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 Charles Frederick Roos - Principia Press
Contents: Static Versus Dynamic Economics; Demand for Consumer Goods; Automotive Demand for Gasoline; Demand for Agricultural Products; Demand for Capital Goods; Factors Influencing Residential Building; Growth and Decline of Industry; etc.
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.
by Wolfgang Härdle - Cambridge University Press
Nonparametric regression analysis has become central to economic theory. Hardle, by writing the first comprehensive and accessible book on the subject, contributed enormously to making nonparametric regression equally central to econometric practice.
by Jerome Stein - Springer
Stochastic Optimal Control (SOC) is very helpful in understanding and predicting debt crises. The mathematical analysis is applied empirically to the financial debt crisis of 2008, the crises of the 1980s and the European debt crisis.