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From |
"Dimitriy V. Masterov" <[email protected]> |

To |
Statalist <[email protected]> |

Subject |
st: how to evaluate predictions with balanced panel data |

Date |
Mon, 3 Jun 2013 19:52:20 -0700 |

I would like to evaluate several different models that provide predictions of behavior at a monthly level. The data is balanced, and n=100,000 and T=12. The outcome is attending a concert in a given month, so it is zero for ~80% of the people in any month, but there's a long right tail of heavy users. The predictions I have do not seem to respect the count nature of the outcome: fractional concerts are prevalent. I don't know anything about the models. I only observe 5 different black-box predictions yhat1,...,yhat5 for each person per month. I do have an extra year of data that the model builders did not have for the estimation (though the concert goers are the same), and I would like to gauge where each performs well (in terms of accuracy and precision). For instance, does some model predict well for frequent concert goers, but fails for the couch potatoes. Is the prediction for January better than the prediction for December? Alternatively, it would be nice to know the predictions allow me to rank people correctly in terms of the actuals, even if the exact magnitude cannot be trusted. My first thought was to run a fixed effects regressions of actual on predicted and time dummies and look at the RMSEs for each model. But that does not answer the question about where each model does well or if the differences are significant. The distribution of the outcome also worries me with this approach. My second idea was to bin the outcome into 0, 1-3, and 3+, and calculate the confusion matrix, but this ignores the time dimension, unless I make 12 of these. It's also pretty coarse. Previous questioners were pointed towards -concord- by N. Cox & T. Steichen--which has the by() option, but that would require collapsing the data to annual totals--and Harrel's c (calculated through -somersd- by R. Newson), which has the cluster option, but I am not sure that would allow me to deal with the panel data. How would you tackle this problem with Stata? DVM * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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