Financial Risk Manager Part 1
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A financial analyst wishes to come up with Ordinary Least Squares Estimation (OLS) regression model to analyze the financial performance of a company. However, the analyst is aware that some of the explanatory variables might be excluded (and hence omitted variable bias). What is the cause of omitted variables bias?
Explanation:
Explanation
Omitted variable bias occurs when a variable that should have been included in a regression model is left out. This omitted variable is both a significant determinant of the dependent variable and is correlated with at least one of the independent variables that are included in the model. When this happens, the estimated coefficients of the included variables become biased, leading to inaccurate predictions. This is because the effect of the omitted variable is falsely attributed to the included variables with which it is correlated.
Why Option D is Correct:
- The omitted variable must be correlated with at least one included independent variable
- The omitted variable must be a determinant of the dependent variable
- When both conditions are met, the estimated coefficients become biased
Why Other Options are Incorrect:
Choice A is incorrect: Omitted variable bias does not occur when the omitted variable is independent of the included independent variables and is not a determinant of the dependent variable. In this case, omitting such a variable would not cause any bias as it does not affect the dependent variable nor is it correlated with other independent variables.
Choice B is incorrect: While it's true that an omitted variable can be correlated with all of the included independent variables, this alone doesn't lead to omitted variable bias. The key factor for this bias to occur is that the omitted variable must also be a determinant of the dependent variable.
Choice C is incorrect: Even though an omitted variable can be a determinant of the dependent variable, if it's completely uncorrelated with all other included independent variables, its omission will not result in any bias in estimating parameters for those included variables.
Therefore, it is crucial to include all relevant variables in a regression model to avoid omitted variable bias and ensure the accuracy of the model's predictions.