How to set a Seed in Julia?

Julia v0.7 and older

In Julia, you can set a seed to the random number generator using the srand() function. The code example below sets the seed to 1234. Generating a random variable with rand(1) after setting the seed to 1234 will always generate the same number, i.e. it will always return 0.5908446386657102.

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Linear Regression in STATA

In STATA one can estimate a linear regression using the command regress. In this post I will present how to use the STATA function regress to run OLS on the following model $y = \alpha + \beta_{1} x_{1}$

Cluster Robust Standard Errors in Stargazer

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.

Robust Standard Errors in Stargazer

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.

Multiple Regression in Julia

Julia presents various ways to carry out multiple regressions. One easy way is to use the lm() function of the GLM package. In this post I will present how to use the lm() and run OLS on the following model $y = \alpha + \beta_{1} x_{1} + \beta_{2} x_{2} + \beta_{3} x_{3}$

Linear Regression in Julia

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.

Julia presents various ways to carry out linear regressions. In this post I will present how to use the native function linreg() to run OLS on the following model $y = \alpha + \beta_{1} x_{1}$