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Books > Business & Economics > Economics > Economic theory & philosophy
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Quantitative Economics with R - A Data Science Approach (Paperback, 1st ed. 2020)
Loot Price: R2,440
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Quantitative Economics with R - A Data Science Approach (Paperback, 1st ed. 2020)
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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 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 applied economics and enjoy engaging with the
material actively, while also equipping them with key data science
skills.
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