st: predicting consumption

[gemini mtei <gjmt_99@yahoo.com>]

I am trying to predict household total consumption from the national household budget survey to a small survey that we conducted but didn't collect consumption. I have used a linear model (OLS) as follow,

log(consumption)= B0 +B1wealth+B2log(household size) + B3wealth*log(household size) +B4wealth*location, where

wealth is measured by asset index constructed from ownership of assets, housing characteristics, source of utilities, and household head specific characteristics (i.e. education and employment). Location captures urban-rural differences. 

The model is giving me R-square of .55 and i have done all diagnostic tests and it seems fine. I have used the split half method for validation of the predicted consumption but (i.e. selecting a random sample from the households survey, run consumption model and predict into the remaining sample then compare with actual consumption) the problem i am facing is the model over predicts consumption for the households with low consumption while it under predict for households with higher consumption. 

I need the predicted consumption for the analysis of out of pocket financing incidence in the small survey i mentioned above. These survey had small difference in their implimentation time and the assumption i am putting is that since the household budget survey is nationally representative i can use it to predict consumption into this small survey.  Can you advise whether i am making mistake in model specification? Is there a special case in predicting with interactions?

Your advice will be of help, 

Thanks, Gemini.



      

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