Research Module E&S
Preface
Topics
1
Introduction to R
1.1
Short Glossary
1.2
First Steps
1.3
Further Data Objects
1.4
Simple Regression Analysis using R
1.5
Programming in R
1.6
R-packages
1.7
Tidyverse
1.7.1
Tidyverse: Plotting Basics
1.7.2
Tidyverse: Data Wrangling Basics
1.7.3
The pipe operator
%>%
1.7.4
The
group_by()
function
1.8
Further Links
1.8.1
Further R-Intros
1.8.2
Version Control (Git/GitHub)
1.8.3
R-Ladies
2
Statistical Hypothesis Testing
2.1
Hypotheses and Test-Statistics
2.2
Significance Level, Size and p-Values
2.3
The Power Function
2.4
Asymptotic Null Distributions
2.5
Multiple Comparisons
2.6
R-Lab: The Gauss-Test
3
Estimation Theory
3.1
Bias, Variance and MSE
3.2
Consistency of Estimators
3.3
Rates of Convergence
3.4
Asymptotic Distributions
3.5
Asymptotic Theory
3.6
Mathematical tools
3.6.1
Taylor expansions
3.6.2
Tools for deriving asymptotic distributions
3.6.3
The Delta-Method
4
Linear Regression
5
Monte-Carlo Simulations
5.1
Checking Test Statistics
5.1.1
Simple Example: Gauss Test
5.1.2
Simulated Power Function
5.2
Checking Parameter Estimators
6
How to Write
6.1
Five Common Writing Mistakes
6.2
Gregory Mankiw: How to Write Well
6.3
Rob J Hyndman: Avoid Annoying a Referee
6.4
LaTeX
7
How to Present
7.1
The Aim of your Talk
7.2
A Suggested Structure
7.3
Preparing Slides
7.4
Keeping to Time
7.5
Giving the Presentation
Final Advice
References
Research Module in Econometrics & Statistics
Ch. 4
Linear Regression
The lecture script of this chapter can be found
HERE
.