**ECON2228.04: Introduction
to Econometric Methods (Spring 2022)**

O'Neill 257, T Th (9:00 – 10:15)

Christopher Maxwell** **Maloney
Hall, 337

maxwellc@bc.edu Off. Hrs: by Zoom (tbd)

This is an introductory course in the use of econometric
methods, with an emphasis on empirical applications. Our focus will be on learning ** to do**
econometrics, not just learning about econometrics or econometric theory.

While the course will cover the development of the formal tools of econometric analysis (simple and multiple regression analysis, estimation, inference, categorical variables, functional forms and so forth), we will also spend quite a bit of time on empirical methods (posing questions, building datasets, running regressions, looking at results, supplementing datasets, running more regressions, etc … until we can confidently say something about the questions at hand). As such, an important part of the course will be a set of empirical exercises and an empirical research project in which students will be building their own datasets and applying the various econometric methods developed in the course.

**Prerequisites**:
An introductory statistics course such as EC 151. No exceptions. Students should also have an understanding of
basic *Excel* (which will be used at times to assemble datasets and verify
calculations) and basic calculus.[1] I also assume that you've had some previous
exposure to *Stata*, the computer language that we will be using to run
regressions (you should have seen Stata in EC151). (See far below for more about Stata.)

**Course reference text**: The course is built around a set of handouts,
which are posted to Canvas. To simplify
things for you, I have also self-published the handouts through Amazon. I have printed hardcopies for everyone
(those copies are __free__ to you and will be distributed in class). You can also order the book through Amazon. I
sell the book at my cost, which is $8.86 (it's 372 8.5 x 11 pages), and will
only make it available for you all for about two weeks or so. It should be available now.[2] I have also posted to Canvas the pdf for the
text.

**Some additional texts:
**There is no need to purchase any of these (most are available at
O’Neill). I list them because it is
sometimes useful to see different presentations of the material.

·
Jeffrey M. Wooldridge, *Introductory
Econometrics: A Modern Approach*, 7^{th} ed., Cengage Learning,
2019.

·
Angrist, Joshua D. and Jörn-Steffen
Pischke, *Mastering
'Metrics: The Path from Cause to Effect*, 2014.

·
Stock, James H. and Mark W. Watson, *Introduction
to Econometrics*, 4^{th} ed., Addison-Wesley, 2019.

·
Studenmund, A.H., *Using
Econometrics: A Practical Guide*, 7^{th} ed., Pearson, 2017.

·
Bailey, Michael A., *Real Econometrics: The Right Tools to Answer Important Questions*, 2^{nd}
ed., Oxford, 2019.

**Grading** (Exams: 75%; Research Project: 10%; Exercises/qFlips/Quizzes: 10%; Labs: 5%):

·
**Exams (75%):** Three exams:
Two mid-term exams and an __optional__ final exam.

Anticipated dates (these may change):

§ Mid
Term Exam #1 on Half #1 material; Thursday, March 3^{rd} : OLS Analytics and Assessment (this is the last class before Spring Break;
plan accordingly)

§ Mid
Term Exam #2 on Half #2 material; Thursday, May 5^{th} (last class in
the semester): OLS Estimation and Inference + Topics

§ Optional
Final Exam: Thursday, May 12^{th},
12:30 PM

For mid term exams, you are
allowed one *cheat sheet* (8.5 x 11 or
A-4) and the use of a calculator; for the final exam, you are allowed two *cheat sheets* and the use of a
calculator.

Exam weights and the optional final exam:

§ If you take the optional final exam: Each mid-term exam counts for 21% of your course grade, and the final exam counts for the remaining 33% (so the exam weights are: 21%-21%-33%).

§ If you decide not to take the optional final exam: Each mid-term exam counts for 37.5% of your course grade.

You must commit to taking the final exam at the time you receive the exam. To allow you to make a fully informed decision about whether or not to take the (optional) final exam, conditional course grades, which assume that you are not taking the final exam, will be posted to Canvas as quickly as possible after the end of classes. At that time , I'll be happy to provide you with a sense of how final exam performance will impact your course grade.

There are no make-up exams in this course. If you miss either mid-term exam, then you must take the final exam (exam weights will be adjusted proportionately). All exam grades are curved. While every exam is different, exam scores in general seem to average about 70% of total available points, which curves to about an 85 or so.

·
**Research
Project (10%):** Independent
replication of an econometric analysis that has been published in an academic
journal. More details below.

·
**Exercises/qFlips/Quizzes (10%):**
There will be four Exercises over the course of the semester. These are team projects, which you will
typically have two weeks to complete. As
well, there will be about ten quizzes or short online assignments (called *q(uick)Flips*),
which will typically follow each Unit. Course
grades for Exercises/qFlips/Quizzes will be curved, after
dropping the lowest score… it's OK to miss one; don’t miss two! More details below.

·
**Labs (5%):** Course-wide labs, focused on using Stata in
empirical/econometric analysis. If you
are not registered for a Lab, please do so… now! While I am always
happy to tackle Lab questions, you should know that the Labs are completely
independent of the 2228 courses.

**Canvas: ** Historically,
I did not use Canvas for the course, except to post course-related scores and
grades. All other course-related
materials were posted to my oh-so-spiffy (not!) course website: http://www.cmaxxsports.com/ec228
. (To access the website, just google *ec228*.)
With the move to *OnlineU*,
however, I migrated all course content from my website to Canvas. So while we will use the website a bit (for qFlips), almost all of the course content is now on the courses'
Canvas website. Do __not__ expect my
ec228 website to be up-to-date or complete.

**Accommodations**:
If you are a student with a documented disability seeking reasonable
accommodations in this course, please contact Kathy Duggan (x2-8093;
dugganka@bc.edu) at the

**Academic Integrity:**
You will be held to Boston College’s standards of academic integrity. If you have any questions as to what that
means, see BC's academic integrity policies webpage.[3]

**Pass/Fail**: It is perfectly fine, of course, to take the
course Pass/Fail… but it is definitely not OK to do so and shirk on group
projects/exercises. That is not fair to
your teammates… and they will come to hate you!
Accordingly: If you are taking
the course Pass/Fail, please let me know at the start of the semester, and I
will monitor goings-on and make adjustments if necessary.

** The Research
Project** (10% of your course
grade): This is an applied/empirical
project, which will kick off with team assignments at around the time of the
first mid-term exam. In the past, there have been two phases to
the project: Phase I – Independent Replication and Phase II
- Improvement. However, this semester I'm
dropping Phase II, and so the Project is now focused solely on independently replicating
an existing published piece of econometric analysis… of your choosing. [4]

**1. ****Team
assignments:** The Research Project kicks off with team assignments
around the time of the first mid-term exam.
I will assign the teams, which will likely have three members each.

·
** Kickoff/Team assignments:**
Project teams assigned around Mon. March 14

**2. ****Topic
selection:** Topics should showcase interesting
econometric analysis, and need not be restricted to topics in Economics. It’s important to get an early start, as
empirical research is always slow going!.
To help you in that regard, I’ll ask teams to email me a one paragraph
description of their topic/paper of interest, within two weeks of team
assignments. I will compile and
circulate those blurbs and we'll discuss them in class at some point.

·
** Topic
selection:** Due around Weds. March 30

**3.
****Independent replication:**** **Independently replicate both the summary
statistics of interest presented in the paper (to show that you have indeed
replicated the construction of the dataset) as well as at least one set of
regression results of interest.

Your end-of-semester
deliverable will be a *PowerPoint*
presentations (or the equivalent), which should be concise and to the point; ** Shorter is always better**. I will say
more about the format of the deliverable when teams are assigned.

Your PowerPoint presentation
should discuss your data sources in detail and how your dataset was
constructed. Credit will reflect in part the level of difficulty.[5] In some cases you may be able to obtain data
from the original authors, which obviously greatly simplifies (trivializes) the
replication phase. But that is not *independent replication*. You can do that if you want, but your grade
will suffer mightily (since building datasets is hard work, and copying and
pasting is not). If you do work with the
authors' data and/or programs, be sure to give them full credit for such. Not doing so is plagiarism.

At
the end of the semester teammates will assess their own and each other's
performance using the posted *Peer
Evaluation* form (see posting to Canvas).
Students’ grades will reflect both their individual performance as well
as the quality of the final team submission.

·
** Independent
Replication: **Due around Fri., April 29

**Leave
plenty of time for Replication. **You’ll find this far more challenging and
time consuming than you could ever imagine.
And yes,

** Shirkers
take notice... ** I repeat:

*Empirical work is slow going.
Be sure to leave yourself enough time to complete the assignment to your
satisfaction.*

** Exercises/qFlips/Quizzes** (10% of your course grade; grades on
Exercises/qFlips/Quizzes are curved):

*Exercises***: **There will be four or so empirical exercises, which
together with *qFlips*
and Quizzes (see following) count towards 10% of your course grade. Exercises will be** **team assignments,
usually with two students per team, and are graded on a 10 point scale. I will assign teams, which will differ from
Exercise to Exercise. Answers are
submitted online (one submission set per team; as well, one set of snapshots
uploaded to Canvas).

In some cases, the
Exercises are designed to give you practice with the techniques and tools we
have developed in the course… other
times, they are designed to introduce you to new material, which we have not
yet covered in the course. These will
take some time to complete, so please do not leave them until the last moment. A good rule of thumb is that Exercises will
take about a week to complete… so budget your time accordingly (no sympathy for
teams getting a late start).

*qFlips***: **We will have about a half dozen custom
tailored *quickFlips*
this semester. They will
typically be self-paced *online* assignments, and
are designed to give you some rudimentary practice with concepts and
applications that we are covering in the course. Some of these will be team assignments; I will assign teams, which will typically
have two members.

*Quizzes***:** There
will also be three or so short Canvas quizzes over the course of the
semester. These will be multiple choice
exams and typically feature about eight questions. Quizzes are to be completed individually
(though they are open book; open notes; open lifelines; open etc etc).

Each qFlip and Quiz is graded on a two point
scale (1/5^{th} the value of an Exercise). I anticipate close to perfect scores on these
as they are primarily designed to reinforce the learning of the Unit material
(you have an unlimited opportunity to revise/update your qFlip
answers).

** Grading**: I will drop your lowest qFlip/Quiz
score, just in case you inadvertently
miss a deadline; but do not miss two deadlines! These scores (max: two points
each) are then added to your Exercise scores (max: ten points each), and the
total is scaled up to a maximum of 100.

**Course
Topics** (I have posted (see Canvas) course notes/handouts
for each Unit… and slideshows and videos too! As well, you may want to consult the
Wooldridge text.)

**Introduction**

*Unit 1 – Introduction
& Getting Started*: Estimating the relationship between x
and y; causality v. correlation; data types; economic v. statistical
significance; robust analysis (how many regressions did you run?); art v.
science; sample statistics (sample means, variances, standard deviations, covariances, and correlations); standardizing data; OLS as
minimization of SSRs (FOCs and SOCs)

**Simple Linear Regression
(OLS/SLR) Models**

*Unit 2 – OLS/SLR
analytics* (single explanatory variable):
*In the beginning* (SLR.1: the data generation process); residuals
and sum squared residuals (SSR); OLS, FOCs and SOCs, Sample Regression Function
(SRF), predictions and residuals; economic significance/meaningfulness
(elasticity and *beta* regressions)

*Unit 3 – OLS/SLR
assessment*: Sum Squared Explained
(SSE) and Sum Squared Total (SST); SST = SSE + SSR (w/ constant term in the
model); Goodness of Fit (GOF) metrics - Coefficient of Determination (R^{2}),
Mean Squared Error (MSE) and Root MSE (RMSE); comparing SLR models using GOF
metrics

**Multiple Linear
Regression (MLR) Models**

*Unit 4 – OLS/MLR
analytics I *(adding, and subtracting, explanatory variables): Comparing SLR and MLR analytics; interpreting
coefficients I – *ceteris paribus* (partial effects and the SRF); interpreting
coefficients II – partial correlations (*WhatsLeft* and W*hatsNew**)*; an
overview of omitted variable bias (endogeneity)

*Unit 5 – OLSMLR
assessment*: Comparing SLR and MLR
assessment (GOF metrics); shortcomings of R^{2}; adjusted R^{2};
comparing MLR models using GOF metrics

*Unit 6 – OLS/MLR
analytics II*: The *collinearity* regression; multicollinearity, R^{2}_{j}'s and Variance
Inflation Factors (VIFs); Omitted variable bias/impact (endogeneity);
simple v. partial correlations

*Mid-Term Exam #1 (about here)*

*Way-Too-Fast* Review
of Statistics

*Unit 7 – Review of Estimation
and Inference*: Our focus will be on
estimation of the population mean; LUEs (Linear Unbiased Estimators); BLUEs
(Best Linear Unbiased Estimators); point and interval estimators; standard
errors, t statistics, p-values; confidence levels; critical values; confidence
intervals; hypothesis testing; significance levels

**Estimation and Inference in Regression Analysis**

*Unit 8 – SLR Estimation*: Gauss-Markov assumptions
(SLR.1 – SLR.5); Population Regression Function (PRF); conditional means;
means, variances, standard deviations and standard errors of OLS estimators
(intercepts and slopes); unbiasedness (OLS coefficients; MSE); LUEs; homoskedasticity;
*BLUE*: The Gauss-Markov Theorem

*Unit 8a – Heteroskedasticity*: Issues (OLS standard errors no longer
correct; LUE but not BLUE); White-corrected standard errors (*robust *inference); working towards BLUE
(weighted least squares… but where do those weights come from?)

*Unit 9 – SLR Inference*: Add SLR.6 to the mix; normally distributed
errors; variances, standard deviations and standard errors; t statistics;
t-tests (Null hypotheses); p values; confidence intervals; hypothesis tests;
economic v. statistical significance (elasticities v. p-values); *Convergence I* (t stats and R^{2})

*Unit 10 – MLR Estimation and Inference*: Compare
to SLR; *What's new? … Not much!*; now MLR.1-MLR.5; n-k-1; multicollinearity,
standard errors and Variance Inflation Factors (VIFs); MLR.6;
heteroskedasticity and *robust*
standard errors

**Topics**

*Unit 11 – Dummy Variables and Fixed Effects*: Dummies revisited; on the RHS and on the LHS;
uses on the RHS (slope and intercept dummies); quieting the endogeneity
critics (fixed effects); Examples (sovereign debt ratings, gender bias in
wages, and death penalty deterrence)

*Unit 12 – F-tests and Convergence*: Extension of t-tests to more complicated null
hypotheses; testing linear restrictions with the F(q, n-k-1) distribution; *Convergence II*: connects Goodness-of-Fit metrics and inference
stats (t stats and incremental R^{2}, SSR and SSE); reported F stats (for
the regression) and associated p values; relation to adjusted R^{2}; *Babies and bathwater*; Chow tests;
machine learning

*Mid-Term Exam #2 (about here)*

**Further Topics**

*Unit 13 – Linear Probability Models (LPMs) and
Functional Forms*: Dummies on the
LHS; Linear Probability Models (LPMs); exploring functional forms (quantile dummies;
linear splines; logarithms and exponentials; polynomials; cubic splines, and
fixed effects)

*Unit 14 – Further Topics I*: *Differences-in-Differences*
(*Deflategate*;
NBA Referee Own-Race Bias); *Regression
Discontinuity Designs* (Highway Fatalities & Daylight Savings Time); *Instrumental Variables* (The Oregon
Health Insurance Experiment (Medicaid)); Maximum Likelihood Estimation (MLE);
limited dependent variables; logit and probit models;
censored and truncated regression models

*Unit 15 – Further Topics II*: OLS asymptotics (large
sample properties; consistency; convergence in distribution); misspecified models; proxy variables; missing data;
outliers; non-random samples; forecasting and *prediction* intervals;

**Stata @ Boston College**

There are a large number of statistical software packages that
you can use to do econometric analysis.
We will use Stata, one of the more popular packages and the package that
receives the most support at

I will be providing more details as the semester develops, but
for now: Stata is available to BC
students through the “application server”, known as the *apps server*.. In the past
you've needed Citrix Receiver installed on your computer to access the apps
server (and if you were not connected to the BC network, you also needed to use
VPN to access the apps server). But
those days are over. You can now use the
"Light Version" of Citrix Receiver to directly access the apps server
(no need to install the Citrix Receiver or VPN.) To learn more, go to: https://www.bc.edu/content/bc-web/offices/its/support/software-hardware/apps.html
.

Alternatively, and to avoid traffic jams with Citrix and the
apps server, you may want to purchase a six-month *Stata/BE* license for $48.
For details, go to: https://www.stata.com/order/new/edu/profplus/student-pricing/

We will devote some time to learning how to use Stata to build datasets and run regressions. You will discover that building datasets is long, hard, tedious and unrewarding work… and running regressions is relatively quick and easy… and a lot more fun!

**Stata Resources **

As the semester progresses, you may find the following resources of interest:

·
Encountering Stata questions/issues/features?…
just *Google* it (always include “UCLA”).

And here are a few sites that might be helpful (the pdfs are posted to the course website):

· http://fmwww.bc.edu/GStat/docs/StataIntro.pdf

· https://stats.idre.ucla.edu/stata/modules/

· http://dss.princeton.edu/training/StataTutorial.pdf

· https://stats.idre.ucla.edu/stata/

· … and don’t forget YouTube: https://www.youtube.com/results?search_query=stata

Examples and datasets (**bcuse**
may be helpful here… I’ll explain in class):

· http://fmwww.bc.edu/gstat/examples/wooldridge/wooldridge.html

· http://fmwww.bc.edu/ec-p/data/wooldridge/datasets.list.html

· http://fmwww.bc.edu/ec-p/data/ecfindata.php (link down at the moment)

· https://stats.idre.ucla.edu/other/dae/

· https://stats.idre.ucla.edu/other/annotatedoutput/

Also: Ben Lambert’s *full course in econometrics* videos are
terrific and come with a *British accent*!

· https://www.youtube.com/user/SpartacanUsuals/playlists

[1] The course website and Canvas site contain a few links to online materials and tutorials.

[2] Let me know if you'd like a copy and are not able to purchase it through Amazon, and we can discuss shipping options.

[3] https://www.bc.edu/content/bc-web/academics/sites/university-catalog/policies-procedures.html#academic_integrity_policies

[4] Published here means published in an academic journal (so no unpublished senior theses, web blogs, or the like).

[5] If you
want a sense of *degree of difficulty*, just ask.