In statistics, omitted-variable bias (OVB) occurs when a model is created which incorrectly leaves out one or more important causal factors. The 'bias' is created when the model compensates for the missing factor by over- or underestimating one of the other factors.
More specifically, OVB is the bias that appears in the estimates of parameters in a regression analysis, when the assumed specification is incorrect, in that it omits an independent variable (possibly non-delineated) that should be in the model.
Read more about Omitted-variable Bias: Omitted-variable Bias in Linear Regression, Effects On Ordinary Least Square
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