Fixed Effects, Random Effects, and First Differencing

I came across a stackoverflow post the other day touching on first differencing and decided to write a quick review of the topic as well as related random effects and fixed effects methods. In the end we'll see that random effects, fixed effects, and first differencing are primarily used to handle unobserved heterogeneity within a … Continue reading Fixed Effects, Random Effects, and First Differencing


Simulating Endogeneity

Introduction¬†The topic in this post is endogeneity, which can severely bias regression¬†estimates. I will specifically simulate endogeneity caused by an omitted variable. In future posts in this series, I'll simulate other specification issues such as heteroskedasticity, multicollinearity, and collider bias. The Data-Generating Process Consider the data-generating process (DGP) of some outcome variable $latex Y $: … Continue reading Simulating Endogeneity