Econometrics

Professor Francis X. Diebold

Welcome!

This course provides an undergraduate introduction to modern econometrics, in
both cross-section and time-series environments.

Prerequisites: Statistics for Economists (Econ 2300) must be taken *prior* to Econ 2310. (Certain
other introductory Penn courses or course sequences in probability and
statistics including regression may be acceptable, again taken *prior* to Econ 2310, such as Penn's Stat
4300+4310 or Penn's Engineering equivalent. Any other background must be
explicitly approved.)

Heavily-used site: *Econometric Data Science*
(open text, slides, data, code, etc.). The site is constantly evolving, so
check frequently for updates. The course outline is effectively the text's
table of contents, but the slides and lectures, *not the text*, are the centerpiece of everything. The text is
offered as one tool to help you understand the slides and lectures, which,
again, are the centerpiece of everything. Although the slides are
self-contained, there is no way to understand them without attending lectures,
where I will interpret/embellish/generalize/specialize them, guiding you
selectively. Hence regular class attendance is absolutely essential.

Relevant texts, recommended
but not required, include: Gujarati, *Econometrics
by Example*, latest edition (pragmatic, easy to read); Wooldridge, *Introductory Econometrics: A Modern Approach*,
latest edition (balanced and comprehensive); Stock and Watson, *Introduction to Econometrics*,
latest edition (pragmatic and balanced, as well as deep and insightful; worth
the time investment).

Software: Your choice. The *Econometric Data Science* site
has some R, EViews, Stata, and Python code samples. R is the official course
software.

TA office hours as
announced in class. Professor Diebold’s office hours
(held in PCPSE 607) here.

Grading will be based on equally-weighted problem sets (50% of final grade)
and equally-weighted exams (50% of final grade). Problem sets are due at the
start of class on the assigned day. *Under
no circumstances will late problem sets be accepted*, so be sure to start
(and finish) them early, to insure against illness and emergencies.

**Important Administrative
Policies: **Here. (*Read them carefully.*)

**Important Dates and Assignments (All are tentative until
confirmed/discussed in class)**:

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Add period ends.

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In-Class Exam 1 (No books, notes, electronic devices, etc.)

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Problem Set 1 (Must be done alone. Show all code in an appendix.)

Obtain the test score dataset. (1) Display a scatterplot of
math score (MATH_SCR) vs. student/teacher ratio (STR). (2) Regress MATH_SCR on
an intercept alone. Interpret this regression and discuss your results. In this
intercept regression framework, how would you test the hypothesis that the
(population) mean score is 82? Do it, and discuss your results. Now conduct a
one-sided hypothesis test that the population mean score is not less than 82
and discuss your results. (3) Regress MATH_SCR on an intercept and STR. Discuss
your results. Do you need an intercept? Again graph
MATH_SCR vs. STR, this time with your preferred fitted regression line
superimposed.

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Drop period ends.

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Grade type change deadline.

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In-Class Exam 2 (No books, notes, electronic devices, etc.)

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Last day to withdraw.

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Problem Set 2 (May be done in groups of at most three. I expect a creative
analysis, well-defended yet qualified as appropriate, thorough yet concise,
maximum 15 pages. Show all code in an appendix.)

(1)
Regress READING score on student/teacher ratio. (2) Select a "best"
predictive regression model for reading score. Among other things, you may want
to consider non-normality, outliers, group effects, nonlinearities, and
heteroskedasticity. Do the results differ from those of Regression 1? Interpret
your results. (3) Repeat 1 and 2 with a predictive regression model for MATH
score. Are your selected models the same for reading and math? (4)
Suppose California creates a new school district, and that legislators mandate
a 15/1 student/teacher ratio. Based on that information alone,
predict the new district's average reading score (point, interval,
density). (5) Now suppose that, in addition, you learn that the new
district has average income $7,000, 50% English learners, 60 % qualifying for a
reduced-price lunch, and all other variables are at their dataset sample mean.
Predict the district's average reading score (point, interval, density).

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In-Class Exam 3 (No books, notes, electronic devices, etc.)

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Problem Set 3 (May be done in groups of at most three. I expect a creative
analysis, well-defended yet qualified as appropriate, thorough yet concise,
maximum 15 pages. Show all code in an appendix.)

(1) Specify and estimate a
model of U.S. monthly domestic auto sales, NSA (series DAUTONSA from FRED),
using ONLY data for January 1967 - February 2019. Among other things, you
may want to consider trend, seasonality and other calendar effects, nonlinearity,
and autoregressive dynamics (with at most six lags). Do NOT worry about
possible structural change, non-normality, or heteroskedasticity. (2) Use your
preferred model from part 1 to make out-of-sample point, interval, and density
forecasts of DAUTONSA for March 2019. Evaluate the performance your forecasts.
Again, do not worry about possible structural change, non-normality, or
heteroskedasticity. (In particular, construct your forecasts assuming
structural stability and Gaussian disturbances with constant
variance.) (3) Now worry about possible structural change and/or
non-normality and/or heteroskedasticity, and re-do the analyses of parts 1 and
2. How, if at all, do your preferred model and forecasts change? (4) Bonus (+5
points max, +4 pages max): How would you extend your point, interval, and density
forecasts to September 2019?

**Note Well:** *Changes may be implemented at any time. Check this site frequently, and
attend class, for updates and explanations.*