GARCH Estimation
This post details GARCH(1,1) model and its estimation manually in Python, compared to using libraries and in Stata. For GJR-GARCH(1,1), see my documentation on frds.io.
2023¶
Download SEC Filings from EDGAR
This post documents how to download SEC filings from EDGAR using edgar-analyzer, a Python program I wrote. It features:
- 3 commands only to download any type of filings for any period of time
- auto throttling of download speed to adhere to the SEC policy of fair use
Difference-in-Differences Estimation
Empirical researchers have been using difference-in-differences (DiD) estimation to identify an event's Average Treatment effect on the Treated entities (ATT). This post is my understanding and a non-technical note of the DiD approach as it evolves over the past years, especially on the problems and solutions when multiple treatment events are staggered.
CRSP Missing Codes
A note on the missing codes in CRSP.
FRED - Federal Reserve Economic Data
Since Stata 15, we can search, browse and import almost a million U.S. and international economic and financial time series made available by the St. Louis Federal Reserve's Federal Research Economic Data. This post briefly explains this great feature.
Correlated Random Effects
Can we estimate the coefficient of gender while controlling for individual fixed effects? This sounds impossible as an individual's gender typically does not vary and hence would be absorbed by individual fixed effects. However, Correlated Random Effects (CRE) may actually help.
At last year's FMA Annual Meeting, I learned this CRE estimation technique when discussing a paper titled "Gender Gap in Returns to Publications" by Piotr Spiewanowski, Ivan Stetsyuk and Oleksandr Talavera. Let me recollect my memory and summarize the technique in this post.
