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Investigating Omitted Variable Bias In Regression Parameter Estimation: A Genetic Algorithm Approach, Lonnie K. Stevans, David N. Sessions

Lonnie K. Stevans

Bias in regression estimates resulting from the omission of a correlated relevant variable is a well known phenomenon. In this study, we apply a genetic algorithm to estimate the missing variable and, using that estimated variable, demonstrate that significant bias in regression estimates can be substantially corrected with relatively high confidence in effective models. Our interest is restricted to the case of a missing binary indicator variable and the analytical properties of bias and MSE dominance of the resulting dependent error generated vector process. These findings are compared to prior results for the independent error proxy process. Simulations are run …