Quantitative Economics With R: A Data Science Approach

Dayal, Vikram

ISBN 10: 9811520372 / ISBN 13: 9789811520372

Published by Springer, 2020

About this Item

This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9789811520372.

Seller Inventory # 3977820

Bibliographic Details

• Title: Quantitative Economics With R: A Data Science Approach
• Publisher: Springer
• Publication Date: 2020
• Language: English
• ISBN 10: 9811520372
• ISBN 13: 9789811520372
• Binding: Soft cover
• Condition: Good
• Weight: 600 grams
• Seller catalogs:
  • Economics

About this title

Synopsis

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code,  the reader’s R skills are gradually honed, with the help of  “your turn” exercises. 

At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inferenceis illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods―generalized additive models and random forests (an important and versatile machine learning method)―are introduced intuitively with applications. 

The book will be of great interest to economists―students, teachers, and researchers alike―who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.

About the Author

Vikram Dayal is a Professor at the Institute of Economic Growth, Delhi. He has been using the R software in teaching quantitative economics to diverse audiences, and is the author of the Springer Brief titled An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing. He has published research on a range of environmental and developmental issues, from outdoor and indoor air pollution in Goa, India, to tigers and Prosopis juliflora in Ranthambore National Park. He studied economics in India and the USA, and received his doctoral degree from the Delhi School of Economics, University of Delhi.