Introduction to Python for Econometrics, Statistics and Numerical Analysis: 3rd Edition, 1st Revision

These notes are designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research using Python.

**Tag(s):**
Python
Statistics

**Publication date**: 09 Sep 2019

**ISBN-10**:
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**ISBN-13**:
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**Paperback**:
427 pages

**Views**: 9,838

**Type**: Lecture Notes

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**Post time**: 29 Oct 2016 09:00:00

Introduction to Python for Econometrics, Statistics and Numerical Analysis: 3rd Edition, 1st Revision

These notes are designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research using Python.

From the Introduction:

More Resources:

Kevin Sheppard wrote:These notes are designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research using Python. They should also be useful for students, researchers or practitioners who require a versatile platform for econometrics, statistics or general numerical analysis (e.g. numeric solutions to economic models or model simulation).

Python is a popular general purpose programming language which is well suited to a wide range of problems. Recent developments have extended Python’s range of applicability to econometrics, statistics and general numerical analysis. Python – with the right set of add-ons – is comparable to domain-specific languages such as R, MATLAB or Julia.

More Resources:

- The book webpage at kevinsheppard.com

- Kevin Sheppard's page at GitHub.io

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About The Author(s)

Professor Kevin Sheppard is Tutorial Fellow in Economics and Associate Professor in Financial Economics at Keble College, Oxford. His research focuses on issues, both theoretical and empirical, in financial econometrics. Specifically, He is interested in volatility and dependance modeling, market microstructure, and portfolio allocation.

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