How to fix an Omitted Variable Bias in Data Science using Stata?
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This video explains how to detect and fix an omitted variable bias. If you forget to include an important explanatory variable in your regression model, an omitted variable bias can occur. I explain how you can detect this problem using the Ramsey RESET test. This test also indicates non-linear relationships. We will explore how we can distinguish between non-linear effects and omitted variables using fitted values.
All the material, including slides, data, and the Stata code, is available on GitHub (see Channel pages for a link).
Chapters
0:00 Omitted Variable Bias
1:34 Worked Example in Stata
3:55 Log Transformation
5:08 Regression Model
6:50 Ramsey RESET Test
9:10 Higher Orders
15:36 Collapse Command
17:01 Visualisation
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
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