Assessment Materials in Econometrics
A freely usable library of standardised assessments for economics students, with an option to deliver the assessments online and have a summary report emailed to the lecturer. These are designed as "an objective, repeatable measure of what your students know about a particular subject" and so support testing of prerequisites for a course and enable pedagogical research.
Online introductory course based around 20 videos, each with separate transcripts and download links, gradually being added as of early 2021. Each clip has self-assessment questions. Covers "Ceteris paribus, selection bias, randomized trials, regression, instrumental variables, regression discontinuity, diff-in-diff, and more."
Subscription site with more than 900 quiz questions on introductory econometrics, especially Stata software. The questions come with links and references for further study. A campus licence allows instructors to create and administer customised quizzes, searching by keyword or specifying questions by textbook and chapter. For free, you can try out a quiz of ten randomly-selected questions.
Slides, lecture notes and assignments from a 2019 course. Some of the documents are available via a GitHub repository
Reading assignments, lab exercises, and quiz questions from an undergraduate course given in Autumn 2018
One hundred and one multiple-choice questions from a legacy course that have been shared on GitHub. Answers can be viewed by replacing the filename FinalExam.html with FinalExam-Answers.html
Archived course materials from a 2011/12 module for year 2 undergraduates, including slides, 20 PDF lecture handouts, and exercises.
Materials from a 2011/12 course for year 2 undergraduates with an existing grasp of matrix algebra and rudimentary statistical inference. The module aims "To deepen and consolidate knowledge of probability and statistics, with a focus on sampling and inference, as they pertain to Econometrics." Ten lecture handouts, separate lecture slides, and some assessment materials, all in PDF format.
Six lecture PDFs, plus supplements and exercises, from an introductory Econometrics course. Topics include Matrix Algebra, Elementary and Multiple Regression, Serially Correlation Regression Disturbances, and Dynamic Regressions, totalling 75 pages of material.
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.
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.
This webpage provides ten multiple choice questions for introductory econometrics, written by Guy Judge of Portsmouth University. The quiz is hosted by the Quia service, which allows academics to add their own quizzes by subscription. Marking and feedback on the correct answers is provided.
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.
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.
