| 書(shū)目名稱 | Quantitative Economics with R | | 副標(biāo)題 | A Data Science Appro | | 編輯 | Vikram Dayal | | 視頻video | http://file.papertrans.cn/781/780831/780831.mp4 | | 概述 | 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.Int | | 圖書(shū)封面 |  | | 描述 | .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 inferenceis 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 t | | 出版日期 | Textbook 2020 | | 關(guān)鍵詞 | R; Time Series Data; Causality; Graph; Data Wrangling; Solow Model | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-15-2035-8 | | isbn_softcover | 978-981-15-2037-2 | | isbn_ebook | 978-981-15-2035-8 | | copyright | Springer Nature Singapore Pte Ltd. 2020 |
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