Predictive Modeling in Economics and Finance
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.