Using R for Introductory Econometrics

✍ Scribed by Florian Heiss

• Publisher: Florian Heiss
• Year: 2020
• Tongue: English
• Leaves: 379
• Edition: 2
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Cover
Contents
Preface
1. Introduction
Part I. Regression Analysis with Cross-Sectional Data
2. The Simple Regression Model
3. Multiple Regression Analysis: Estimation
4. Multiple Regression Analysis: Inference
5. Multiple Regression Analysis: OLS Asymptotics
6. Multiple Regression Analysis: Further Issues
7. Multiple Regression Analysis with Qualitative Regressors
8. Heteroscedasticity
9. More on Specification and Data Issues
Part II. Regression Analysis with Time Series Data
10. Basic Regression Analysis with Time Series Data
11. Further Issues In Using OLS with Time Series Data
12. Serial Correlation and Heteroscedasticity in Time Series Regressions
Part III. Advanced Topics
13. Pooling Cross-Sections Across Time: Simple Panel Data Methods
14. Advanced Panel Data Methods
15. Instrumental Variables Estimation and Two Stage Least Squares
16. Simultaneous Equations Models
17. Limited Dependent Variable Models and Sample Selection Corrections
18. Advanced Time Series Topics
19. Carrying Out an Empirical Project
Part IV. Appendices
R Scripts
Bibliography
List of Wooldridge (2019) Examples
Index