Quantitative economics with R : A Data Science Approach / by Vikram Dayal.
2020
HB143.5
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Details
Title
Quantitative economics with R : A Data Science Approach / by Vikram Dayal.
Author
Dayal, Vikram.
ISBN
9789811520358 (electronic book)
9811520356 (electronic book)
9789811520341
9811520348
9811520356 (electronic book)
9789811520341
9811520348
Published
Singapore : Springer, 2020.
Language
English
Description
1 online resource (xv, 326 pages) : illustrations
Other Standard Identifiers
10.1007/978-981-15-2035-8 doi
Call Number
HB143.5
Dewey Decimal Classification
330.01/51
519
519
Summary
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.-- Provided by publisher.
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text file PDF
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Print version: 9789811520341
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Table of Contents
Ch 1 Introduction
Ch 2 R and RStudio
Ch 3 Getting data into R
Ch 4 Wrangling and graphing data
Ch 5 Functions
Ch 6 Matrices
Ch 7 Probability and statistical inference
Ch 8 Causal inference
Ch 9 Solow model and basic facts of growth
Ch 10 Causal inference for growth
Ch 11 Graphing and simulating basic time series
Ch 12 Simple examples: forecasting and causal inference
Ch 13 Generalized additive models
Ch 14 Tree models.
Ch 2 R and RStudio
Ch 3 Getting data into R
Ch 4 Wrangling and graphing data
Ch 5 Functions
Ch 6 Matrices
Ch 7 Probability and statistical inference
Ch 8 Causal inference
Ch 9 Solow model and basic facts of growth
Ch 10 Causal inference for growth
Ch 11 Graphing and simulating basic time series
Ch 12 Simple examples: forecasting and causal inference
Ch 13 Generalized additive models
Ch 14 Tree models.