Professor Francis X. Diebold

This course provides an upper-level undergraduate / masters-level introduction
toall aspects of predictive modeling, in economics and related fields.

Prerequisites: Courses in (0)
calculus, (1) intermediate economics, (2) probability/statistics for
economists, and introductory econometrics, including basic time-series
econometrics. We will also use some ideas from financial economics, and some
elementary matrix algebra, and students must be willing/able to learn them as
necessary. Finally, students should be able to program (including simulations)
in an environment like EViews, State, R, Python, etc. We will emphasize EViews
and R.

Although we will make heavy use of general econometrics/statistics, this course
is much more sharply focused. It explicitly and exclusively about economic *prediction*,
or *forecasting*, as opposed to general econometrics/statistics, or
anything else. Emphasis will be on forecast construction, evaluation, and
combination (point, interval, density).

Relevant topics include but are not limited to: regression from a predictive
viewpoint; conditional expectations vs. linear projections; decision
environment and loss function; the forecast object, statement, horizon and
information set; the parsimony principle, relationships among point, interval
and density forecasts; statistical graphics for forecasting; forecasting trends
and seasonals; model selection for forecasting; characterizing, modeling and
forecasting cycles with ARMA and related models; Wold’s theorem and the
general linear process; nonlinearities and regime switching; the chain rule of
forecasting; optimal forecasting under symmetric and asymmetric loss; recursive
and related methods for diagnosing and selecting forecasting models; formal
models of unobserved components; conditional forecasting models and scenario
analysis ("stress testing"); vector autoregressions, predictive
causality, impulse-response functions and variance decompositions; use of
survey data; business cycle analysis using coincident and leading indicators:
expansions, contractions, turning points, and leading indicators; incorporation
of subjective information; Bayesian VARs and the Minnesota prior; evaluating a
single forecast; comparing forecast accuracy; encompassing and forecast
combination; combining forecasts; preliminary series, revised series, and the
limits to forecast accuracy; prediction markets; unit roots, stochastic trends,
stochastic trends and forecasting; unit roots; smoothing; ARIMA models,
smoothers, and shrinkage; using stochastic-trend unobserved-components models
to implement smoothing techniques in a probabilistic framework; cointegration
and error correction; evaluating forecasts of integrated series; volatility
forecasting via GARCH, stochastic volatility and realized volatility.

Books:

Diebold's *Forecasting*.

Silver's *The Signal and the Noise*.

Articles:

We will read and discuss a significant number of research journal articles.

Supplementary materials:

*No Hesitations* blog.

Other books: *(1) Econometric Data Science* and (2) *Elements of Forecasting*
(4e)

Software intros: EViews Intro; R Intro; Python Intro (Sheppard)

Piazza: The system will get you help quickly and efficiently from classmates
and TA's. Rather than emailing questions, simply post them directly on Piazza.
Our class page is: https://piazza.com/upenn/***.
If you have any problems or feedback for the developers, please email them at team@piazza.com.

Grading: Consistent class attendance and participation are crucial for good
performance. Performance will be assessed by *N* standardized problem
set scores (P's), a standardized final exam score (E), and class participation
(C). (Regarding class participation, *I intend for this to be a
highly-interactive class*.) The final score will be .60*Pavg + .25*E +
.15*C. P's are due *one hour before the start of class* on the assigned
day. *Under no circumstances will late P's be accepted*, so be sure to
start (and finish) them early, to insure against illness and emergencies.

**Important
administrative policies** here. (READ CAREFULLY!)

Office hours: Posted here

TA: ***

Weekly TA review sessions: ***

Important dates:

P 1 due Sept ***. Do Ch. 3, EPC 1. Data here.

P 2 due Oct ***. Do section 4.2. Data on book site.

P 3 due Oct ***. Do Ch. 7, EPC 1.

P 4 due Nov ***. Here.

P 5 due Nov ***. Read and report on Brownlees, Engle and Kelly (here).

P 6 due Dec ***. Read and report on Gillen, Plott and Shum (here).

Final exam: Standard university-scheduled day/time/location.

Note well: Modifications and
adjustments to this outline are inevitable and may be implemented at any time.
Check frequently for updates.