Detecting Endogeneity in Data Science using Stata

Описание: 【Ｏｎｌｉｎｅ　Ｃｏｕｒｓｅｓ】
⚡Getting Started with Stata: (24 lectures + 4 assignments = 5.5 hours content): available on Udemy: https://www.udemy.com/course/getting-...

⚡Applied Time Series using Stata (29 lectures + 4 assignments = 6.5 hours content): available on Udemy: https://www.udemy.com/course/applied-...

This video explains how to detect endogeneity. Endogeneity is a common problem in regression analysis. I explain how you can detect this problem using an auxiliary regression approach. We discuss strategies to address endogeneity.

All the material, including slides, data, and the Stata code, is available on GitHub (see Channel pages for a link).

Chapters
0:00 Welcome
0:15 What is Endogeneity?
1:42 Detecting Endogeneity
3:08 Worked Example in Stata
11:33 How to fix Endogeneity?

About the course
This course offers a complete introduction to Data Science. You do not have to have any background in statistics, mathematics or the use of software (Stata). We will cover a wide range of topics, including (1) Introduction to statistical models and Stata, (2) Exploring data, (3) Regression analysis, (4) Post estimation analysis, (5) Analysing panel data, (6) Binary choice models, (7) Model specification, and (8) Measuring the immeasurable: CFA (confirmatory factor analysis) and SEM (structural equation models). The academic version of this course was developed using the title “Analysing Qualitative and Quantitative Data” at SOAS University of London (2015-2019). A much more applied version was created for HMRC and delivered from 2012 to 2015. This version is a very applied and hands-on experience in data science – modified for YouTube.

The channel
YUNIKARN focuses on publishing educational content in applied statistics, mathematics, and data science. In these fields, programming skills have become essential. Hence, we cover various programming languages, including Python, Stata, and C++, to tackle problems and for fun.

Hashtags
#datascience #dataanalytics #stata