Python Guide for Introductory Econometrics for Finance, 4th Edition


Python Guide for Introductory Econometrics for Finance, 4th Edition
By 作者: Chris Brooks
Publisher Finelybook 出版社: Cambridge University Press (28 Mar. 2019)
ASIN: B07WTWVDSN
Pages 页数: 256
Language 语言: English
Format: EPUB
Size: 37 Mb

Book Description to Finelybook sorting
This free software guide for Python with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches presented in Introductory Econometrics for Finance using this highly popular software package. Designed to be used alongside the main textbook, the guide will give readers the confidence and skills to estimate and interpret their own models while the textbook will ensure that they have a thorough understanding of the conceptual underpinnings.

Contents


List of Figures
List of Tables
1Getting started
2 Data management in Python
3Simple linear regression-estimation of an optimal hedge ratio
4Hypothesis testing-Example 1:hedging revisited
5Estimation and hypothesis testing -Example 2:the CAPM
6Sample output for multiple hypothesis tests
7Multiple regression using an APT-style model
8Quantile regression
9Calculating principal components
10Diagnostic testing
11 Constructing ARMA models
12 Forecasting using ARMA models
13 Estimating exponential smoothing models
14Simultaneous equations modelling
15The Generalised method of moments for instrumental variables
16VAR estimation
17 Testing for unit roots
18Cointegration tests and modeling cointegrated systems
19Volatility modelling
20Modelling seasonality in financial data
21 Panel data models
22 Limited dependent variable models
23 Simulation methods
24The Fama-MacBeth procedure
25 Using extreme value theory for VaR calculation
References
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Python Guide for Introductory Econometrics for Finance, 4th Edition.zip

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