SYLLABUS
Department of Economics
Business Forecasting – Econ 8310 (appropriate BASD cross listing)
Fall 2007
Section 1: W: 6:00-8:40 pm
Professor:
Office:
Phone:
Chris Decker, Ph.D.
RH 508J
(402) 554-2828
email: christopherdecker@mail.unomaha.edu
Note: this syllabus is a working document. You will need to refer to it periodically
throughout the term. I may have to make schedule and other modifications as we
proceed through the term. I will announce any such changes during class meetings. I
do plan on treating all due dates and exam dates as fixed and unchangeable so that
you can plan your time accordingly, but coverage may alter depending on any number
of things that can occur during a semester.
Office Hours: Mondays and Wednesdays 4:30-5:30 (or by appointment).
Course Description: This course is survey of forecasting methods and study of selected
techniques commonly used in business environments. The techniques surveyed are
statistical and quantitative in nature. The primary focus is in time-series analysis and
time series econometrics. This is important to understand for a few reasons. First, in
practice, some reasonable forecasts can be purely qualitative and subjective in nature. For
instance, a sales manager might reasonably assert that experience as a sales representative
leads to the belief that next year’s sales will be 3 percent lower than this year. Second,
some forecasts (I would say the majority in fact) combine qualitative and quantitative
applications. For instance, many companies have pre-existing econometric (other
statistical) models in place (some models are extraordinarily poor by the way) whereby an
analyst will generate somehow a model-driven forecast and then make adjustments
(called add factors) to these results presumably based on qualitative information and
experience.
With all deference and due respect to these ways of forecasting, our focus here will be, by
necessity, more systematic, and perhaps more “scientific” in nature. Consequently, we
will need to rely heavily on our statistical backgrounds and quantitative skills to construct
forecasting models and generate scientifically justifiable projections.
Class structure and execution: It is difficult/impossible to state with certainty what I will
cover on what day. All we know is that I will follow the general outline provided below.
You should stay current on the reading, and do the recommended problems that I will
“assign” (if any) as we cover said material. Classes will be a combination of lecture and
discussion and I will spend most of our time working through specific forecasting
applications with given series. I encourage you to take advantage of office hours both if
you are having problems and if you want to go beyond the level of the discussion in class.
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I will be communicating with you via email frequently during the term. Be sure to check
periodically your UNO LOTUS NOTES email account regularly. You are responsible for
any information I send to you that way and I will not make arrangements to send email to
any other email account.
Prerequisites: The stated prerequisite for this class is ECON 8300 – Econometrics (I
would also include in this ECON 3300 as well). However, my inclination is not to
enforce this. Now, I will not lie to you - if you have had econometrics (or its equivalent) it
will help you. If you have not, you can still succeed in this class but you’ll in all
likelihood have to work harder than those with the background. That said, please be
aware that I will be including an abbreviated review of multivariate econometric models
(with a focus on time series applications) so you’re not disadvantaged from that
perspective.
Now, I do expect that all students in this class have a working knowledge of basic
statistics (see, Hanke and Wichern, Chapter 2. This is a good review of this necessary
material). I’ll spend a little time on this, but most of this you will have to review on your
own (and I will consider this material fair game for testing purposes).
Required Texts:
1. John E. Hanke, and Dean W. Wichern, Business Forecasting, 8th ed.
2. Robert Pindyck and Daniel Rubinfeld, Econometric Models and Economic
Forecasts, 4th ed.
Additional books and Journals of Interest:
There are a number of other books and journals that our library has (and/or I have)
that you might find useful and interesting to review on your own:
Books:
DeLurgio, Forecasting Principles and Applications, McGraw-Hill/ Irwin
Wilson and Keating, Business Forecasting, 5th ed., McGraw-Hill/Irwin
Evans, Practical Business Forecasting, Blackwell Publishing
Journals:
International Journal of Forecasting (academic)
Journal of Forecasting (academic)
Journal of Business Forecasting (practitioner focused)
Exams: There will be two exams, a mid-term and a non-cumulative final. I will have
more to say about these exams as test dates get closer.
Statistics Review Problem Set: While statistics is the primary pre-requisite for this class,
it is nonetheless necessary for you to make sure that you are up to speed on this topic.
However, I’m ill-inclined to spend class time reviewing this with you. So, what you will
be required to do then is the following. There will be an extensive set of statistics
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problems posted on blackboard that you will have to compete and turn in for a grade (and
of course this will comprise part of your course grade). Also, you will see some statistics
questions on Exam 1 as well. I will announce via class and/or email when and how these
problem sets are to be turned in. As you will be working on them outside of class, there is
a probability that you will work together in groups on this. That’s fine with me BUT if
you do not understand this material and choose to “copy” from other classmate’s work, be
advised that since statistics questions will appear on your in-class exams, such a strategy
is NOT wise (moreover, if a student does really well on the problem set and very poorly
on the exam questions, this will send a clear signal to me as to the seriousness with which
the problem set was taken and you will have to explain this outcome to me! If I find the
explanation do be inadequate, I will decide on the appropriate course of action to take.)
Forecasting Project: A major component of this class is a term paper/project where each
student will be required to construct a model and a forecast for a given time series (or
group of time series concepts). Posted on Blackboard is a detailed description of this
project.
Potential Sources of Data for Forecasting Project: There are many sources of time
series data. During class I will highlight a few places that I’m familiar with (such as US
government data from bea.gov and bls.gov). If you have any particular questions, etc.,
about data that you have found, and/or are having difficulty finding data for your project,
come and see me and I will help you.
Homework: I do think homework assignments are a good way to learn and prepare for
exams. As such, I will assign certain questions from your texts that you will find useful to
work through. I will NOT grade these assignments but will post answer keys on
Blackboard when I believe that 1) I’ve covered the relevant material in class and 2) you
have had sufficient time to work through problems on your own
In-class problems/activities: There will be a few in class activities in this class where we
might meet in the CBA computer lab. I will make such information available to you as
such times draw near.
Software: Much of what we will do has to be “hands on” as it were. As such, you will be
required, either in homework questions or for your forecast project, utilize some type of
computer software program that can do time series analysis and econometrics. Most
packages can. RATS, STATA, LIMDEP, and FORECASTX are just a few such
programs.
For this class, I will use EVIEWS 6.0. I’ve used it for years and the business school
does have a group license for this software in the CBA computer lab (but I think the
maximum number of users is fixed at some number…I’ll check on that specifically). You
can also obtain your own copy of this software through the University Bookstore as well.
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At any rate, this is what I will use in class and I would suggest that you follow my lead on
this.
However, honestly I’m not inclined to force any student at this point to use a particular
software package. So if you are familiar with a particular program that can do all the
various statistical applications that we will discuss in class, than please feel free to use
that software. BUT if you go down this route be aware that if you run into problems of
any kind, I CANNOT HELP YOU! You’re in effect on your own.
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Grading: Your grade will be made up of your scores on the three exams, the industry
analysis paper, and in-class activities (etc.).
Distribution
Review of Statistics problem set
Exam 1
Exam 2
Forecasting Project
pct.
5%
30%
30%
35%
My Scale:
100.0 - 99.0
A+ 77.9 - 75.0
C+
98.9 - 92.0
A 74.9 - 71.0
C
91.9 - 89.0
A- 70.9 - 68.0
C88.9 - 86.0
B+ 67.9 - 65.0
D+
85.9 - 81.0
B 64.9 - 61.0
D
80.9 - 78.0
B- 60.9 - 58.0
DThis will be the scale I apply to all
grades. Moreover, I truncate all grades
(i.e. I do not round up. So a 77.9 IS a
C+, it’s not a B-).
Important course material: I will be using Blackboard to post material, data sets,
homework, working copies of my lecture slides, etc. So check frequently for added
material on blackboard. I will also try to send emails around when I do so. For lecture
material, it is best to check the afternoon of our class day (usually around 4:30) for new
material. Honestly, I’ll likely be adding, changing, and otherwise working on lecture
material right up until class time (a sorry fact of life but I’m sure you all understand).
Success in this class: Focus on what I do, what I lecture on, and what I stress as
important material to cover by way of reading.
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Course Topics:
I.
Introduction to Forecasting, Conditional and Unconditional Forecasting
Henke and Wichern (HW): Chapter 1 (review chapter 6)
Pindyck and Rubinfeld (PR): Chapter 8
II.
Time Series Data Patterns, Autocorrelation Function, Random Walks,
Differencing, Stationarity
HW: Chapter 3
PR: Chapter 16
III.
Moving Averages and Smoothing Methods
HW: Chapter 4
PR: Chapter 15
IV.
Time Series Decomposition, Seasonal Adjustment
HW: Chapter 5
PR: Chapter 15
V.
Multiple Regression Analysis with Time Series Data
HW: Chapters 6, 7, 8 (mostly 8)
VI.
a. Box Jenkins Models (i.e. univariate ARIMA models)
HW: Chapter 9
PR: Chapter 17
b. Estimating and Forecasting with Box Jenkins Models
PR: Chapter 18
c. Combining Regression Analysis with Time Series Analysis
PR: Chapter 19
VII.
VIII.
Multiple Equation Time Series Models, Simulation, Practical Application of
Vector Autoregressive Models
PR: Chapters 13, 14
Special Topics in Forecasting (time permitting): New Product Forecasting and
Diffusion models
Readings TBA
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Important Dates:
Wednesday, September 12
Wednesday, September 19
Wednesday, October 24
Wednesday, November 21
Week of December 10 through 14
Wednesday, December 19
Class cancelled
Stat review due
Exam I
Thanksgiving holiday (no class)
Forecasting project due
Exam II (not cumulative)
Issues regarding academic honesty, integrity and class behavior: I fully expect you to
know and understand the terms of academic honesty and integrity as presented by the
university. Please review the material at:
http://www.unomaha.edu/graduate/catalog/2004-2005/important_info/gen_policies/acahon.html
and at http://www.unomaha.edu/writingcenter/AcademicPolicy.doc
In addition, I’m really hoping that we have an interesting and productive term. That said,
there are a few things that cause me great consternation. These are issues that have arisen
largely in my undergraduate classes so I suspect you folks, as mature graduate students,
would understand and easily comply with. That said, I’ll enumerate them anyway. Please
bear with me on this. Please:
1. avoid sleeping in class (at least, try very hard not to! I know, I know, it’s
economics after all!)
2. do not read material or engage in any other activity not directly relevant to the
class topic at hand.
3. do not leave before class is over (if you must leave early, please let me know
before class).
4. do not get up in the middle of class for any reason at all (unless you are truly ill).
5. turn all cell phones, and other electronic equipment off during class time.
I do know that many of you have substantial work-related responsibilities that may make
points 3, 4, and 5 more difficult to comply with. But please be advised that I will provide
you with a few short breaks throughout our evenings together where you can check
messages, use the restroom, etc. Also, please understand that if you do fail to comply
with these, I will fail you on your next scheduled exam. Please, this is very important to
me.
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