## Econometrics HT 18

### by Lund University

This is NEKG31, Econometrics for students at Lund University fall 2018

## Chapter 1: Sample moments

We define a sample and define sample mean, sample variance and more.

## Chapter 2: Getting started with econometric software

Introduction to Eviews

## Chapter 3: Ordinary least squares, lectures

Fitting a straight line through a scatter plot

## Chapter 4: Ordinary least squares, problems

Problems related to the previous chapter

## Chapter 5: Deriving the OLS formula

Here we derive the OLS formula by minimizing the sum of squared residuals.

## Chapter 6: Measures of fit

We evaluate how well our straight line fit the scatter plot.

## Chapter 7: Random variables and distributions

We must understand random variables in order to understand the linear regression model.

## Chapter 8: Moments of a random variable

Expected value and variance

## Chapter 9: Moments of two or more random variables

Covariance and conditional expectations

## Chapter 10: The linear regression model, lectures

Introducing the most important model in econometrics.

## Chapter 11: The linear regression model, problems

Problems related to material in the previous chapter.

## Chapter 12: The properties of the OLS estimator, lectures

The OLS estimator has some nice properties in the linear regression model.

## Chapter 13: The properties of the OLS estimator, problems

Problems related to the previous chapter.

## Chapter 14: Some distributions

In preparation for inference in the LRM.

## Chapter 15: Inference in the linear regression model

Hypothesis testing and confidence intervals.

## Chapter 16: Several explanatory variables

A linear regression model where our dependent variable may depend on several x-variables.

## Chapter 17: Inference in the linear regression model with several explanatory variables

Hypothesis testing and confidence intervals.

## Chapter 18: Multicollinearity and forecasting

Two minor topics related to the LRM.

## Chapter 19: Nonlinear regression models

An introduction to non-linear models.

## Chapter 20: Logarithmic regression models

The most important class of non-linear models.

## Chapter 21: Dummy variables

When your explanatory variables are group belongings.

## Chapter 22: Dummy variables, problems

Problems related to previous chapter.

## Chapter 23: Heteroscedasticity

When the variance of the error term no longer is constant.

## Chapter 24: Heteroscedasticity, problems

Problems related to material from the previous chapter.

## Chapter 25: Endogeneity

Cases when the OLS estimator is longer appropriate.

## Chapter 26: Instrumental variables

Instrumental variables are used to handle endogeneity problems

## Chapter 27: Univariate time series models, lectures

We look at a single variable, such as inflation, when our sample is over time.

## Chapter 28: Univariate time series models, problems

Problems related to previous chapter.

## Chapter 29: Multivariate time series models, lectures

Time series models involving several variables.

## Chapter 30: Testing for unit root and cointegration

Testing for unit root and cointegration

## Chapter 31: Autocorrelation, lectures

Autocorrelation is a problem that may arise in time series models.

## Chapter 32: Autocorrelation, problems

Problems related to the previous chapter.

## Chapter 33: Models based on panel data

Data over time and cross section.

## Chapter 34: Fixed effects versus random effects

Two possible methods of estimating an error component model with panel data.

## Chapter 35: Panel data, problems

Problems related to the previous two chapters.