by Marcelo S. Perlin
Not surprisingly, fields with abundant access to data and practical applications, such as economics and finance, it is expected that a graduate student or a data analyst has learned at least one programming language that allows him/her to do his work efficiently. Learning how to program is becoming a requisite for the job market.
This project will gradually turn the course materials for the “Econometrics and Statistics: Microeconometrics” course at Universität Innsbruck into an online book.
The topics covered roughly follow the book Analysis of Microdata by Winkelmann & Boes (2009, Springer-Verlag) and encompass: models for categorical responses (binary, multinomial, ordered), count data, limited dependent variables, and duration models.
by Jonathan Lin
This is the website for Audit Analytics in R. This audience of this book is for:
Audit leaders who are looking to design their environment to encourage cultivate collaboration and sustainability. Audit data analytics practitioners, who are looking to leverage R in their data analytics tasks. You will learn what tools and technologies are well suited for a modern audit analytics toolkit, as well as learn skills with R to perform data analytics tasks. Consider this book to be your roadmap of practical items to implement and follow.
Introduce Economics graduate students to the modern data science toolkit
This is an R tutorial book for Financial Econometrics in PDF format.
by Eric Zivot
This book is based on my University of Washington sponsored Coursera course Introduction to Computational Finance and Financial Econometrics that has been running every quarter on Coursera since 2013. This Coursera course is based on the Summer 2013 offering of my University of Washington advanced undergraduate economics course of the same name. At the time, my UW course was part of a three course summer certificate in Fundamentals of Quantitative Finance offered by the Professional Masters Program in Computational Finance & Risk Management that was video-recorded and available for online students. An edited version of this course became the Coursera course. The popularity of the course encouraged me to convert the class notes for the course into a short book.
by Florian Oswald, Vincent Viers, Jean-Marc Robin, Pierre Villedieu, Gustave Kenedi
Welcome to Introductory Econometrics for 2nd year undergraduates at ScPo! On this page we outline the course and present the Syllabus. 2018/2019 was the first time that we taught this course in this format, so we are in year 3 now.
This is a short introduction to R to go with the first year econometrics courses at the Tinbergen Institute. It is aimed at people who are relatively new to R, or programming in general. The goal is to give you enough of knowledge of the fundamentals of R to write and adapt code to fit econometric models to data, and to simulate your own data, working alone or with others. You will be able to: read data from csv files, plot it, manipulate it into the form you want, use sets of functions others have built (packages), write your own functions to compute estimators, simulate data to test the performance of estimators, and present the results in a nice format.
Most importantly, when things inevitably go wrong, you will be able to begin to interpret error messages and adapt others’ solutions to fit your needs.
This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.
R supplementary resource for the “Principles of Econometrics” textbook by Carter Hill, William Griffiths and Guay Lim, 4-th edition
by Tony Carilli
The intended audience for this book is anyone making using of Real Econometrics: The Right Tools to Answer Important Questions 2nd ed. by Michael Bailey who would like to learn to use R, RStudio, and the tidyverse to complete empirical examples from the text. This book will be useful to anyone wishing to integrate R and the Tidyverse into an econometrics course.
by Robert Wichmann, Chris Brooks
This free software guide for R 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.
by Jonathan K. Regenstein Jr.
A unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples.
Financial economics is a vibrant area of research, a central part of all businesses activities, and at least implicitly relevant for our everyday life. Despite its relevance for our society and a vast number of empirical studies of financial phenomenons, one quickly learns that the actual implementation is typically rather opaque.
This book aims to lift the curtain on reproducible finance by providing a fully transparent code base for many common financial applications. We hope to inspire others to share their code publicly and take part in our journey towards more reproducible research in the future.
The book starts with an introduction to the most important tools for the portfolio analysis: timeseries (mainly xts) and the tidyverse. Afterwards, the possibilities of managing and exploring financial data will be developed. Then we do portfolio optimization for mean-Variance and Mean-CVaR portfolios. This will be followed by a chapter on backtesting, before I show further applications in finance, such as predictions, portfolio sorting, Fama-MacBeth-regressions etc.
Created and maintained by Oscar Baruffa
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