Advanced econometrics

by Lund University

This is NEKN31, Advanced Econometrics for students at Lund University

Chapter 1: Mathematics

All the mathematics that we need to get started. Most of this will be known to you. We begin by reviewing matrix algebra and functions of several variables. We then look at some multivariable calculus. In particular, how to differentiate a function with respect to a vector. We apply these derivatives to linear functions and quadratic forms.

Chapter 2: The algebra of the linear regression model

This chapter is about the linear regression model

Chapter 3: Probability theory

All the probability theory that we need for this course

Chapter 4: Statistics

The final chapter of this course is an introduction to statistics. The basic idea of statistics is to make inference about a population given a sample drawn from this population. We begin by translating the population/sample concepts into a framework consistent with formal probability theory in section one. Section two looks at some important distributions that we often end up using in statistics. Section three looks at the simplest problems in statistics, making inference about the mean and the variance. We then move on to a more general study of statistics, introducing estimators and their small-sample properties. The following section is devoted to the more important, but also more difficult, large-sample properties of estimators. The final section will generalize what we have done so far in this chapter by looking at several estimators collected in a vector.

Chapter 5: The statistics of the linear regression model, small samples

The statistics of the linear regression model, small samples

Chapter 6: The statistics of the linear regression model, large samples

The statistics of the linear regression model, large samples

Chapter 7: The linear regression model - topics

Some additional material on the linear regression model. This chapter is based on chapter 3 of Verbeek. Most of this is likely known from before. See Lecture 6 Part 2.

Chapter 8: Heteroscedasticity


Chapter 9: Endogeneity and instrumental variables

Endogeneity and instrumental variables

Chapter 10: Method of moments and GMM

Method of moments and GMM

Chapter 11: Maximum likelihood

Maximum likelihood

Chapter 12: Models with limited dependent variables

Models with limited dependent variables

Chapter 13: Univariate time series

Univariate time series

Chapter 14: Time series models

Time series models

Chapter 15: Panel data

Panel data