In our previous article, we delved into the fundamentals of creating dummy variables for binary categories, using gender—with its two categories of male and female—as a prime example. However, the landscape of categorical variables is far more expansive, often featuring more than two categories. In this follow-up article, we’ll broaden our focus to tackle categorical …
In regression analysis, quantifiable data often steals the spotlight. Yet, there are instances where qualitative or categorical variables like gender, ethnicity, age groups, or even something as simple as car colours play a crucial role. How do we seamlessly integrate these categorical variables into our statistical models? Take, for example, a study examining how gender …
In the previous 2 articles we discussed the theoretical and practical implications of the Pooled OLS, Fixed Effect and Random Effect Models. This article will discuss the significance of dummy variables such as time or industry dummies in our panel data. We will use the same dataset we used in the previous article. The data …
Panel Data With Fixed Effects (Time, year, country fixed effect) Read More »
In the previous article, we discussed the theoretical aspect of the panel data analysis, including the basic knowledge and limitation of the OLS, Fixed, and Random Effect Models. This article will demonstrate the application of various statistical models using Stata. Along with that, we will also look into the Breusch Pagan test and Hausman Test. …
Researchers and analysts consistently endeavour to derive significant insights to inform decision-making and policy development in an era of abundant data. Panel data analysis is a robust methodology that offers insights into longitudinal patterns and reveals valuable information within intricate datasets. This article explores panel data analysis, explaining its fundamental nature and various applications. Definition …
