e537bib.doc - hhstokes

Business Research and Forecasting I 28 July
2015 Texts: 1
Computer Material: 1
Other Related Material: 2
General Outline of the course: 2
Late submission of projects and take home exams and Attendance 4
Joint work 4
Problem Sets: 4
Computer Skills: 4
Brief Road Map of Material Covered in Economics 537-538 6
Software 11
Assignments 12
Problem Set # 1 - ARIMA Identification using Real Data 14
Problem Set # 2 - ARIMA Identification and Estimation using Generated
Data. 18
Problem Set # 3 - ARIMA Estimation using Real Data. 21
Problem Set # 4 Transfer Function Modeling 23
Problem Set # 5 ARCH/GARCH Modeling 25
Problem set # 6 - VAR Models using real data. Cointegration Analysis 31
Problem set # 7 - Spectral Analysis and the relationship between time
domain analysis and frequency domain analysis. 33 Dr. Houston H. Stokes
722 UH
E-Mail: hhstokes@uic.edu
Stokes Page for links to key material www.uic.edu/~hhstokes Texts: 1. Stokes, Houston H. Specifying and Diagnostically Testing Econometric
Models, Second Edition Quorum Books, 1997. Selected Chapters of 3rd
edition (20xx) on line under E537. All chapters under E535.
2. Enders, Walter, Applied Economic Time Series. Third Edition Wiley
2010. 3. Stokes, Houston, The Essentials of Time Series Modeling: An Applied
Treatment with Emphasis on Topics Relevant to Financial Analysis,
Preliminary Chapters on line. (200x) Computer Material: 1. Stokes, Houston, "B34S On-Line Help Manual" 450 pages. Available on
line from B34S page. Help is available on individual commands for this
page. 2. Doan, Thomas, Rats Version 9 Reference Manual. Estima 2015. 3. Doan, Thomas, Rats Version 9 User's Guide. Estima 2015.
4. Becketti, Sean, Introduction to Time Series Using Stata, Stata Press
2013 Note: RATS manuals can be accessed from Windows RATS versions in lab on 7th
floor. Other Related Material: 1. Neuburger & Stokes, "The Relationship between Interest Rates and Gold
Flows under the Gold Standard: A New Empirical Approach," Economica,
Volume 46, August 1979, pp 261 - 279 2. Stokes & Neuburger, "The Effect of Monetary Changes on Interest Rates:
A Box-Jenkins Approach," The Review of Economics and Statistics, Vol.
LXI, No. 4, November 1979, pp 534 - 548 3. Box, Jenkins and Reinsel, Time Series Analysis Forecasting and
Control, 4th Edition Wiley 2008. 203-208.
4. Hastie, Trevor, Rob Tibshirani and Jerome Friedman. . The Elements of
Statistical Learning: Data Mining, Inference, and Prediction.. New
York: Springer 2001. Second edition 2009.
5. Tsay, Ruey Analysis of Financial Time Series. 3nd Edition 2010, Wiley,
New York. 6. Theil, H. "On the Use of Incomplete Prior Information in Regression
Analysis," Journal of the American Statistical Association 58. No. 302
(1963): pp 401-414. 7. Zellner, A., and Franz Palm. "Time Series Analysis and Simultaneous
Equation Econometric Models." Journal of Econometrics 2 (1974): 17-54.
Republished as Chapter 1 in Zellner, A., and Franz Palm Edition The
Structural Econometric Time Series Analysis Approach. UK: Cambridge
University Press, 2004.
8. Engle-Granger "Co-Integration and Error Correction: Representation,
Estimation and Testing" Econometrica 55. No. 2 (March 1987): 251-276. In the assignments, every effort has been made to outline the computer code
needed to minimize the "learning curve". Assignments can be done on user's
PCs (If they install B34S or have RATS on the local PC) or in the lab on
the 7th floor where both RATS and B34S are available. Students can obtain
accounts on the Linux machine smith.econ.uic. The advantage of smith over
PC use is that more storage is available and longer jobs can be run. On
the PC high resolution graphics are available. General Outline of the course: The purpose of the course is to introduce the student to statistical
time series analysis. ARIMA and transfer function model building will be
discussed and students will be asked to apply their knowledge in several
computer exercises which will be graded. There will be a take home final.
The grading will be 70% computer exercises and 15% take home final and 15%
the in-class final. If time permits we will discuss various shrinkage
techniques such as Principle Component (PC), Partial Least Squares (PLS)
and the Continuum Regression Model (CRM) which are very useful in "Big
Data" applications. These techniques are discussed to better prepare you
for data analysis job interviews. While there is brief discussion of
spectral analysis in this course, more work is done on this topic in Econ
538. The lectures will be from Stokes Specifying and Diagnostically Testing
Econometric Models (1997, 200x) and Essentials of Time Series Modeling
(200x), both of which are in draft form and subject to change. These can be
downloaded from the class web page. The B34S software is available on smith
and will be given to all students in the course. Matlab is available in the
computer lab on the 7th floor and in the university labs. Some very basic
time series analysis (ARIMA, ACF, PACF) can be performed with Stata but the
capability for these time series applications is somewhat limited at this
timer. Data sets used in the course projects can be converted to Stata
format using b34s. b34sexec options ginclude('b34sdata.mac') member(fwheat)$ b34srun$
b34sexec stataio writestata file('fwheat.dct') heading('Wheat Data');
b34srun;
Some Stata help listed next program getdata
drop _all
infile using "gas.dct",clear
gen n=_n
tsset n
tsline gasout
end; program examples
corrgram gasout
arima gasout, arima(1,1,1)
arima gasout, ar(1)
arima gasout, ar(1/5)
arima gasout, ma(1/4)
end program modelgasout
arima gasout, ar(1,2,3)
predict modelfit
predict r, resid
ac r, name(top,replace)
pac r, name(middle,replace)
tsline gasout modelfit, name(bottom,replace)
graph combine top middle bottom, rows(3)
end program vargas_lag10
varbasic gasin gasout, lags(1,2,3,4,5,6,7,8,9,10)
varlmar , mlag(10)
varstable
varnorm
vargranger
end program vargas_lag6
varbasic gasin gasout, lags(1,2,3,4,5,6)
varlmar , mlag(6)
varstable
varnorm
vargranger
end * getdata
* examples
* vargas_lag10
* vargas_lag6
* import test.csv
import delimited test Late submission of projects and take home exams and attendance Unless given prior written permission, 15% per day will be taken off
late work. Students turning in their work on time in the past have been at
a disadvantage to those that turn in their work after having heard what
others have done. Class participation is key to learning how to use time
series techniques. Joint work You can work with a maximum of one other person but all students must
submit their own work. The write-ups of the two team members must be
unique. The idea is that it may be helpful to discuss results with someone
else but it is not beneficial to "farm out" work to your teammate and as a
result not master the material. Teams are formed informally but, once
formed, must stay together for the semester unless a "divorce" is
explicitly granted in writing. If you work with someone else you must list
that person's name on your front page. R is also a viable option for some time series problems, especially in E538
although error message are in general lacking. Problem Sets: There are 7 problem sets which are due on the 3th, 5th, 7th, 9th,
11th, 13th and 15th week of the course. These problem sets should be
typed and the output discussed. Results should be listed in the text and
selected computer outputs attached only to show your calculations.
Presentation of results is a key skill and will be given weight in the
final grade. Extra credit will be given if alternative software systems are
used to further analyze and validate the results. Computer Skills: After each class there will be a weekly "computer camp" to help clear
up any problems that have not been addressed in class. Unlike OLS modeling,
developing an ARMA filter takes practice. For that reason class time will
be devoted to mastering this skill. Students are encouraged to try to model
their own datasets. The B34S bjiden and bjest commands provide the basic
capability to identify and estimate a user specified model. The code for
these commands came from the Peck program that was developed for Box and
Jenkins. A major objective of the course is to train the student to
estimate such models. The matrix command autobj allows automatic model
identification and estimation. On a user PC it is possible to filter and
estimate more than a 1000 series and make forecasts in under a minute. This
allows a market trader to monitor a large number of stocks and make trade
within a 5 minute window. The models identified and estimated using autobj
can usually be beaten by the user with a "custom" model. However, users
can check their preliminary models with ACF analysis, inspection of the sum
of squares of the residual and against models against those identified by
automatic methods. Knowledge of the theory in necessary to use automatic
methods effectively. However in business applications, the large number of
series generally suggests that automatic methods are the best way to go. Recursive, out-of-sample forecasting simulations will give the user an
idea of whether the "custom" model, has in fact over-fit the data. The B34S
gamfit and marspline commands can be used to estimate AR(k) models using
threshold effects and can provide interesting and useful diagnostic tools
during the model building process. The B34S pls command can be used to
study the importance of various lags in a VAR model. Brief Road Map of Material Covered