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Interpreting Regression Analysis Output from EViews

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I need to be able to critically assess a regression analysis printout from EViews (sample attached) and be able to identify possible issues - i.e.:
- potential heteroskedasticity
- potential autocorrelation
- potential multicollinearity problem
prior to running the specific tool which provides further analysis for one of these 3 issues. Also, assessing the f-statistic and obs*r-squared from one of these 3 tests to confirm or reject the existence of a problem.

Knowing what values in what fields will indicate statistical significance is essentially what I need.

NOTE: The attached file is a sample printout (the data is not correct) - I need to know 'in general' what to look out for in a printout.

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Solution Preview

I think what you are generally asking is how to interpret output from a hypothesis test whether it is for heteroskedasticity, autocorrelation, or multicollinearity. When you run a hypothesis test the null is that the condition doesn't exist or that there is no heteroskedasticity, autocorrelation, or multicollinearity. The test statistic is used to try to reject ...

Solution Summary

The solution provided is a 200-250 word general explanation of how to interpret whether output from a hypothesis test is for heteroskedasticity, autocorrelation, or multicollinearity and three .pdf attachments which got into more depth on the topic.

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Interpreting integrity tests data on a regression analysis

I need someone to critically assess the relative merits/weaknesses of a economic modeling equation and the subsequent integrity tests performed on the 40 year time series data. Attached is a word document with screenshots of various EViews results/tests/diagrams, etc and an excel file with the associated data. Also attached is the EViews file I am using to analyze the data.

A brief comment on each screenshot, what it means, raise red flags (and yellow flags) of flaws in the data, etc.....and if heteroskedasticity, multicollinearity, autocorrelation is present how specifically to compensate for it.

Section 1 of the word doc uses the basic equation and Section 2 looks at the same equation with AR(1) AR(2) autoregression added.

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