**EC 228** – **Econometric
Methods (Spring 2020)**

.02: T Th (9:00 – 10:15) & .03: T Th (12:00 – 1:15)

Stokes Hall 295S

Christopher Maxwell** **Maloney
Hall, 337

maxwellc@bc.edu Office Hrs: T W 2-4

http://www.cmaxxsports.com & by arrangement

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 econometrics.

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, 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 below for more about Stata.)

**Course reference text** (recommended, but not required):

·
Jeffrey M. Wooldridge, *Introductory
Econometrics: A Modern Approach*, Cengage Learning.

I have deliberately not listed the edition; if you decide to
purchase the text, feel free to buy the 2^{nd} (2002), 3^{rd}
(2006), 4^{th} (2008), 5^{th} (2012), 6^{th} (2015) or
7^{th} (2019) editions. A copy
of the text will be placed on reserve at the O’Neill Library.

**Important Note: ****I do not
follow the text closely. I will be
distributing lecture notes for most of the material covered in class.**

**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.

·
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, 2018.

·
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.

·
Ashley, Richard, *Fundamentals of Applied
Econometrics*, Wiley, 2012.

**Grading** (Exams: 75%; Research Project: 10%; Labs: 7.5%;
Exercises/qFlips: 7.5%):

· Three exams (75%): Two mid-terms and one optional final exam

§
MT #1: Thurs, February 27^{th} (just
prior to Spring break) OLS Analytics and
Assessment

§
MT #2: Thurs, April 23^{rd} (next to
last week in the semester) OLS Estimation
and Inference + Topics

§
Optional Final: 02: Weds. May 6^{th} @ 9
AM, or perhaps 03: Sat May 9^{th} @ 9 AM

All exam grades are curved.

Exams are *open
cheat-sheet* (one cheat sheet (both sides) allowed for each mid-term; two
cheat sheets allowed for the final exam).

*Optional Final Exam*:

- If you decide to take the optional Final Exam, then each mid-term exam counts for 20% of your course grade, and the final exam counts for the remaining 35% (so the exam weights are: 20%-20%-35%).

- 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 pick up that 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.

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).

· Research Project (10%): Replicate and improve upon an econometric analysis that has been published in an academic journal. More details below.

· EC 227 Labs (7.5%): Course-wide labs, focused on using Stata in empirical/econometric analysis. Lab scores will be curved.

·
Exercises/qFlips
(7.5%): There will be four or five
Exercises over the course of the semester.
As well, there will be a five or six *q(uick)Flips* (these are typically online
assignments). Course grades for
Exercises/qFlips will be curved. More details below.

**Canvas: ** Historically,
I have not used Canvas for the course, except to post course-related scores and
grades. Instead, all course-related
material (other than scores and grades) was posted to the oh-so-spiffy (not!) course
website: http://www.cmaxxsports.com/ec228
. (To access the website, just google *ec228*.)
Starting this semester, however, I am hoping to post all material to
both the course website and to Canvas.
This will be a work in progress.
Wish me luck!

**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.[2]

**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.

** Research
Project** (10% of your course
grade): This is an empirical project,
which will kick off with team assignments after the first mid-term exam. There
are two phases to the project:

1. **Replication**: You
will first replicate an existing published piece of econometric analysis (of
your choosing), [3]
and then …

2. **Improvement**: You
will then improve that analysis in some way (by adding more data, changing the
specification of the model, changing the estimation technique, and so
forth).

Topics
should showcase interesting econometric analysis, and need not be restricted to
topics in Economics.

**Topic selection**: It’s important to get off to 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, by 6 PM
on Mon. March 23^{rd} . I will
compile those and we'll discuss them in class on Tues. March 24th .

·
**Phase
0: Topic Selection** (briefly discussed in class, Tues. March. 24

**Replication and Improvement**: Your end-of-semester deliverables
will be two *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
deliverables when teams are assigned.
For now:

·
**Phase
I: Replication** (due Thurs. April 16

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.

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

·
**Phase
II: Improvement** (due
@ last class Thurs April 30

** Your
turn!** ... your improvement to the published
analysis. This should be a lot of fun…
but again, it will not go quickly or smoothly, so budget your time accordingly.

Your
Phase I presentation should discuss your data sources and how your dataset was
constructed; credit will reflect in part the level of difficulty.[4] In some cases you may be able to obtain data
from the original authors, which obviously greatly simplifies the replication
phase. You can do that if you want, but
since building datasets is hard work, you won’t get as much credit for your
efforts as you would had you built the dataset yourself.

In your Phase II PowerPoint,
you should brag about your outstanding improvements to the analysis, and
discuss as well aspects of your analysis that did not pan out as expected. If possible, we’ll have in-class research
project presentations on the last day of classes.

I will
assign the research project teams, which will likely have three members each
(teams will be assigned after the first mid-term exam). Students’ grades will reflect both their
individual performance as well as the quality of the final team products (the
PowerPoint presentations as well as the in-class presentation).

*Shirkers
take notice:** Peer
evaluation* forms will be
distributed at the end of the semester, so that team members can assess each
other’s performance… as well as their own.

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

** The Labs **(7.5% of your course grade): There will be six or so one-hour sections of Econ2227
econometrics labs, taught by excellent BC Econ graduate student
instructors. The lab sections will focus
on the use of Stata in empirical analysis generally, and econometric analysis
specifically. Lab scores will be
curved.

** Exercises and qFlips** (7.5% of your course grade; grades on
Exercises/qFlips are curved):

*Exercises***: **There will be four or five empirical
exercises, which together with a similar number of *qFlips* (see following) count
towards 7.5% 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. Teams submit just one Answer Set.

In some cases, the Exercises
are designed to give you practice with the techniques and tools we have
developed in class… other times, they
are designed to introduce you to new material, which we have not yet covered in
class. These will take some time to
complete, so please do not leave them until the last moment.

*qFlips***: **We will have five or six *qFlips* 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
class. They are graded on a two point
scale; everyone should get perfect scores on these. (Grading:
I will drop your lowest qFlip score, just in
case you inadvertently miss a deadline.
But do not miss two deadlines… no late work accepted.)

**Course Topics** [Wooldridge 4^{th} and 5^{th}
edition chapter numbers are in square brackets (there are just a few changes
with the later editions)]

**A: Introduction to
Econometrics and Empirical Research**

1. The Nature of econometrics and economic data [Chapter 1]

- 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

**B: Introduction to
Ordinary Least Squares (OLS)**

2. Simple Linear Regression (SLR) Models [portions of Chapter 2.1-2.3]

*SLR analytics*(single explanatory variable):*in the beginning*(SLR.1: the data generation process); residuals and sum squared residuals (SSR); OLS as minimization of SSRs (FOCs and SOCs); sample statistics (sample means, variances, standard deviations, covariances, and correlations); Sample Regression Function (SRF) and predictions; economic significance (elasticity)*SLR assessment*: Sum Squared Explained (SSE) and Sum Squared Total (SST); SST = SSE + SSR (w/ constant term in the model); goodness of fit (Coefficient of Determination, R^{2}), Mean Squared Error (MSE) and Root MSE (RMSE)

3. Multiple Linear Regression (MLR) Models [portions of Chapter 3.1-3.4]

*MLR analytics*(adding, and subtracting, explanatory variables): SLR analysis continued; interpreting coefficients I –*ceteris paribus*(partial effects and the SRF); the*collinearity*regression; multicollinearity; interpreting coefficients II –*what’s new*; omitted variable bias (endogeneity)*MLR assessment*: SLR assessment continued; adjusted R^{2}

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

*Sidetrip**:
Getting started with that research paper*

4. Carrying out an empirical project [Chapter 19]

· The scientific method: Testing hypotheses; collecting data; running regressions; doing it all again; and again; until… conclusions

**C: Way-Too-Fast Review of Statistics**

5. Estimation and Inference [Appendix C] (some of this will have been covered in B. above)

·
Our focus will be on estimation of the
population mean, LUEs (Linear Unbiased Estimators), and BLUEs (Best Linear
Unbiased Estimators)

· Estimation: Populations; point estimators; sample statistics and sampling distributions (sample means, variances, standard deviations, covariances and correlations); unbiasedness; interval estimators

· Inference: standard errors, t statistics, p-values; confidence levels; critical values; confidence intervals; hypothesis testing; significance levels

**D: Estimation and
Inference in Regression Analysis**

6.
Estimation
with SLR and MLR models [remainders of Chapters 2 and 3; portions of
Chapters 6, 8 & 9]

- Estimation in SLR models: 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
- Estimation with MLR model (what changes? … not much!): … now MLR.1-MLR.5; Multicollinearity and Variance Inflation Factors (VIFs); n-k-1

7.
Inference
in SLR and MLR analysis [Chapter 4, portions]

- Inference with SLR models: 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)
- Inference with MLR models (what changes? … not much!): Now MLR.6; n-k-1.

8.
Related
topics: Heteroskedasticity, F tests
& measurement

·
Heteroskedasticity [Chapter 8]: 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?)

- F-tests:
Extension of t-tests to more complicated null hypotheses; testing linear
restrictions with the F(q, n-k-1) distribution; relationship to changes in
SSRs, R
^{2}and SSEs; connect the two approaches to Assessment (Goodness-of-Fit metrics and Inference); reported F stats (for the regression) and associated p values; Chow tests

·
Measurement – units and errors [Chapter 9];
scaling effects and *beta* regressions
[Chapter 6]

**E: SLR/MLR
Analysis – Further issues etc.**

9.
Further Issues I [portions of Chapters 6-9]

· Binary and categorical independent variables (Dummies) [Chapter 7]; fixed and interaction effects (intercept and slope dummies); average residuals (lazy economists)

- Binary dependent variables [Chapter 7]: linear probability models
- Functional forms (polynomials; logs; percentile dummies; splines; etc) [Chapter 6]

10. Selected
Topics [Handouts + portions of Chapter 15]

*Differences-in-Differences*: Deflategate; NBA Referee Own-Race Bias*Regression Discontinuity Designs*: Highway Fatalities & Daylight Savings Time- Endogeneity and Instrumental Variables [Chapter 15]: The Oregon Health Insurance Experiment (Medicaid)

11. Further
Issues II [portions of Chapters 5 & 6]

· OLS asymptotics [Chapter 5]: Large sample properties; consistency (convergence in distribution)

·
Forecasting and *prediction* intervals [Chapter 6]

12. Limited
dependent variables [Chapter 17]:
Maximum Likelihood Estimation (MLE); logit and probit
models; censored and truncated regression models

13. More
about specification and data problems [Chapter 9]

· Misspecified models; proxy variables; missing data; outliers; non-random samples

**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”, which can be accessed at https://bcapps.bc.edu/vpn/index.html … once CitrixReceiver has been installed on your computer. (If you are not connected to the BC network, you’ll need to use VPN to access the apps server.) To learn how to access Stata through the apps server, go to http://www.bc.edu/offices/help/teaching/app_server.html .[5]

Alternatively, and to avoid traffic jams with Citrix and the
apps server, you may want to purchase a six-month *Stata IC* license for about $50 (sorry, but *small* Stata will not suffice for Ec
228). For details, go to: http://www.stata.com/order/new/edu/gradplans/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, easy and 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

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

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

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

[5] And to learn about VPN, go to http://www.bc.edu/offices/help/getstarted/network/vpn.html .