© 2020

Quantitative Economics with R

A Data Science Approach

  • Employs a popular data science approach while discussing concepts and applications related to economics

  • Explains causal inferences with the aid of simulations, data graphs, and sample applications

  • Introduces readers to two versatile statistical learning techniques—generalized additive models and tree models


Table of contents

  1. Front Matter
    Pages i-xv
  2. Introduction to the Book and the Data Software

    1. Front Matter
      Pages 1-1
    2. Vikram Dayal
      Pages 3-8
    3. Vikram Dayal
      Pages 9-27
  3. Managing and Graphing Data

    1. Front Matter
      Pages 29-29
    2. Vikram Dayal
      Pages 31-36
    3. Vikram Dayal
      Pages 37-59
    4. Vikram Dayal
      Pages 61-80
  4. Mathematical Preliminaries for Data Analysis

    1. Front Matter
      Pages 81-81
    2. Vikram Dayal
      Pages 83-92
    3. Vikram Dayal
      Pages 93-108
    4. Vikram Dayal
      Pages 109-115
  5. Inference from Data

    1. Front Matter
      Pages 117-117
    2. Vikram Dayal
      Pages 119-151
    3. Vikram Dayal
      Pages 153-223
  6. Accessing, Analysing and Interpreting Growth Data

    1. Front Matter
      Pages 225-225
    2. Vikram Dayal
      Pages 227-244
    3. Vikram Dayal
      Pages 245-255
  7. Time Series Data

    1. Front Matter
      Pages 257-257
    2. Vikram Dayal
      Pages 259-271

About this book


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 bootstrapis introduced. Causal inference is 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 appliedeconomics and enjoy engaging with the material actively, while also equipping them with key data science skills.


R Time Series Data Causality Graph Data Wrangling Solow Model

Authors and affiliations

  1. 1.Indian Economic Service SectionInstitute of Economic GrowthDelhiIndia

About the authors

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.

Bibliographic information