e538.doc - Houston H. Stokes Page

Business Research and Forecasting II
Dr. Houston H. Stokes
722 UH
E mail hhstokes@uic.edu
TA :
Business Research and Forecasting II 1
Texts: 1
Computer Material: 3
General Outline of the course: 3
Lates and joint work 3
Problem Sets: 4
Problem set # 1 - Decomposition of VAR models into the frequency
domain and spectral forecasting 7
Problem Set # 2 - Identifying and Estimating VARMA models using real
data. 9
Problem Set # 3 - Decomposition of VAR Model. MARS Modeling. 10
Problem Set # 4 GAM and ACE Models. 15
Problem Set # 5 Random Forrest and PPREG Models 18
Texts: Stokes, Houston H., Specifying and Diagnostically Testing Econometric
Models, Second edition Quorum Books, 1997. Revised but preliminary
drafts on web for selected most chapters. Third edition two times
size of second edition. Please report any errors.
Hastie, Trevor and Robert Tibshirani and Jerone Friedman., The
Elements of Statistical Learning: Data Mining, Inference and
Prediction, Springer (ISBN 978-0-387-84857-0) Second Edition 2009
(This is a key reference for Data mining). To obtain PDF of book go to
web page of Trevor Hastie.
Stokes, Houston The Essentials of Time Series Modeling: An Applied
Treatment with Emphasison Topics Relevant to Financial Analysis in
manuscript form. See Chapters 1-8. Book can be obtained from web.
Revised and preliminary drafts on the web from the Economics 537 web
page. Please report any errors. (This consists of lecture notes.) Stock, James "Unit Roots, Structural Breaks and Trends," Chapter 46 in
Handbook of Econometrics Volume 4 Editors Engle & McFadden, North-
Holland, 1994 available at
http://www.econ.iastate.edu/faculty/bunzel/TTeach/pdf%20files%5C0204046
.pdf Watson, James, "Vector Autoregressions and Cointegration," Chapter 47
in Handbook of Econometrics Volume 4 Editors Engle & McFadden, North-
Holland, 1994 available at
http://www.elsevier.com/hes/books/02/04/047/0204047.pdf
Bollerslev, Tim and Robert Engle and Daniel Nelson, "ARCH Models,"
Chapter 49 in Handbook of Econometrics Volume 4 Editors Engle &
McFadden North-Holland, 1994. Hamilton, James, Time Series Analysis, Princeton, 1994. (Classic
highly technical reference). Crawley, Michael. The R Book,Wiley, 2007. (A good way to get going
with R for statistical analysis and programming).
Enders, Walter Applied Econometric Time Series. Third Edition, Wiley
2010. Tsay, Ruey, Analysis of Financial Time Series. Second Edition, New
York, Wiley 2005. (Good discussion of ARCH/GARCH modeling and other
methods that are of interest in financial data analysis.) Pena, Daniel, George Tiao and Ruey Tsay, A Course in Time Series
Analysis, New York, Wiley, 2002. (Very good survey articles on many
aspects of time series analysis that are more accessible than
Hamilton). Stokes, Houston and Hugh Neuburger., New Methods in Financial
Modeling; Explorations and Applications. Quorum Books, 1998. (Has a
number of applications of the various methods discussed in the
course). Computer Material: Doan, Thomas, RATS User's Manual Version 7 Estima, 2007 Doan, Thomas, Rats Reference Manua Version 7l. Estima 2007 Stokes, Houston H., "B34S On-Line Help Manual." Available on line. 450 plus
pages. "ARCH/GARCH and other Nonlinear Capabilities in the SCAB34S Applet
Collection." by Houston H. Stokes and Lon-Mu Liu. Available in Word 97 or
PDF format on the B34S page. 96 pages. General Outline of the course: The purpose of the course is to extend the students knowledge of
statistical time series analysis over what was learned in Economics 537. In
addition various machine learning/data mining methods are discussed.
Economics 538 is concerned with advanced transfer function model building,
spectral analysis, intervention analysis and vector AR model building,
vector ARMA model building. Students will also be introduced to the Geweke
approach to VAR model building, which decomposes a VAR model into the
spectral domain. Machine learning topics include MARS, ( spline GAM, ACE,
LOESS, CART and Random Forest models. There will be a take home final. The grading will be 50% computer
exercises and 25% take home final and 25% in class final. Lates and joint work Unless given prior written permission, 15% per day will be taken off
late work with a maximum of 2 days late allowed. 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. 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 team mate 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. If you work with someone else you must list that
person's name on your front page. There are a number of software products available to perform the
computer work. Students will not be required purchase any manuals. All B34S
documentation is on line. B34S® and RATS® can be used for all calculations,
although SAS® and SCA ®, which was developed by Professor, Liu in IDS, can
also be used if desired. B34S® and RATS setups are shown. Students can
download B34S versions to be run on their home machines. Be sure and get
the latest version. Problem Sets: There are 5 problem sets which are due on the 3th, 6th, 9th, 12th
and 14th 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.
Assignments: The readings in Stokes (1997, 200x), Stokes-Neuburger (1998) and Hastie-
Tibshirani-Friedman (2009) are required. Other readings are optional. Of
the optional readings, Hamilton is the most important, 1. Spectral Decomposition of VAR Model . Stokes (1997) Chapter 12 Sections 12.0 & 12.1 plus examples
. Stokes-Neuburger (1998) chapter 7
. Geweke (1982a, 1982b)
. Problem set # 1 due 3rd week 2. VARMA Model Building . Stokes (1997-200x) Chapter 8
. Stokes-Neuburger (1998) Chapter 4 & 6
. Zellner-Palm (1974) (see Economics 537 FTP location).
. Problem set # 2 due 6th week 3. MARS Modeling . Stokes(1997 revised) Chapter 14
. Stokes-Neuburger (1998) Chapter 4
. Faraway (2006) 240-246
. Hastie-Tibshirani-Friedman (2009) 321-328
. Problem Set # 3 due 9th week 4. GAM, ACE, LOESS and ( Spline Model Building, Boosting . Stokes (1997 revised) Chapter 14.
. Stokes-Neuburger (1998) Chapter 4
. Hastie-Tibshirani-Friedman (2009) 295-384
. Faraway
. Problem set # 4 due 12th week 5. Lasso and Elastic Net Models, Projection Pursuit and Random Forest
Models
. Hastie-Tibshirani-Frideman (2009) 557-624
. Stokes (1997 revised) Chapter 17.
. Stokes (1997 revised) Chapter 10 sections 10.3, 10.4 and 10.5.
. Problem Set # 5 Due 14th week
Take Home Final due 16th week
Problem set # 1 - Decomposition of VAR models into the frequency domain
and spectral forecasting Assignment: Be sure you have read carefully Stokes (1997) chapter 12
section 12.0 and 12.1 and Stokes-Neuburger (1998) Chapter 7 section 7.2. 1. Discuss the purpose of the Geweke procedure whereby a VAR model is
decomposed into the frequency domain. How is this used? What research
question does it answer? 2. Discuss the purpose of the bootstrap procedure. What is the role of
setting the number of replications? 3. Using the Lydia Pinkham data that you have been studying in problem # 6
question 3 and problem set #7 in Economics 537 decompose a VAR model of
order 10 into the frequency domain. Use 10, 100 and 1000 replications.
What do you find? Why are the estimated VAR coefficients not the same as
found with the B34S in assignment 3? Which are better? Why do we have
these two approaches? Software help. The below listed file shows a setup for 10 replications. /$ economics 538 project # 6
b34sexec options ginclude('b34sdata.mac') macro(lydiapnm)$
b34srun$
b34sexec varfreq datap=12 years(1954,1) yeare(1960,6)$
var advertis sales$
varf nlags=10 var(sales advertis) feedx(advertis) feedy(sales)
dummy=cons nrep=10 table('ads vs sales')
freq=(1.0 .9 .8 .7 .6 .5 .4 .3 .2 .1 0.0)$
b34seend$ 4. The below listed code will do out of sample forecasting for the Lydia
Pinkham data b34sexec options ginclude('b34sdata.mac') member(lydiapnm);
b34srun;
/$ user places RATS commands between
/$ PGMCARDS$
/$ note: user RATS commands here
/$ B34SRETURN$
/$ b34sexec matrix;
call echooff;
call loaddata;
call load(specfore); call print(' ':);
call print('Forecast of sales and Advertising':);
nfor=30;
base=60; /; base=68; call specfore(sales, base,nfor,0,fsales1,obs,error1,actual1);
call specfore(sales, base,nfor,2,fsales2,obs,error2,actual2);
call specfore(advertis,base,nfor,0,fadd1,obs,error3,actual3);
call specfore(advertis,base,nfor,2,fadd2,obs,error4,actual4); call print(' ':);
call print('With out Trend Correction':);
call tabulate(obs,actual1,fsales1,error1,actual3,fadd1,error3); call print('With Trend Correcti