**EC 228 02:
Econometric Methods (Spring 2018)**

Stokes Hall 295S: T Th (10:30 – 11:45)

Christopher Maxwell** **Maloney
Hall, 337

maxwellc@bc.edu Office Hrs: TTh 3+

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.

**Peer tutors**: @ Connors Family Learning Center *(stay tuned)*

**Prerequisites**:
An introductory statistics course such as EC 151. No exceptions. I will also assume that students have an
understanding of basic *Excel* (which will be used at times to assemble
datasets and verify calculations) and basic calculus. I do not assume that anyone has previous
exposure to *Stata*, the computer language that we will be using to run
regressions (but you will be better for it if you have seen Stata before). (See below for more about Stata.)

**Course reference text**:

·
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), or 6^{th} (2015)
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 just because
sometimes it is useful to see a different presentation 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*, 3^{rd} ed., Addison-Wesley, 2014.

·
Studenmund, A.H., *Using Econometrics: A
Practical Guide*, 7^{th} ed., Pearson, 2016.

·
Bailey, Michael A., *Real Econometrics: The Right Tools to Answer Important Questions*,
Oxford, 2016.

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

**Grading** (Exams: 75%; Research project: 10%; Labs: 7.5%;
Exercises: 7.5%):

· Three exams: Two mid-terms and one optional final exam

§
MT #1: Thurs,
Mar 1^{st} (last class before Spring Break)… OLS - SLR and MLR models

§
MT #2: Thurs,
Apr 26^{th} (next to last week of classes)… Estimation and Inference + additional topics

§
Optional Final: Mon May 14^{th}, 2:30 PM (?) … covers full semester

All exam grades are curved.

Exams are *open
cheat-sheet* (one cheat sheet 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%.

- 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 final exam,
conditional course grades, which assume that you are not taking the final exam,
will be posted to Canvas by the end of the day, Tuesday May 8^{th}
. (I have been assured that lab scores will
be available no later than the 7^{th} .)

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. There are two hardcopy deliverables, both *PowerPoint* presentations:

*Replication*:
PowerPoint due Thurs Apr.19^{th}

*Improvement*: PowerPoint due Thurs May 3^{rd} (last
class)

We’ll have in-class presentations on May 3^{rd} ,
the last class. This will be a team
assignment, with three students per team.
I will assign teams. Grades will
be given for both individual as well as collective performance. More details below.

· EC 228 Labs (7.5%): EC 228 course-wide labs, focused on using Stata in empirical/econometric analysis. Lab scores will be curved.

·
Exercises (7.5%): There will be five Exercises over the course
of the semester. These will focus on
empirical applications of the tools developed in the course. Feel free to work together on these, but
please submit your own write-up (unless it’s a *team* assignment … more on
this below). Course grades for Exercises
will be curved. More details below.

**Canvas: ** Boston
College has in the past few years migrated away from BlackboardVista to a new
course management system called ** Canvas**. That migration has not gone smoothly for my
courses. Until Canvas offers

**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, please go to http://www.bc.edu/offices/stserv/academic/integrity.html.

**Research
Project** (10% of total grade): This is an empirical research project, which
will kick off with team assignments after the first mid-term exam. Depending
on how you count, there are two phases to the project: you
will first replicate an existing published piece of econometric analysis (of
your choosing), [1]
and 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 by 6 PM on March 26^{th}, a one paragraph description of their
topic/paper of interest. I will compile
those and discuss them in class on the 27^{th} .

·
**Phase
0: Topic Selection** (discussed in class, Tues. Mar 27

**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.

·
**Phase
I: Replication** (due Thurs Apr. 19

Replicate
both the summary statistics 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 May 3

** 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.[2] 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. We’ll have in-class research project presentations
on May 3^{rd} , the last day of classes.

I will
assign the research project teams, which will likely have three members
each. 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.

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

**The Labs:** There will be six or so one-hour sections of
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, which
will be curved, count towards 7.5% of your course grade.

**Exercises: **There will be five
empirical exercises, counting towards 7.5% of your course grade. These will typically be** **team assignments,
usually with two students per team, and are typically graded on a 10
point scale. I will assign teams, which will differ from
Exercise to Exercise. Teams should
submit just one Answer Set. Final grades
on Exercises are curved.

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.

**Course Topics** [Wooldridge 4^{th} and 5^{th}
edition chapter numbers are in square brackets (there are just a few changes
with the 6^{th} edition)]

**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: Review of Probability and Statistics**

5. Review of foundational basics: Probability and statistics [Appendices B & C] (some of this will have been covered in Part B. above)

· Probability: Probability distributions (probability density functions (pdfs); measures of central tendencies (expectations) and variability (variances); joint and conditional distributions; measures of association (covariance and correlation); conditional expectations and variances

· Statistics (Estimation and Inference): Populations; point estimators; sampling distributions; sample means, variances, covariances and correlations; unbiasedness; interval estimators; confidence intervals; hypothesis testing

·
Focus on estimation of the population mean, LUEs
(Linear Unbiased Estimators), and BLUEs (Best Linear Unbiased Estimators)

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

6.
Estimation
with SLR and MLR models [remainders of Chapters 2 and 3]

- 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; F-tests of linear restrictions; reported F stats and p values; Chow tests

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

8. Further
Issues I [portions of Chapters 6-8]

·
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?)

·
Scaling effects and *beta* regressions [Chapter 6]

- Binary and categorical independent variables (Dummies) [Chapter 7]; fixed and interaction effects (intercept and slope dummies); average residuals (lazy economists); percentile dummies and more functional forms
- Binary dependent variables [Chapter 7]: linear probability models
- Functional forms (polynomials; logs; splines; etc) [Chapter 6]

9.
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)

10. 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]

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

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

· Misspecified models; proxy variables; measurement error in dependent and independent variables; missing data; outliers; non-random samples

**Calendar**

I can’t guarantee how exactly we will progress through the material. But based on previous semesters, here’s a likely calendar (#s refer to weeks in the semester):

*Introduction to EC 228 and Econometrics*

1. Syllabus; Introduction to the course; Econometrics examples

*Introduction to OLS: SLR and MLR models*

2. *SLR Analytics*; Exercise #1 distributed
(due wk #4)

3. *SLR Assessment*

4. Finish Intro to OLS/SLR; Exercise #2 distributed (due wk #7)

5. *MLR Analytics* (focus on differences from
SLR analytics)

6. *MLR Assessment* (again, focus on
differences viz. SLR assessment); Distribute practice questions for MT #1

7. Finish Intro to OLS/MLR; Mid Term #1 (Intro to OLS)

*Spring Break*

*Review of Probability and Statistics*

8. Return MT #1s; Kick off research projects with team assignments; Review of probability and statistics; Exercise #3 distributed (due wk #9) (In the past this has taken 4.5 classes… I’m hoping to be more efficient this semester.)

*Estimation and Inference*

9. Finish Probability and Statistics; Intro to MLR Inference (reading regression results); Kickoff SLR and MLR models: Estimation and inference;

10. Research project topic selections; SLR and MLR models: Estimation and inference cont’d; Exercise #4 distributed (due wk #12); Thurs: Easter break

11. SLR and MLR
models: Estimation and inference cont’d;
Heteroskedasticity and *robust*
standard errors

*Further Topics/Issues*

12. Dummy variables; Exercise #5 distributed (due wk #14)

13. More about
dummy variables; Functional forms; Consistency; *Replication* PowerPoint presentations due; Distribute practice
questions for MT #2

14. Selected topics; Semester wrap-up and review; Mid Term #2 (focus on material since MT #1)

15. Tues: class
devoted to helping with papers; Thurs: *Improvement*
PowerPoint presentations due; in-class presentations

**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 … 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 .[3]

Alternatively, and to avoid traffic jams with Citrix and the
apps server, you may want to purchase a six-month *Stata IC* license for $45 (sorry, but *small* Stata will not sufficient 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.

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

· http://www.stat.ucla.edu/labs/pdflabs/started.pdf

· 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 *British accents*!

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

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

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