Introduction to Python for Econometrics, Statistics and Numerical Analysis
by Kevin Sheppard
Number of pages: 281
Python is a widely used general purpose programming language, which happens to be well suited to Econometrics and other more general purpose data analysis tasks. These notes provide an introduction to Python for a beginning programmer. They may also be useful for an experienced Python programmer interested in using NumPy, SciPy, and matplotlib for numerical and statistical analaysis.
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by Daniel McFadden - University of California, Berkeley
The contents: Economic Analysis and Econometrics; Analysis and Linear Algebra in a Nutshell; Probability Theory in a Nutshell; Limit Theorems in Statistics; Experiments, Sampling, and Statistical Decisions; Estimation; Hypothesis Testing.
by Charles F. Manski, Daniel McFadden - The MIT Press
The book provides a methodological foundation for the analysis of economic problems involving discrete data, and charts the current frontiers of this subject. It is also useful for the researchers involved in the structural analysis of discrete data.
by Daniel McFadden, Antti Talvitie, and Associates - University of California
From the table of contents: Theory and Estimation of Behavioral Travel Demand Models; Development, Testing, and Validaton of a Work-Trip Mode-Choice Model; Modeling Choices Other than Work-Trip; Issues in Demand Modeling and Forecasting.
by Thomas Andren - BookBoon
This book covers the most basic concepts in econometrics. Subjects as basic probability and statistics, statistical inference with the simple and multiple regression model, dummy variables and auto correlation are explained.