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Trying to use a linear regression model to forcast the CPI. I noticed that when I took a moving average of the residuals, though homsokedatisc and nonautocorrelated(ie they squiggle up&down with no uniform pattern), that they seemed to move in the same direction as the CPI. That is the moving average of the residuals and the depended variables correlated. What does this imply? Is this a case of ommitted variable bias? Are my coefficiants biased? What are some common prognansis for a such a problem?

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Residuals of what kind of model? And were you using seasonally adjusted CPI? –  John Mar 29 '13 at 17:03
    
Hi John, An arimax model. So AR terms and some structural/fundamental variables that I think drive the CPI. –  jessica Mar 29 '13 at 17:14
    
of course it means your model is not good, average of residuals must be zero. can you please tell us at least what your model looks like? give us equation –  bits_international Mar 29 '13 at 18:18

1 Answer 1

The reason you're seeing the bias is because of the adjustment, commonly referred to the "fudge factor" that they government applies to basically all of their published statistics. This is almost always in the direction to make the numbers rosier, and therefore your error term likely will exhibit signs of positive serial correlation.

CPI_ShadowStats

The standard test for first-order serially correlated errors is the Durbin-Watson test statistic. For higher-order testing of serial correlation you can use the Breush-Godfrey test.

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