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Quantitative economics with Ra data ...

Dayal, Vikram.

Quantitative economics with Ra data science approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantitative economics with Rby Vikram Dayal.
其他題名:	a data science approach /
作者:	Dayal, Vikram.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xv, 326 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	EconomicsData processing.
電子資源:	https://doi.org/10.1007/978-981-15-2035-8
ISBN:	9789811520358$q(electronic bk.)

Quantitative economics with Ra data science approach /
Dayal, Vikram.

Quantitative economics with Ra data science approach /[electronic resource] :by Vikram Dayal. - Singapore :Springer Singapore :2020. - xv, 326 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9789811520358$q(electronic bk.)

Standard No.: 10.1007/978-981-15-2035-8doiSubjects--Topical Terms:

208545
Economics
--Data processing.

LC Class. No.: HB143.5 / .D393 2020

Dewey Class. No.: 330.0151

Quantitative economics with Ra data science approach /
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505 0 $a 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.
520 $a 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.
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