated.
Thank you!
Second Order Differencing
Moderators: EViews Gareth, EViews Moderator
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- Fe ddaethom, fe welon, fe amcangyfrifon
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Re: Second Order Differencing
Use dlog(y,2) as your dependent variable.
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Re: Second Order Differencing
Yes so the regression that I used for forecasting was with dlog(y,2) as my dependent variable so my forecasted values were in those terms. My original "y" dataset was an index in the 100s so is it possible to transform the forecasted values back in terms of the index in the 100s? I want to compare my forecasted index value with the actual index value if possible.
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- Fe ddaethom, fe welon, fe amcangyfrifon
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Re: Second Order Differencing
When you use dlog(y,2) as the dependent variable, EViews will forecast y not dlog(y,2)
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Re: Second Order Differencing
Oh okay I see now. That helps, thank you!
One more thing, when you select the "static" option for a forecast, is that recursive or rolling window? I couldn't find the specification in the Eviews guide.
One more thing, when you select the "static" option for a forecast, is that recursive or rolling window? I couldn't find the specification in the Eviews guide.
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- Fe ddaethom, fe welon, fe amcangyfrifon
- Posts: 13319
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Re: Second Order Differencing
Hi Gareth,
As you mentioned static forecast means 1 step ahead. What about 4 step ahead forecast?
thanks,
Saakshi
As you mentioned static forecast means 1 step ahead. What about 4 step ahead forecast?
thanks,
Saakshi
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- EViews Developer
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Re: Second Order Differencing
Forecast of data at t+4 given information at t.
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