Econometrics with Python
The aim of this study is not limited to applying 132 examples from Jeffrey Wooldridge's book "Introduction to Econometrics: A Modern Approach", which has been the most widely used econometrics book in the world for 10 years according to Amazon's 2010-2020 Best-seller report, with Python.
Beyond that;
Thanks to open source, free and accessible application packages coded in Python language, you will be able to output expensive software by writing a few lines of code, saving hundreds and thousands of dollars,
Without being dependent on licensed software in university laboratories, you can easily do your homework, project, work from your home, in the coffee chain around the corner, in the library, on the sun lounger on the beach,
You will be able to taste the advantage of Python packages developed and updated by hundreds of thousands of volunteers who have never met each other in the world, but will enjoy the free learning resources of these packages,
You will take a strong step towards gaining coding skills, one of the most important competencies of our age.
INGREDIENTS
MULTIPLE REGRESSION MODEL
MULTIPLE REGRESSION ANALYSIS: INCLUSION
MULTIPLE REGRESSION ANALYSIS: ASYMPTOTIC PROPERTIES OF CCT
MULTIPLE REGRESSION ANALYSIS: ADVANCED TOPICS
MULTIPLE REGRESSION ANALYSIS WITH QUALITATIVE INFORMATION: BINARY (OR Dummy) Variables
VARYING VARIANCE
MORE ABOUT IDENTIFICATION AND DATA ISSUES
REGRESSION ANALYSIS WITH TIME SERIES
ADDITIONAL ISSUES ON USING SEKK WITH TIME SERIES DATA
SERIAL CORRELATION AND VARYING VARIANCE IN TIME SERIES REGRESSIONS
POOLED HORIZONTAL SECTIONS: SIMPLE PANEL DATA METHODS
ADVANCED PANEL DATA METHODS
LIMITED DEPENDENT VARIABLE MODELS AND SAMPLE SELECTION FIXES
ADVANCED SERIES TOPICS