Introduction Many econometric models often get afflicted by what is called the endogeneity problem. This happens when one or more independent variables are correlated with the error term (omitted variable bias), or when the dependent and independent variables jointly determine each other (simultaneity bias). This problem needs to be addressed because in the presence of […]
Two-Stage Least Squares (2SLS) Regression in Stata Read More »
When doing regressions of an outcome variable on covariates that involve categorical variables, there are some issues that one needs to be mindful of with such analysis. Some common problems associated with regressions involving categorical variables are: Dummy Variable Trap Usually, when using categorical variables in a regression model, we tend to use dummy variables
Problems with Regressions Involving Categorical Variables Read More »
What are Dummy Variables Dummy variables, also called binary variables, are variables that only take two values: 0 and 1. Such variables are often used to quantify qualitative data like gender, sector of work. city/country etc. For example, a variable called ‘female’ might equal 1 for observations on females and 0 for observations on male
How to work with Dummy Dependent Variable in Stata Read More »
Oftentimes, we need to compare variables with each other in Stata. Stata’s compare command in Stata allows simple comparisons between variables to be made, which will be discussed further in this article. Download Example File The general syntax for this command is: compare variable1 variable2 [if] [in] After writing the command, the two variables to
Use of Compare command in Stata to compare variables Read More »
Whenever we run OLS regressions, there are a few basic assumptions that are made with regard to our data so that correct inferences and predictions can be made. To learn the basics of regression assumption, please refer to an introductory textbook on econometrics. The four regression assumptions that we will discuss in this article are
