Discrete choice methods with simulation is an online text written by Kenneth Train of University of California, Berkeley in 2003. It covers topics such as numerical maximization, simulation assisted estimation and Bayesian procedures. Each chapter is available as a PDF file to download, and the site also provides an index, bibliography and errata discovered since publication. Users can also download the whole text as a single zip file.

# Online Text and Notes in Econometrics

This page has a great deal of illustrated text on optimization and linear programming with many related external links. It has been produced by Hossein Arsham of the University of Baltimore.

Last revised in January 2013, this is a thorough and technical 346-page textbook.

Materials from a course taught in Spring 2001, including a syllabus, video lectures (in quite a low resolution) and an electronic version of the 2009 textbook Discrete Choice Methods with Simulation. Topics include Advantages and Limitations of Logit, Numerical Maximization, and Hierarchical Bayes

This is a short, very introductory article, spread over four pages, which takes the reader through a simple regression test in Excel. The given example involves testing whether Okun's Law applies to US data, and there is a downloadable Excel file used in the exercise.

This archived page from a 2000 short course has a series of lengthy bullet-point lecture handouts in PDF format. There is also a directory of data files and programs for the proprietary program SST. Kenneth Train, Daniel McFadden of University of California, Berkeley

The Nobel Foundation makes available a great deal of material on each of the Economics prize winners, including video of each Prize Lecture since Robert Mundell in 1999. As well as a lay introduction to each prize winner's research, there are "Advanced information" links giving a more technical explanation. This link is to the Economics Network's quick index of lecture videos and related materials on the site. Each video is a full lecture (usually between 40 and 60 minutes) with good audio and video quality, and pitched at a non-technical audience. Transcripts of each lecture are available.

Subtitled "A guide for selecting statistical techniques for analysing survey data", this presents a tree of choices about your data and the hypotheses being tested, resulting in a recommended statistical test. It was created by the authors of the MicrOsiris statistical software. It is freeware that is downloaded with MicrOsiris.

Freely downloadable as a 374-page PDF, this manual shows students how to use Gretl software to reproduce all the examples from Hill, Griffiths, and Lim's Principles of Econometrics, 3rd edition (Wiley). The data sets and script files used in the book are also freely downloadable. The current version dates from January 2009.

Lecture notes totally 163 pages from a 2000 short course are available here in PDF format. Most of the text refers to exercises using the accompanying TSP 4 and Excel files.

This is a course website for Introductory Econometrics as taught by Mike Abbott at Queen's University, Kingston (Australia). It includes extensive course materials, lecture notes, statistical tables, datasets and assignments and a number of past exams, going back to 1997, some with answers in separate files.

This Spring 2000 course page has brief notes from a series of 23 lectures on Economics, statistics and econometrics as taught by Andrew K. G. Hildreth of University of California, Berkeley.

Available are notes from lectures, problem sets, and a sample exam. Lecture topics are: Discrete Response Models, Sampling and Selection, Generalized Method of Moments, Instrumental Variables, Systems of Regression Equations, Simultaneous Equations, and Robust Methods in Econometrics. From an Econometrics / statistics course as taught in 2001.

From a Summer Institute mini-course run by the National Bureau of Economic Research in 2007, this is a set of resources from each of 15 lectures, including video (usually 1hr long and hosted on Google Video) as well as handouts and slides. This link goes to Economics Network's index of these materials.

This 140-page book, originally published in 1971 but now out of print, is available online in its entirety as a single PDF file.

Thirteen PDF files on this page each contain detailed lecture notes on: Sets and Subsets, Numbers, Limits, Derivatives, Maxima and Minima, Geometric Growth, Taylor's Theorem, The Binomial Theorem, Exponential and Logistic Growth, Bivariate Optimisation, Constrained Optimisation, Matrix Algebra 1, and Matrix Algebra 2.

These pages give materials for the BA degree honours year Applied Econometrics class at the University of Strathclyde as taught by Roger Perman. Lecture notes and references notes in Word format are listed on this course home page. It covers topics such as Dynamic Econometric Modelling, Model misspecification and misspecification testing, Stochastic regressors, instrumental variables and weak exogeneity and Panel data analysis.

This 1998 course page has seven sets of extensive lecture notes totalling more than 160 pages of explanatory material. There are also seven quizzes, also in PDF and PostScript formats. The course is an Introduction of Econometrics / Statistics as taught by Daniel McFadden, James Powell at University of California, Berkeley.

Bayesian statistics and its application to econometrics - lecture slides and notes. The Powerpoint presentation comprises nearly 100 slides.

Fifteen detailed lecture handouts in PDF are archived here along with 11 exercise sheets with answers. The lecture topics are: Sets and Boolean Algebra, The Binomial Distribution, The Multinomial Distribution, The Poisson Distribution, The Binomial Moment Generating Function, The Normal Moment Generating Function, Characteristic Functions and the Uncertainty Principle, The Bivariate Normal Distribution, The Multivariate Normal Distribution, Conditional Expectations and Linear Regression, Sampling Distributions, Maximum Likelihood Estimation, Regression estimation via Maximum Likelihood, Cochrane's Theorem, and Stochastic Convergence.