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  • © 2020

Quantitative Economics with R

A Data Science Approach

Authors:

  • 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

Buying options

eBook USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-981-15-2035-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 79.99
Price excludes VAT (USA)
Hardcover Book USD 109.99
Price excludes VAT (USA)

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Table of contents (16 chapters)

  1. Front Matter

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

    1. Front Matter

      Pages 1-1
    2. Introduction

      • Vikram Dayal
      Pages 3-8
    3. RStudio and R

      • Vikram Dayal
      Pages 9-27
  3. Managing and Graphing Data

    1. Front Matter

      Pages 29-29
    2. Getting Data into R

      • Vikram Dayal
      Pages 31-36
    3. Wrangling and Graphing Data

      • Vikram Dayal
      Pages 37-59
    4. Networks

      • Vikram Dayal
      Pages 61-80
  4. Mathematical Preliminaries for Data Analysis

    1. Front Matter

      Pages 81-81
    2. Functions

      • Vikram Dayal
      Pages 83-92
    3. Difference Equations

      • Vikram Dayal
      Pages 93-108
    4. Matrices

      • Vikram Dayal
      Pages 109-115
  5. Inference from Data

    1. Front Matter

      Pages 117-117
    2. Statistical Inference

      • Vikram Dayal
      Pages 119-151
    3. Causal Inference

      • Vikram Dayal
      Pages 153-223
  6. Accessing, Analysing and Interpreting Growth Data

    1. Front Matter

      Pages 225-225
    2. Growth Data and Models

      • Vikram Dayal
      Pages 227-244
    3. Growth Causes

      • Vikram Dayal
      Pages 245-255
  7. Time Series Data

    1. Front Matter

      Pages 257-257
    2. Graphs for Time Series

      • 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.


Keywords

  • R
  • Time Series Data
  • Causality
  • Graph
  • Data Wrangling
  • Solow Model

Authors and Affiliations

  • Indian Economic Service Section, Institute of Economic Growth, Delhi, India

    Vikram Dayal

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.


Bibliographic Information

Buying options

eBook USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-981-15-2035-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 79.99
Price excludes VAT (USA)
Hardcover Book USD 109.99
Price excludes VAT (USA)