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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.
This book deals with not just complex linkages, interactions and
exchanges that form the relationship between the economic
activities, human society and the ecosystems, but also the
influences and impacts that each causes on the other. In recent
times, this ecology-economy-society interface has received
unprecedented attention within the broader environment-development
discourse. The volume is in honour of Kanchan Chopra, one of the
pioneers of research in these areas in India. She has recently been
awarded the coveted Kenneth Boulding Award by the International
Society for Ecological Economics (ISEE) and is the first Asian to
receive it. The four sub-themes of the book reflect some of the
important areas in the environment-development discourse -
sustainability of development, institutions and environmental
governance, environment and well-being, and ecosystem and
conservation. Within each of the sub-themes, the policy and the
practice as well as the macro and micro aspects are addressed. With
contributions mainly from ecological economists and ecologists, the
book's approach is interdisciplinary, both in spirit and content,
reflecting the honoree's work, which went not just beyond the
mainstream ideology of economics, but also the way she listened to
ideas from disciplines like ecology and sociology. The volume also
includes two reflective essays on academic life and works of
Kanchan Chopra. The book is a valuable resource for students,
teachers, researchers, practitioners and policy makers in the areas
of development economics, ecological economics, environmental
economics and related disciplines such as conservation,
development, ecology, economics, environment, governance, health,
sociology and public policy.
This book deals with not just complex linkages, interactions and
exchanges that form the relationship between the economic
activities, human society and the ecosystems, but also the
influences and impacts that each causes on the other. In recent
times, this ecology-economy-society interface has received
unprecedented attention within the broader environment-development
discourse. The volume is in honour of Kanchan Chopra, one of the
pioneers of research in these areas in India. She has recently been
awarded the coveted Kenneth Boulding Award by the International
Society for Ecological Economics (ISEE) and is the first Asian to
receive it. The four sub-themes of the book reflect some of the
important areas in the environment-development discourse -
sustainability of development, institutions and environmental
governance, environment and well-being, and ecosystem and
conservation. Within each of the sub-themes, the policy and the
practice as well as the macro and micro aspects are addressed. With
contributions mainly from ecological economists and ecologists, the
book's approach is interdisciplinary, both in spirit and content,
reflecting the honoree's work, which went not just beyond the
mainstream ideology of economics, but also the way she listened to
ideas from disciplines like ecology and sociology. The volume also
includes two reflective essays on academic life and works of
Kanchan Chopra. The book is a valuable resource for students,
teachers, researchers, practitioners and policy makers in the areas
of development economics, ecological economics, environmental
economics and related disciplines such as conservation,
development, ecology, economics, environment, governance, health,
sociology and public policy.
This book gives an introduction to R to build up graphing,
simulating and computing skills to enable one to see theoretical
and statistical models in economics in a unified way. The great
advantage of R is that it is free, extremely flexible and
extensible. The book addresses the specific needs of economists,
and helps them move up the R learning curve. It covers some
mathematical topics such as, graphing the Cobb-Douglas function,
using R to study the Solow growth model, in addition to statistical
topics, from drawing statistical graphs to doing linear and
logistic regression. It uses data that can be downloaded from the
internet, and which is also available in different R packages. With
some treatment of basic econometrics, the book discusses
quantitative economics broadly and simply, looking at models in the
light of data. Students of economics or economists keen to learn
how to use R would find this book very useful.
This brief views the environment through diverse lenses - those of
standard economics, institutional economics, political science,
environmental science and ecology. Chapter 2 discusses diverse
theoretical and statistical models - constrained optimization
models, game theory, differential equations, and statistical models
for causal inference - in a simple manner. Developing countries
have certain distinct environmental problems - traditional
pollution and traditional dependence on the commons. While chapters
3 and 4 discuss these specific problems, statistical graphs of the
World Development Indicators explore the macro-context of
developing countries in chapter 1. Chapter 5 examines ecological
systems, which are nonlinear and unpredictable, and subject to
sudden regime shifts. Chapter 6 deals with the global challenges of
climate change and biological invasions. The last chapter discusses
sustainable development and institutions. The brief explains these
topics simply; mathematics is largely confined to an appendix. The
broad treatment and simple exposition will appeal to students new
to the field of economics. The extension of core economic models in
diverse directions will also be of interest to economists looking
for a different treatment of the subject.
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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