![](3D"course-6433-S2005_files/image001.gif")
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Depart=
ment
of Economics Business
Cycles and Forecasting
Lectur=
er: O.
Mikhail ECO 64=
33-A001 Summer=
2005 |
![](3D"course-6433-S2005_files/image003.jpg")
Course Outline
Time =
&nb=
sp; : &=
nbsp; T,
Th 6:00 p.m. – 9:50 p.m.
Location&nb=
sp; : &=
nbsp; BA
I – 115.
Office =
; &n=
bsp; : &=
nbsp; BA
II – 302.
Office Hours : &=
nbsp; M,
T, W, Th 1:00 p.m. – 2:00 p.m. and After the class.
Wed Page &n= bsp; : &= nbsp; http://www.bus.ucf.edu/omikhail
Class Web Page: &= nbsp; ht= tp://www.bus.ucf.edu/omikhail/Eco6433/2005/Eco-6433-s2005.htm
E-Mail =
; <=
/span>: &=
nbsp;
=
; &n=
bsp; =
; omikhail@bus.ucf.e=
du
Phone =
&nb=
sp; : &=
nbsp; 407-823-4258
Fax &n=
bsp; =
; : &=
nbsp; 407-823-3269
This is an MBA class in applied Time Series Analysis and Forecastin=
g.
The class is open for registration to all Master Degree students at the
Class Objectives
The st= udy of business cycles and forecasting originated in macroeconomics at the turn of= the last century with the groundbreaking works of Mitchell (1913, 1927). Nowada= ys, times series analysis and forecasting grew and found its way in most business-administration fields. Is this change temporary? Will it revert ba= ck to its path? How long will it last? Regardless of the series of interest (G= ross Domestic Product, Stock Prices, Stock Options, Sales, Profits, Revenues, etc…), the tools that were developed in time series are readily avail= able to provide answers. The main aim of time series analysis is to understand p= ast behavior and consequently, to forecast future behavior. Forecasts guide decision in numerous fields: operation planning and control, marketing, economics, financial speculation, financial risk management, capacity plann= ing, business and government budgeting, demography and crisis management.
In bus= iness activities, “Time is Money” and forecasting is important. Many business and economics decisions depend on forecasts. Explaining why the se= ries (e.g., profits) decreased is a tedious and straightforward exercise –= in most cases. Predicting when i= t will increase or when it will decrease again requires one to master few technical tools. Generally, few analysts recognize the existence of regular cycles and longer term secular movements in business and economic activity. Rather than cycles, analysts tended to think in terms of ‘crises’, a term u= sed to refer to financial panics and/or periods of slow business and economic activities.
There = are different approaches to forecasting. This class is intended to help student= s, managers and administrators to do statistical forecasting, and therefore to learn how to manage uncertainty by using effective forecasting and other predictive techniques. Think of this class as a detailed study of the avail= able tools in an analyst/forecaster toolbox. Regardless of your field of study, these tools will be useful to your studies – hopefully. By the end of= the class, students should be able to master a set of statistical techniques to forecast a variable of interest.
Basic knowledge of calculus, matrix algebra and elementary statistics are require= d. Students are responsible for all materials covered in class that is presented in the textbook, in the readings on the class web page and in the lecture notes.
Attend= ance at lectures is not mandatory but is recommended. If you are experiencing any difficulty in this class = (or any other), please arrange a meeting with me to discuss it.
In your future, if you plan on working with time series, I highly
recommend the following book. Walter End=
ers
(2003) Applied
Econometric Time Series, 2nd Edition Wiley. This is one of the best boo=
ks
that exist for an accessible, comprehensive and up to date time series trea=
tment
and analysis. We will keep referring to parts of the Enders book throughout=
the
class. If you plan on pursuing graduate studies with a field in time series
analysis, then buying the following book is one of the best investments that
you can make. James Douglas Hamilton (1994) Time
Series Analysis,
Book(s)
· (FD= ) Francis X. Diebold (2004) Elements of Forecasting, Third Edition, Thomson South-Western.
·
(PH=
F)
Philip Hans Franses (2004) Time
Series Models for Business and Economic Forecasting,
· (BD= ) Peter J. Brockwell and Richard A. Davis (2003) Introduction to Time Series and Forecasting, Second Edition, Springer.
=
=
E-mail
Policy and Class Web Page
=
Office
Hours
If my office hours (stated above) conflict =
with
your schedule and you need to meet with me, please let me know so I can arr=
ange
a mutually acceptable time to meet.
Class Structure
T= he class will be held in an active-discussion framework. Due to the class size and t= he limited class time, I suggest that students form study groups outside of cl= ass to work through the class material.
Lab Sessions
Lab sessions will be held regularly to learn Eviews = and Interactive Time Series Modeling (ITSM 2000).
Evaluation
Quizzes &= nbsp; &nbs= p; &= nbsp; &nbs= p; 30%
Assignments &= nbsp; &nbs= p; &= nbsp; &nbs= p; 20%
Term Paper = &nb= sp; = &nb= sp; 40%
Class Presentation of you= r Term Paper &n= bsp; 10%
I expect you to uphold the guidelines of the
Golden Rules, http://www.ucf.edu/=
goldenrule
No early or make-up quizzes will be given. =
Class
Presentations will be scheduled during the last week of classes.
Reading
List
Week 1 &nbs=
p; TM91:
Introduction, The graphical display of time series, Summarizing time
&=
nbsp; &nbs=
p; &=
nbsp; series,
Transforming and smoothing time series.
=
; &n=
bsp; =
; Chapters
1, 2, 3 and 4.
&=
nbsp; &nbs=
p; FD: Introduction to Forecasting: Appli=
cations,
Methods, Books,
&nb=
sp; =
&nb=
sp; Journals,
and Software. Appendix: The Linear Regression Model
&nb=
sp; =
&nb=
sp; Six
Considerations Basic to Successful Forecasting
&nb=
sp; =
&nb=
sp; Statistical
Graphics for Forecasting
&=
nbsp; &nbs=
p; &=
nbsp; Chapters
1, 2 and 3.
&nb=
sp; =
PHF: Introduction and Overvi=
ew:
Trends, Seasonality, Abberant &=
nbsp; &nbs=
p; &=
nbsp; &nbs=
p; &=
nbsp; &=
nbsp; &nbs=
p; &=
nbsp; Observations,
Conditional Heteroskedasticity. Chapters 1 and 2.
Week
2 =
FD: Modeling and Forecasting Trend
&=
nbsp; &nbs=
p; &=
nbsp; Modeling
and Forecasting Seasonality
&nb=
sp; =
&nb=
sp; Chapters
4 and 5.
&nb=
sp; =
BD: Introductio=
n,
Stationary Processes and Autocorrelation function
&nb=
sp; =
&nb=
sp; Chapters
1 and 2.
Week 3 =
; Monday, May 30, 2005 Memorial Day
Week 3 &nb=
sp; FD: Characteriz=
ing
Cycles
&nb=
sp; =
&nb=
sp; Modeling
Cycles: MA, AR, and ARMA Models
&=
nbsp; &nbs=
p; &=
nbsp; Forecasting
Cycles
&nb=
sp; =
&nb=
sp; Chapters
6, 7, 8 and 9.
&nb=
sp; =
BD: ARMA Models=
&nb=
sp; =
&nb=
sp; Chapter
3.
&nb=
sp; =
PHF: ARMA, Autocorrelation a=
nd
identification, Model selection and &=
nbsp; &nbs=
p; &=
nbsp; &=
nbsp; &=
nbsp; &nbs=
p; &=
nbsp; Forecasting.
&nb=
sp; =
&nb=
sp; Chapter
3.
Week 4 &nb=
sp; FD: Distributed=
lags,
Polynomials and Vector Autoregression VAR
&nb=
sp; =
&nb=
sp; Evaluating
Forecasts
&nb=
sp; =
&nb=
sp; Chapters
10 and 11.
Week 5 &nb=
sp; FD: Unit Roots,
Stochastic trends, ARIMA Forecasting and Smoothing
&nb=
sp; =
&nb=
sp; Chapter
12.
&nb=
sp; =
TM99: Persistence, trend reversion, fract=
ional
integration and long &nbs=
p; &=
nbsp; &nbs=
p; &=
nbsp; &=
nbsp; &=
nbsp; &nbs=
p; &=
nbsp; memory
processes
&nb=
sp; =
&nb=
sp; Chapter
3.
Week 6 &=
nbsp; FD: Volatility
Measurements, Modeling and Forecasting
&nb=
sp; =
&nb=
sp; Chapter
13.
&nb=
sp; =
TM99: Univariate non-linear stochastic mo=
dels
&nb=
sp; =
&nb=
sp; Chapter
4.
=
; &n=
bsp; Class Presentations
Academic Dates – Summer 2005
Academic Dates and De=
adlines |
|
|
Classes Begin |
May 16 |
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Late Registration and Ad=
d/Drop |
May 16 – May 20 |
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Withdrawal Deadline |
June 3 |
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Graduate Thesis/Disserta=
tion
Defense Deadline |
July 11 |
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Graduate Thesis/Disserta=
tion
Submission Deadline |
July 29 |
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Classes End; Last Day to=
Remove
Incomplete |
June 24 |
|
Final Examination Period=
|
June 24 |
|
Grades Available on MyUCF
(begins at 9 a.m.) |
June 30 |
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Commencement |
August 6 |
THE UCF CREED
|
Integrity,
scholarship, community, creativity, and excellence are the core values IntegrityI will practice and defend academic and perso= nal honesty. ScholarshipI will cherish and honor learning as a fundam=
ental
purpose CommunityI will promote an open and supportive campus
environment by CreativityI will use my talents to enrich the human
experience. ExcellenceI will strive toward the highest standards of performance in any endeavor I undertake. |