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Is Forecast Evaluation sensitive to negative values?

Posted: Wed Mar 23, 2016 9:31 am
by sunnyeviews
Dear Sir or Madam,

Is Forecast Evaluation sensitive to negative values? I am trying to run the Forecast Evaluation (simple mean, trimmed mean, simple median, least-sqaures, mean square error, and MSE ranks), but I receive an error message: positive or non-negative argument to function expected.

Yours faithfully,

Re: Is Forecast Evaluation sensitive to negative values?

Posted: Wed Mar 23, 2016 9:40 am
by EViews Gareth
Could you post your workfile?

Re: Is Forecast Evaluation sensitive to negative values?

Posted: Wed Mar 23, 2016 12:11 pm
by sunnyeviews
attached. Thank you very much. :D

Re: Is Forecast Evaluation sensitive to negative values?

Posted: Wed Mar 23, 2016 12:32 pm
by EViews Gareth
What exactly did you do?

Re: Is Forecast Evaluation sensitive to negative values?

Posted: Fri Apr 15, 2016 1:57 am
by Oladapo
Please house, I need help on how to forecast GDP data of my country, Nigeria, from 2014 to 2060. I already have data from 1960 to 2014 but I need forecast data till 2060.

Re: Is Forecast Evaluation sensitive to negative values?

Posted: Fri Apr 15, 2016 5:26 am
by trubador
Please house, I need help on how to forecast GDP data of my country, Nigeria, from 2014 to 2060. I already have data from 1960 to 2014 but I need forecast data till 2060.
You need to build a model to do that. I think you are working with annul data, which covers more than 50 years. Since you are interested in forecasting the next 50 years, you'll need a structural model. If you can find data on capital stock and labor force as well, then "growth accounting" approach would be a good place to start. I cannot think of any simple/easy alternative way other than univariate decomposing of the series through fitting a log-linear or polynomial functional form (you also can try ETS feature of EViews for that matter):

Code: Select all

equation eq.ls log(gdp) c @trend equation eq.ls log(gdp) c @trend @trend^2
Of course, you'll need to be careful about the breakpoints and structural changes in the data.