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Preface
Projects
1
Introduction to R
1.1
Short Glossary
1.2
First Steps
1.3
Further Data Objects
1.4
Simple Statistical Analysis
1.5
Regression Analysis
1.6
R-packages
2
Estimation Theory
2.1
Bias, Variance and MSE
2.2
Consistency of Estimators
2.3
Rates of Convergence
2.4
Asymptotic Distributions
2.5
Asymptotic Theory
2.6
Mathematical tools
2.6.1
Taylor expansions
2.6.2
Tools for deriving asymptotic distributions
2.6.3
The Delta-Method
3
Statistical Hypothesis Testing
3.1
Hypotheses and Test-Statistics
3.2
Significance Level, Size and p-Values
3.3
The Power Function
3.4
Asymptotic Null Distributions
4
Ordinary Least Squares: The Classical Linear Regression Model
4.1
Finite-Sample Properties
4.1.1
The Algebra of Least Squares
4.1.2
Coefficient of determination
4.1.3
Finite-Sample Properties of OLS
4.2
Asymptotics under the Classic Regression Model
5
Monte Carlo Simulations
5.1
Review
5.2
Simple Simulation Example
5.3
Consistency
5.4
Asymptotic Normality
5.5
Testing
5.6
Regression
6
Nonparametric Regression
6.1
Polynomial Regression
6.2
Nadaraya Watson Estimators
7
High-Dimensional Methods
7.1
Subsection
8
How to Write
8.1
Five Common Writing Mistakes
8.2
Gregory Mankiw: How to Write Well
8.3
Rob J Hyndman: Avoid Annoying a Referee
8.4
LaTeX
9
How to Present
9.1
The Aim of your Talk
9.2
A Suggested Structure
9.3
Preparing Slides
9.4
Keeping to Time
9.5
Giving the Presentation
Final Advice
References
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Research Module in Econometrics & Statistics
7
High-Dimensional Methods
7.1
Subsection