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Business Cycles and Forecasting

Rutgers University
The State University of New Jersey
Department of Economics – CCAS
Spring 2013

Class Information

Course Title: Business Cycles and Forecasting (index#57295), Economics 392/Section 01
Instructor: Dr. I-Ming Chiu
Office: ARMITAGE 328
Phone: (856) 225 6012
E-mail address: ichiu@camden.rutgers.edu
Class Meeting: BSB 334. 8:00-9:20 AM (Tuesday/Thursday)
Office Hours: 11:30-1:30 PM, Thursday or by appointment

Course Description:

The term “business cycles” is used to describe the fluctuations in the overall economic activities. This class will introduce various macroeconomic theories that help students better understand the causes of business cycles. After reviewing main stream business cycles theories, students will be exposed to modern forecasting methodologies. All the forecasting technique will be learned via the powerful & flexible statistical software R. The ultimate goal of this class is to equip students with forecasting ability and enable them to make better economic/business decisions.


Other Textbook:

  • Vincent Su, Economic Fluctuations and Forecasting, HarperCollins College Publishers, 1996.


All the computations will be done using an open source statistical software R. It can be downloaded at http://www.r-project.org.

Class Material:

Handouts, readings, and assignments will be posted on Sakai website for you to download.

Useful Websites:

Academic Calendar:

Grading:Contribution to Final Grade
Attendance 5%
Take-home problems 40%
Midterm Exam (2) 30%
Final Exam/Project 25%
Participation (extra credit) 5%

Grading Policy:

Term grades will be based on the final distribution of the above grading weights.

Exam Preparation:

The exam questions will be drawn from three sources: (i) homework assignments, (ii) course lectures, and (iii) reading material.

Class Participation:

Class attendance is essential for learning achievement. When missing a class, it would cost you more time to learn on your own. I strongly recommend the following steps for your successful learning: (1) attend every class and take notes; (2) review everything you learn from the class immediately, never put it off; (3) ask questions and participate in class discussions.

Academic conduct:

Make up exams will be given only upon prior notice. I request prior knowledge of any expected absence from an exam. If this is not feasible, you can document a valid reason for missing the exam. Unexcused absence on any exam will result in a grade of zero. Dishonesty in seeking an excused absence or in the examination process will result in a grade of zero on the exam involved and in university discipline.

Course Outline:  

Topic 1

Macroeconomic Theories: From Classical to New Keynesian Theory

Topic 2

Business Cycles

Topic 3

Model for Data Mining & The Forecasting Perspective

Topic 4

Basic Tools for Understanding Data

Topic 5

Linear Regression Models

Exam 1

Date: TBA in the class

Topic 6

Time Series Decomposition & Exponential Smoothing Methods

Topic 7

The Box-Jenkins Methodology for ARIMA Models

Topic 8

Logistic Regression Model

Exam 2

Date: TBA in the class

Topic 9

Introduction to Tree Models and Neural Network Models

Topic 10

Ward’s Method of Cluster Analysis and Principal Components

Additional Topic

Structural Equation Models w/ Observed Variables: Path Analysis

Final Exam

2:00 – 5:00 PM, Thursday, May 16.