Economics 427 - Economic Forecasting
-----Become an Expert Data Analyst!-----

Instructor: Peter Fuleky
Office: 508 Saunders Hall
Hours: anytime by appointment

Email: fuleky at

Class Info:
Semester: Fall 2021
Class Location: SAUND 541 or online
Class Time: MWF 12:30-1:20
Final Exam: TBD
Prerequisites: ECON 321 or BUS 310 or NREM 310 or (MATH 251A and NREM 203) or (MATH 371 and MATH 373) or (MATH 471 and MATH 472)


Student Learning Outcomes:
Learn important data analytic tools, methods and skills through real life examples. Carry out data wrangling and exploration, regression analysis, and prediction with machine learning. Conduct hands-on data analysis guided by fully developed case studies, present results to an educated audience, and demonstrate effective communication.

Required Reading:
Gabor Bekes and Gabor Kezdi: Data Analysis for Business, Economics, and Policy. (

Topics and Case Studies:
Listed on the book's website:

Recommended Reading:
Rob Hyndman and George Athanasopoulos: Forecasting: Principles and Practice (
Nate Silver: The Signal and the Noise.
Charles Wheelan: Naked Statistics.
Jennifer Castle, Michael Clements and David Hendry: Forecasting, An Essential Introduction.
Trevor Hastie and Rob Tibshirani: An Introduction to Statistical Learning with Applications in R. (
Eric Ghysels and Massimiliano Marcellino: Applied Economic Forecasting using Time Series Methods

You are responsible for all assigned textbook readings, assigned supplementary readings, and the content of my lectures.

Additional Books of Interest:
Keith Ord, Robert Fildes (2012): Principles of Business Forecasting, Cengage Learning.
Soren Bisgaard, Murat Kulahci (2011): Time Series Analysis and Forecasting by Example, Wiley.
Gloria Gonzalez-Rivera (2012): Forecasting for Economics and Business, Prentice Hall.
Francis X. Diebold (2006): Elements of Forecasting, South-Western.
Spyros G. Makridakis, Steven C. Wheelwright, Rob J Hyndman (1997): Forecasting: Methods and Applications, Wiley.
Michael K. Evans (2003): Practical Business Forecasting, Blackwell.
Walter Enders (2009): Applied Econometric Times Series, Wiley.
James H. Stock, Mark W. Watson (2010): Introduction to Econometrics, Addison-Wesley.
R. Carter Hill, William E. Griffiths, Guay C. Lim (2011): Principles of Econometrics, Wiley.
R Cheetsheets.
Here is some old material that I used in the past: Notes 2011-13, Visualizing Multiple Regression.

Course Requirements:
Grades for the course will be based on one midterm exam (20%), one comprehensive final exam (25%), problem sets (10%), a term project (30%), and participation (15%). Participation includes written quizzes (5%), oral recaps of previous class sessions, and contribution to class discussions (10%) (see next page). Due dates are firm!

Google Classroom:
Limit your private emails to teaching staff. I encourage you to post your questions on the discussion board so that your classmates can also respond. Find our class page at: TBD Join the class using code: TBD

The midterm and final exams must be taken at the scheduled dates and times. Except for medical emergency, I will not schedule makeup exams.

Problem Sets:
Problem sets will include analytical lecture material and coding applications. They will take a substantial amount of time: plan ahead!

Get inspired by the case studies at: You will follow a data analytic workflow from data exploration to prediction and present your work to the class.

Class Participation:
Active participation helps to deepen understanding of course material. To facilitate this, I organize class in an informal lecture and discussion format, and I expect you to participate. You will be evaluated on your contribution to class discussions. Prepare for class by reading ahead in the textbook. Do not miss class. At the beginning of each class there will be a short oral quiz on the topics covered in the previous class session, and we will discuss assigned reading material (this will include short presentations by you). You will also provide constructive critique of your classmates' projects

Academic Integrity:
Academic dishonesty includes cheating and plagiarism, and may result in suspension or expulsion from the University.

Students with Disabilities:
If you feel you need reasonable accommodations because of the impact of a disability, please (1) contact the KOKUA Program (V/T) at 956-7511 or 956-7612 in room 013 of the QLCSS, and (2) speak with me privately to discuss your specific needs.

We will use R, a leading statistical environment with powerful analytical capabilities. Note, R is not point-and-click software - it is a programming language.

Useful Resources for Learning R:

Selected Additional Resources on Forecasting:
International Institute of Forecasters, Forecasting Principles web site,

Economagic,, comprehensive online economic data source.

FRED database, St. Louis Federal Reserve Board,

U.S. Bureau of Economic Analysis, Source for U.S. GDP, personal income, balance of payments data, state personal income data.

U.S. Bureau of Labor Statistics, Source for U.S., state and local data on employment, unemployment, consumer prices, and other data.

State of Hawaii Department of Business, Economic Development and Tourism, Primary source for Hawaii data.

University of Hawaii Economic Research Organization, Lots of Hawaii data and links to recent forecast reports.

University of Hawaii Library's Guide to Economics, Lots of useful links.