Category Archives: Statistic

Omitted Variable Bias

The omitted variable bias is a common and serious problem in regression analysis. Generally, the problem arises if one does not consider all relevant variables in a regression. In this case, one violates the third assumption of the assumption of … Continue reading

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Omitted Variable Bias: What can we do about it?

To deal with an omitted variables bias is not easy. However, one can try several things. First, one can try,

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Multiple Regression in Julia

Julia presents various ways to carry out multiple regressions. One easy way is to use the lm() function of the GLM package. In this post I will present how to use the lm() and run OLS on the following model

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Linear Regression

A linear regression is a special case of the classical linear regression models that describes the relationship between two variables by fitting a linear equation to observed data. Thereby, one variable is considered to be the explanatory (or independent) variable, … Continue reading

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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. From this … Continue reading

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Omitted Variable Bias: Conclusion

The following post provides a recap of the previous posts on the omitted variable bias (Introduction, Explanation, In-depth discussion of the bias, Consequences of the omitted variable bias) and concludes with some general advise. In case you haven’t read the … Continue reading

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Omitted Variable Bias: Consequences

In this post, we will discuss the consequence of the omitted variable bias in a more elaborate way. Particularly, we will show that omitting a variable form the regression model violates an OLS assumption and discuss what will happen if … Continue reading

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