Tag Archives: OLS

Omitted Variable Bias: Explaining the Bias

In the previous two posts on the Omitted Variable Bias (Post 1 and Post 2), we discussed the hypothetical case of finding out what determines the price of a car. In the hypothetical example, we assumed, for simplicity, that the … Continue reading

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Omitted Variable Bias: Understanding the Bias

The second part of the series on the Omitted Variable Bias intends to increase the readers understanding of the bias. Let’s continue with the example from the Introduction. Let the dependent variable be the price of a car and the … Continue reading

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Omitted Variable Bias: Introduction

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 first assumption of the assumption of … Continue reading

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Assumptions of Classical Linear Regression Models (CLRM)

The following post will give a short introduction about the underlying assumptions of the classical linear regression model (OLS assumptions), which we derived in the following post. Given the  Gauss-Markov Theorem we know that the least squares estimator and are … Continue reading

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The Gauss Markov Theorem

The Gauss-Markov Theorem is telling us that in a regression model, where the expected value of our error terms is zero, and variance of the error terms is constant and finite and and are uncorrelated for all and the least … Continue reading

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Derivation of the Least Squares Estimator for Beta in Matrix Notation

The following post is going to derive the least squares estimator for , which we will denote as . In general start by mathematically formalizing relationships we think are present in the real world and write it down in a … Continue reading

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