In Part 1 and Part 2 of this series, we examined how an individual’s qualitative characteristics can affect the results of our regression model. First, we analysed how a categorical variable, such as [gender], changes the constant of our regression model (also known as intercept dummy). Subsequently, we examined the impact of multiple categorical and …
In part 1 of this tutorial, we examined how a categorical variable can affect our regression model. However, most of the time, our model not only depends on more than one categorical variable, but also on a continuous variable; education, gender, and age can together play a significant role in predicting salary levels. This tutorial …
In econometric models, some variables can play a very important role in the explanation of an economic phenomenon. For instance, gender can play a crucial role in determining salary levels. However, quantifying gender isn’t straightforward because gender lacks a numerical nature. This tutorial aims to explain methods for incorporating such information into econometric models, employing …
When working with linear regression models, it’s essential to assess the model’s assumptions and diagnostic tests. These diagnostics help us in ensuring the reliability of our regression analysis. In simple terms, they ensure that the results are correct and unbiased. In this tutorial, we will only focus on three crucial diagnostic tests: normality of residuals, …
Panel data, also known as longitudinal data, involves collecting observations on multiple entities (such as firms, individuals, or countries) over multiple time periods. Researchers often use fixed effects, which can be in the form of time dummies or industry dummies, to account for various sources of data variation. In this article, we will test the …
