Omitted Variable Bias: Violation of CLRM–Assumption 3: Explanatory Variables must be exogenous

One reason why the omitted variable leads to biased estimates is that omitting a relevant variable violates assumption 3 of the necessary assumptions of the classical regression model that states that all explanatory variables must be exogenous, i.e.

E(\epsilon_{i}|X)=0

From this post, we know that omitting a relevant variable from the regression causes the error term and the explanatory variables to be correlated.
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Linear Regression in Julia

Unfortunately, linreg() is deprecated and no longer exists in Julia v1.0. In case you are using Julia v1.0 or above, check out this post. In case you use a version of Julia that is older than 1.0, i.e 0.7, 0.6, etc., the following post will show you how to run a linear regression in Julia.


Julia presents various ways to carry out linear regressions. In this post I will present how to use the native function linreg() to run OLS on the following model

y = \alpha + \beta_{1} x_{1}

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