In a previous post, we discussed how to obtain clustered standard errors in R. While the previous post described how one can easily calculate cluster robust standard errors in R, this post shows how one can include cluster robust standard errors in stargazer and create nice tables including clustered standard errors.
In a previous post, we discussed how to obtain robust standard errors in R. While the previous post described how one can easily calculate robust standard errors in R, this post shows how one can include robust standard errors in stargazer and create nice tables including robust standard errors.
Unfortunately, linreg() is deprecated and no longer exists in Julia v1.0. In case you are using Julia v1.0 or above, check out this post. In case you use a version of Julia that is older than 1.0, i.e 0.7, 0.6, etc., the following post will show you how to run a linear regression in Julia.
In a former blog post (see here) I described how to read an Excel file into Julia. In this post I will focus on how to import an Excel file directly from the Web. This feature might be especially useful for recurring routines that rely on the most up-to-date data. Continue reading How to Read an Excel File dircetly form the Web in Julia Language?
A Venn diagram (also sometimes also called primary diagram or set diagram) is a diagram that depicts all possible logical relations between a collection of sets. Certain subjects, such as omitted variable bias, can be best be explained by using a Venn diagram. This post shows how to construct a Venn diagram in R.
This article shortly describes how to read an Excel file into Julia. Generally, one can use different libraries to read Excel files, including XLSXReader , ExcelReaders or Taro. In this tutorial I will focus on Taro as it created the fewest problems and provides – at least in my eyes – an easy to understand syntax.
Continue reading How to Read an Excel File in Julia Language? An example.
In STATA clustered standard errors are obtained by adding the option
cluster(variable_name) to your regression, where variable_name specifies the variable that defines the group / cluster in your data. The summary output will return clustered standard errors. Here is the syntax:
regress x y, cluster(variable_name)
Below you will find a tutorial that demonstrates how to Continue reading Clustered Standard Errors in STATA
Julia has been around since a couple of years now and continues to attract new users. Julia Language uses concepts from well established programming languages in order to create an easy-to-use high performance software.
The official website describes Julia asContinue reading Why use Julia Language!