Sign up
Forgot password?
FAQ: Login

Heiss F. Using R for Introductory Econometrics

  • pdf file
  • size 48,36 MB
  • added by
  • info modified
Heiss F. Using R for Introductory Econometrics
CreateSpace Independent Publishing Platform, 2016. — 356 p. — ISBN: 1523285133, 9781523285136.
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
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
  • Sign up or login using form at top of the page to download this file.
  • Sign up

See also

Details

Arratia A. Computational Finance: An Introductory Course with R

  • pdf
Atlantis Press, 2014. — 301 p. — (Atlantis Studies in Computational Finance and Financial Engineering). — ISBN: 946239069X, 9789462390690 The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic...
  • 4,32 MB
  • added
  • info modified
Details

Brownlee J. Machine Learning Mastery with R: Get Started, Build Accurate Models and Work Through Projects Step-by-Step

  • archive
  • pdf
Machine Learning Mastery, 2016. — 223 p. — ISBN: N\A Do you want to do machine learning using R, but you’re having trouble getting started? In this post you will complete your first machine learning project using R. In this step-by-step tutorial you will: Download and install R and get the most useful package for machine learning in R. Load a dataset and understand it’s...
  • 4,01 MB
  • added
  • info modified
Details

Ghatak Abhijit. Deep Learning with R

  • pdf
Springer, 2019. — 259 p. — ISBN: 9811358494. Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. Deep learning is a specific...
  • 5,79 MB
  • added
  • info modified
Details

Matloff N. The Art of R Programming: A Tour of Statistical Software Design

  • pdf
R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development...
  • 4,51 MB
  • added
  • info modified
Details

Tsay R.S. Multivariate Time Series Analysis: With R and Financial Applications

  • pdf
Wiley, 2013. — 520 p. — ISBN: 1118617908, 9781118617908 An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of...
  • 5,49 MB
  • added
  • info modified
Details

Wickham Hadley, Grolemund Garrett. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

  • pdf
O’Reilly Media, 2017. — 522 p. — ISBN: 9781491910399. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham...
  • 32,86 MB
  • added
  • info modified
Up