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000928746 019__ $$a1142783607$$a1142885638
000928746 020__ $$a9789811520358$$q(electronic book)
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000928746 0247_ $$a10.1007/978-981-15-2035-8$$2doi
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000928746 08214 $$a519
000928746 1001_ $$aDayal, Vikram.
000928746 24510 $$aQuantitative economics with R :$$bA Data Science Approach /$$cby Vikram Dayal.
000928746 264_1 $$aSingapore :$$bSpringer,$$c2020.
000928746 300__ $$a1 online resource (xv, 326 pages) :$$billustrations
000928746 336__ $$atext$$btxt$$2rdacontent
000928746 337__ $$acomputer$$bc$$2rdamedia
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000928746 5050_ $$aCh 1 Introduction -- Ch 2 R and RStudio -- Ch 3 Getting data into R -- Ch 4 Wrangling and graphing data -- Ch 5 Functions -- Ch 6 Matrices -- Ch 7 Probability and statistical inference -- Ch 8 Causal inference -- Ch 9 Solow model and basic facts of growth -- Ch 10 Causal inference for growth -- Ch 11 Graphing and simulating basic time series -- Ch 12 Simple examples: forecasting and causal inference -- Ch 13 Generalized additive models -- Ch 14 Tree models.
000928746 506__ $$aAccess limited to authorized users.
000928746 520__ $$aThis book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham's tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader's R skills are gradually honed, with the help of "your turn" exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrapis introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods--generalized additive models and random forests (an important and versatile machine learning method)--are introduced intuitively with applications. The book will be of great interest to economists--students, teachers, and researchers alike--who want to learn R. It will help economics students gain an intuitive appreciation of appliedeconomics and enjoy engaging with the material actively, while also equipping them with key data science skills.--$$cProvided by publisher.
000928746 650_0 $$aEconomics$$xData processing.
000928746 650_0 $$aR (Computer program language)
000928746 650_0 $$aGame theory.
000928746 650_0 $$aEconomics.
000928746 650_0 $$aStatistics.
000928746 650_0 $$aComputer simulation.
000928746 650_0 $$aSociology$$xResearch.
000928746 77608 $$iPrint version:$$z9789811520341
000928746 852__ $$bebk
000928746 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-2035-8$$zOnline Access$$91397441.1
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000928746 980__ $$aBIB
000928746 982__ $$aEbook
000928746 983__ $$aOnline
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