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.