The chief finding of the Soyer-Hogarth experiment is that the expert econometricians themselves?our best number crunchers?make better predictions when only graphical information?such as a scatter plot and theoretical linear regression line?is provided to them. Give them t-statistics and fits of R-squared for the same data and regression model and their forecasting ability declines. Give them only t-statistics and fits of R-squared and predictions fall from bad to worse.
It?s a finding that hits you between the eyes, or should. R-squared, the primary indicator of model fit, and t-statistic, the primary indicator of coefficient fit, are in the leading journals of economics - such as the AER, QJE, JPE, and RES - evidently doing more harm than good.This?reminds me of Art Goldberger's teaching in Econ 612.? After I took that class, he turned his class notes into a book.? From page 177:
From our perspective, R2 has a very modest role in regression analysis, being a measure of the goodness of fit of a sample of LS (least squares) linear regression in a body of data.? Nothing in the CR (classical regression) model requires R2 to be high.? Hence a high R2 is not evidence in favor of the model, and a low?R2 is not evidence against it...
...In fact, the most important thing about?R2 is that is is not important in the CR model.? The CR model is concerend with parameters in a population, not with the goodness of fit within the sample.?
I also remember Gary Chamberlain was not crazy about t-statistics--he said he didn't want to see any "damn stars" in our papers.? We should care more about confidence intervals than hypothesis tests.?
Source: http://real-estate-and-urban.blogspot.com/2012/07/mark-thoma-reminds-me-of-something-art.html
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