Econometrics Messages

Project Proposals

(posted: 24 Mar 2023)

Please bring a (printed) project proposal to class on Mon 27 Mar. The idea is to: "start writing when you know less." The process of writing will help you learn what you need to know.

Your proposal should describe the dataset (or datasets) that you plan to work with. Cite sources! And your proposal should present summary statistics from that dataset.

Most importantly, your proposal should describe a null hypothesis that you'd like to test or a time-series that you'd like to forecast. This section of your proposal should consider the ways in which your data may violate the Gauss-Markov assumptions and how you plan to account for those violations.

And this week, we'll also continue our discussions of heteroskedascity and probability models. To prepare, please read Studenmund, chaps. 10 and 13 and Kennedy, chaps. 8, 16 and 17.

Midterm and Projects

(posted: 09 Mar 2023)

When we meet on Mon 13 Mar, we'll conduct a midterm exam. The format will be "written-oral." So please prepare written answers to the "review for first exam" questions in the course syllabus. And please bring your written answers to class on Monday. The oral component will check your comprehension.

Then on Wed 15 Mar, let's dive into your projects. Think of a null hypothesis that you would like to test. Or think of a time-series that you would like to forecast. What's most important is to pick a project that you find interesting.

And try to collect some data. Two good sources of economic data are: stats.oecd.org and FRED. Bring your spreadsheets to class on Wednesday. And we'll discuss them then.

Projects and Gauss-Markov

(posted: 05 Mar 2023)

We will not meet on Mon 06 Mar. When we resume on Wed 08 Mar, I will ask what econometric analysis you would like to conduct for your course project. As a starting point, I have prepared a few datasets. Those are only suggestions. Please pick a project that you find interesting.

Also on Wed 08 Mar, we will conclude our discussion of the Gauss-Markov assumptions. To prepare, please read Kennedy chaps. 5, 6 and 7.

And please also prepare answers to the "review for first exam" questions in the course syllabus. We want to push through the midterm exam quickly because you'll learn the most by working on your project. So let's knock the exam out quickly and focus on the course projects.

Gauss-Markov and Panel Data

(posted: 24 Feb 2023)

On Mon 27 Feb, we'll discuss the Gauss-Markov assumptions. To prepare, please read Kennedy chaps. 5, 6 and 7.  Then on Wed 01 Mar, we'll discuss panel data. To prepare, please read Studenmund chap. 16 and Kennedy chap. 18.

Hypothesis Testing

(posted: 18 Feb 2023)

On Mon 20 Feb, we'll finish our discussion of maximum likelihood and the problem set. And we'll begin a discussion of hypothesis testing.

To prepare, please read Studenmund chaps. 4 and 5 and Kennedy chap. 4. And in anticipation of our discussion of the Gauss-Markov assumptions, please also read Kennedy chaps. 5, 6 and 7.

materials on Canvas

(posted: 05 Feb 2023)

Please excuse me for the delay in posting materials to Canvas. The page at my teaching site, doviak.net/courses/metrics, will serve as the primary source of course material, but I have also uploaded some very helpful files to Canvas for you too.

On Mon 06 Feb, we'll continue our discussion of ordinary least squares and the problem set. To assist you, I have provided my answers to #4 and my answers to #1.

To prepare, please read Studenmund chaps. 1, 2 and 3. And in anticipation of our discussion of maximum likelihood, please also read Kennedy chaps. 1, 2 and 3.

wxMaxima installation

(posted: 27 Jan 2023)

As we discovered, the security features of macOS quarantine wxMaxima, preventing its installation. To lift the quarantine:

xattr -d com.apple.quarantine $HOME/Downloads/Maxima-5.43.0-VTK-macOS.dmg

For more details, see the wxMaxima installation instructions by Univ. of New Haven.

First Sessions

(posted: 23 Jan 2023)

On Mon 23 Jan, we began discussing my Analysis of the "Biagi Law", which I'll use as an example of how to measure the effect of a policy change. To explain my choice, I wrote a page about Teaching with the "Biagi Law" Data. We'll wrap up that conversation on Wed 25 Jan, then review some background on statistics and probability.

Then on Mon 30 Jan, we'll begin discussing ordinary least squares. To prepare, please read Studenmund, chaps. 1, 2 and 3. And please begin studying the problem set. We'll discuss problem #4 first, then return to #1. And to assist you, I have provided my answers to #1.

Finally, for future reference (in case you need it), I keep old messages on the message board page. I hope this way of organizing the course helps.

Welcome to Econometrics

Welcome to Econometrics. This website helps me organize the course. I hope you find it helpful.

I have posted a copy of the syllabus. Please review it and the materials listed on the metrics page.

textbooks

The textbooks that we will use are A.H. Studenmund's Using Econometrics: A Practical Guide and Peter Kennedy's A Guide to Econometrics.

To get the best deal on the Studenmund textbook, compare prices at Amazon and Alibris. And go ahead and buy a used copy. Then use your savings to buy a used copy of Kennedy's Guide. To get the best deal, compare prices at Amazon and Alibris.

let's get started

This course builds on the measures and hypothesis tests that you learned in statistics, using linear regression to measure each source of influence on an dependent variable. Then this course explores different types of datasets, how they may violate the regression model's assumptions and how to account for those violations.

For in-class examples of regression analysis, this course explores the effect that employment protections have on employment outcomes. By focusing on one single dataset, we cover a lot of econometric theory.

But to learn econometrics, you must do econometrics, so this course provides a few datasets and some null hypotheses to help you learn how to conduct an analysis of the data. And to help you more, one student produced a video of me exploring a dataset. I hope you enjoy it.

I'm looking forward to working with you this semester.

Sincerely,
- Eryk Wdowiak