In this note, I walk you through setting up Visual Studio Code (VS Code) on Windows PCs for empirical economics or finance research. I show how you can run Stata codes, use Git (source control), and GitHub Copilot (AI-powered code completion) in VS Code.
This note introduces how to use Stata for four tasks: drawing maps, geocoding, matching locations to polygons, and finding neighboring polygons.
I summarize four credit supply shocks used in previous literature and apply them to estimate the effect of credit supply on housing markets. In particular, I provide evidence of the effect on housing liquidity.
I introduce a new loan-level data set, HMDA+, which is constructed by merging the Home Mortgage Disclosure Act (HMDA) data with Fannie Mae, Freddie Mac, and Ginnie Mae data. This data set covers about half of GSE and FHA loans in HMDA from 2015 to 2019. Compiled using all publicly available data sources, the HMDA+ data set offers detailed information about borrowers, lenders, mortgages, and properties.
I compare two methods to use grouped income data to calculate the local Gini coefficient. The first method uses a user-written program called inequal7, while the second method applies the formula of the Gini coefficient to perform matrix calculations. The preferred method depends on the number of locations you have.