Econonmetric Methods, part I. (Foundations of econometrics and time series)
Teacher:  Helgi Tomasson,

The goal of this course is twofold first to enlighten the foundation for econometrics, probability. theory and statistical inference and second to introduce practical econometrics.

This course contains a) the first two weeks of a brief overview of probability theory and statistical inference b), the 6 weeks of classical  econometrics with emphasis on time series econometrics.

Students are expected to do computer exercises (RATS MATLAB EVIEWS or any software that you prefer) and written exercises. There will be a 3 hour written exam in part I. The exam accounts for 70% of the overall evaluation for the course the remaining 30% will be based on the exercises.

Text books

The main text of the cours will be:

Paul Ruud:  Classical Econometric Theory,  Oxford University Press, 2000.

The teacher may distribute some additional material

Some other useful texts are:

For the overview of statistical theory books on probability, statistical inference or general statiststics

will do. For probability theory see, e.g.

A usefule text for review of econometrics is: For students in economics the book by Whittle, P.:Probability via Expectations where the probability theory is derived from expectations might be of interest. Other books with probability, statistics, econometrics can also be useful.

For statistical inference see, e.g.

Many econometric textbooks, like e.g. Davidson, R. and McKinnon, J.,G.: Estimation and Inference in Econometrics, contain a good overview of the theory of statistical inference.

Other books on probability or statistics may be useful such as books with time series in the title.

Week 1-2: General probability theory, events, outcomes, independence, random variables, distributions, limit theorems, etc.

Week 3-4: Statistical inference, likelihood, estimation, point estimates, confidence intervals, testing hypotheses, asymptotics etc.

Week 5:  Introduction to linear models

Week  6: Generalization of linear models

Week 7: Introducing time series, stationarity, time domain, ARMA and ARIMA.

Week 8: Estimation and testing in ARMA models.

Week 9: Modelling economic time series.

Week 10. Exogeneity, non-stationary models, cointegration.

Sample programs for ARIMA analysis and ARCH

To run the Johansen co-integration regression, use CATS in RATS. Unfortunately I have not been able to find the new version on our network. An example of how to use the old code is here .
Some links:

A matlab(octave/scilab) code that generates random walk is here .

A matlab(octave/scilab) starter-code to do Johansen cointegration is here .