9781523285136-1523285133-Using R for Introductory Econometrics

Using R for Introductory Econometrics

ISBN-13: 9781523285136
ISBN-10: 1523285133
Edition: 1
Author: Florian Heiss
Publication date: 2016
Publisher: CreateSpace Independent Publishing Platform
Format: Paperback 354 pages
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Book details

ISBN-13: 9781523285136
ISBN-10: 1523285133
Edition: 1
Author: Florian Heiss
Publication date: 2016
Publisher: CreateSpace Independent Publishing Platform
Format: Paperback 354 pages

Summary

Using R for Introductory Econometrics (ISBN-13: 9781523285136 and ISBN-10: 1523285133), written by authors Florian Heiss, was published by CreateSpace Independent Publishing Platform in 2016. With an overall rating of 3.7 stars, it's a notable title among other Econometrics & Statistics (Economics, Mathematical & Statistical, Software) books. You can easily purchase or rent Using R for Introductory Econometrics (Paperback) from BooksRun, along with many other new and used Econometrics & Statistics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

  • Introduces the popular, powerful and free programming language and software package R
  • Focus: implementation of standard tools and methods used in econometrics
  • Compatible with "Introductory Econometrics" by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation
  • Companion website with full text, all code for download and other goodies

Praise:

  • "A very nice resource for those wanting to use R in their introductory econometrics courses." (Jeffrey M. Wooldridge)
  • Using R for Introductory Econometrics is a fabulous modern resource. I know I'm going to be using it with my students, and I recommend it to anyone who wants to learn about econometrics and R at the same time." (David E. Giles in his blog "Econometrics Beat")

Topics:

  • A gentle introduction to R
  • Simple and multiple regression in matrix form and using black box routines
  • Inference in small samples and asymptotics
  • Monte Carlo simulations
  • Heteroscedasticity
  • Time series regression
  • Pooled cross-sections and panel data
  • Instrumental variables and two-stage least squares
  • Simultaneous equation models
  • Limited dependent variables: binary, count data, censoring, truncation, and sample selection
  • Formatted reports and research papers combining R with R Markdown or LaTeX
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