Semester 1

Credits 6

Contact: Prof. J.R. Hudson:

Level: 3

Assessment: Exam 100%


The aim of the course is to introduce the student to econometrics enabling him/her to use STATA, a widely used computer package program, as well as briefly introduce them to RATS, another program. They will also become confident with the use of matrix algebra which is very much the language of econometrics. The course is a difficult and demanding one. But econometrics is one of the foundations of the modern economists toolkit and you cannot be an economist today or do postgraduate work without an ability to use econometrics. At Bath it is taught well with greater emphasis on applied work than elsewhere. The techniques are also widely used in other disciplines including management, statistics and biological sciences.


You will cover a greater range of issues than in most other Universities. The topics covered include ordinary least squares (briefly as already covered previously), measures of goodness of fit, simultaneous equation methods and problems (including weak instruments), serial correlation +  heteroscedasticity + GLS, errors in variables, measurement errors, stationarity and cointegration, maximum likelihood and limited dependent variables. In addition, the following books will be of use:

Key Texts:

1. William Greene, Econometric Analysis, Prentice Hall, is rapidly becoming something of a standard.

2. Johnson & Dinardo - Econometrics, McGraw Hill, paperback available. The classic text, excellent for matrix algebra and very much the text on which this and many other courses are based, although as I say Greene is probably now regarded as the standard.

3. Pindyck and Rubinfeld, Econometric Models and Economic Forecasts. Excellent McGraw Hill text, uses matrix algebra, but not to the extent of Johnson. The best reference for ARIMA modelling

4. Cuthbertson, Hall and Taylor, one of the main textbooks to deal with the `new' approaches to econometrics, which in this course means stationarity and cointegration, paperback available, will shortly be in library.

5. Enders, W. Applied Econometric Time Series, Wiley and Sons. Also good for time series analysis. Heavily linked to RATS.

McFadden’s Book (an actual online book with material which is relevant for parts of the course):




Resources for links to the RATS website including programs which can be downloaded and further links to other relevant websites.

This gives a page reviewing econometric software, including e.g. the use of EXCEL to do empirical work.

The following gives a link to the Econometrics Journal on line, with lots of further links to econometricians, software and data sources:

Also look at the American Economic Associations webpage for links on data, software, etc:

A guide to econometric software:

The Centre for Microdata Methods and Practice (particularly resouces and Greene’s lecture notes):



The course consists of a series of two hour lectures + virtual classes on use of package programs + classes in the computer lab.

Topic Plan

Topics will not necessarily correspond to weeks. Some topics, particularly those with which the student will have met previously,  will be dealt with more quickly than others.

For a resume on what you should know from early lectures see:

Week 1: Revision of Matrix Algebra and ordinary least squares & OLS in matrix form. algebra.doc Lecture.doc differentiation.doc

In this lecture too we will begin to work with STATA. This – or on occasion RATS – will be a regular feature of the lectures

Week 2: Further revision + new material: Goodness of fit, including F tests, ARCH, Jarque-Bera, Ramsey Reset,

Week 3: Serial Correlation & simultaneous equations, revisited, the GLS estimator all with the emphasis of matrix algebra.

A new revised lecture on serial correlation and then the original, followed by one on simultaneous equation bias. lecture serial correlation.doc                                                                                                                         

Week 4: Monte Carlo Simulation

See too a video of this lecture:

Week 5: Errors in variables, measurement errors, GMM estimators and instrumental variable estimation          

Week 6: Seemingly Unrelated Regressions

Week 7: Maximum Likelihood Estimation.

Week 8: Cramer Rao Theorem.

Week 9: Binomial probit & logit

Week 10: Further developments in Stationarity, Cointegration & Error Correction Models: Stationarity.doc    also for an interview with Engle and Granger as they won the Nobel Prize, discusses their work: PLEASE SEE TYPED NOTES FOR THE UPDATED LECTURE. ALSO for something on Phillips-Perron tests: Perron.pdf



Week 11: The Problem of Weak Instruments: Instruments.doc


TYPED LECTURE NOTES: Lecture 2 OLS.pdf Lecture 3 F test.pdf Lecture 4 Serial Correlation.pdf Lecture 6 Stochastic Regressions.pdf Lecture 7 Seemingly_Unrelated Regressions.pdf Lecture 8 Maximum Likelihood.pdf Lecture 9 Probit & Logit.pdf Lecture 10 Trend Stationarity.pdf Perron.pdf Lecture 11 Weak Instruments.pdf



Classes: will focus on STATA

For a video introduction see:

And for the programs in the above in a WORD file see:


Notes on STATA see:

For notes on STATA see:


For notes on RATS see: ALSO For examples of RATS programs which you are to block copy and run see: