The equation is one year ahead stock returns as a function of a few explanatory variables.

Many thanks for considering my request, DEEP.

- Thread starter Deepn6
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The equation is one year ahead stock returns as a function of a few explanatory variables.

Many thanks for considering my request, DEEP.

More specifically, \(R^{2} = r'_{x,y} \beta\) where \(r_{x,y}\) is the p X 1 vector of correlations of the predictors and the dependent variable and \(\mathbf{\beta}\) is the vector of regression coefficients. If the regression coeffcients are biased,\(R^{2}\) is most likely going to be biased as well.

More specifically, \(R^{2} = r'_{x,y} \beta\) where \(r_{x,y}\) is the p X 1 vector of correlations of the predictors and the dependent variable and \(\mathbf{\beta}\) is the vector of regression coefficients. If the regression coeffcients are biased,\(R^{2}\) is most likely going to be biased as well.

But only the endogenous explanatory variable is biased. Is it possible that this biases another variable the other way, leaving the R squared roughly unaffected?

However, the key point is that we don't always know that for sure, right? So it could or could not be a problem, but there is no way of knowing this unless you both have population-level data **and** the necessary instruments to remove endogeneity and then assess where the biases are coming from and in which direction they are moving.