Economics 323 Business Conditions Analysis Spring 2016 Dr. H. H. Stokes
Economics 323 (Version 9 November 2015)
Business Conditions Analysis
Dr. Houston H. Stokes
722 UH
Web Page
TA:
Required Text
Business Forecasting, John Hanke, Dean Wichern 9th ed Pearson / Prentice Hall, 2009
Optional References
Data Analysis Using Stata by Ulrich Kohler and Fraunke Kreuter. 3rd Edition, Stata Press, 2012.
Introduction to Time Series Using Stata by Sean Becketti. Stata Press, 2013
Introduction to Econometrics by Christopher Dougherty, 4th edition, Oxford University Press, 2011.
Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge, 5th edition, 2013, South Western Cengage Learning. This book is a bit expensive but is available from the Library.
“Notes on the Basics of Econometric Modeling geared to Dougherty (2011)" by Houston H. Stokes, is available on-line from the course web page and will be discussed in class. See file
Preliminary_Notes.docx
“Econometric Notes” by Houston H. Stokes contains some sections on important topics. It also provides an introduction to Statistics. See especially sections 1-3 and 5 which we will initially discuss. This document is available from the course web page. See file
Econometric_Notes.docx
Purpose of Course
The purpose of the course is to extend the student’s knowledge of Business Conditions Analysis by teaching statistical methods of business forecasting. This includes forecasting micro and macro data. Students will run the computer in class and get experience in a number of software systems. Students will be introduced to OLS and GLS analysis, nonlinear estimation of GLS models, recursive residual analysis options to test the stability of the estimated coefficients and simple time series ARIMA models.
Students will be asked to apply their knowledge in number of computer exercises which will be graded and a final exam, which is a take home. The grading will be 2/3 computer exercises and 1/3 open book final or an approved final project. Students can work in teams of two people but must turn in an individual paper with their name on it. If working in a team, be sure and indicate your team member on the cover sheet of your homework. Once a team is formed, it must stay together for the term. Students must have selected their teams by the third week or operate as a "lone wolf" by choice. Students are free to use software of their choosing. Stata and B34S will be discussed in the course as well as some Excel. The goal is to both teach how to do applied economic work and to allow students to list software they can use on their resumes. Students wanting free B34S software systems for their home machines will be allowed to obtain them.
Objectives
The main objective of the course is to give students knowledge that will allow them to apply the economic theory they have learned in other courses in a manner that will both be useful and will facilitate them obtaining a job. Unless economic knowledge is able to be applied, it is of substantially less value and will soon be forgotten. Since forecasting provides a competitive edge, a great deal of emphasis is placed on developing systematic forecasting skills. Systematic forecasting skills are those that can be replicated by others. Many students taking this class in prior years have used their projects in job interviews to illustrate their skills and to differentiate themselves from other job seekers.
Jobs
Since many of you will want to obtain jobs that use your economics training. The best way to make a good impression at the job interview is differentiate your skills from the skills of the other applicants. It has been found that student resumes that stress the fact that they have completed a successful econometric research project in the area of the job may have a "leg up" on those applicants that appears clueless at the interview stage. To achieve this end, students can mention the types of problems they have analyzed and the software they have used on their resumes. The project alternative is in place of the take home final and would be done by each individual student.
Computer Software
For many of you this will be your first time using the computer for serious work apart from the basics you have learned in the required stat courses. It is important that you do not shrink from this aspect of the course. Computer work is exacting and is necessary for research. The problem sets can be run on your own machine or from the labs where it is possible to open the class web pages and cut and paste commands into control files. Many students find it helpful to work together on their computer projects since they will be able to explain to each other what they are trying to do and learn to express themselves. What is important is to discuss results clearly and with understanding. Attaching masses of computer output is NOT a substitute for writing in a clear fashion. In business stress is laid on being clear and specific in what you have found. While many of the problems can be solved using Excel many cannot be solved using this limited software. For this reason we will be using Stata for much of our work.
Stata examples in this document provides a quick introduction to the software and will be discussed in class first to get you up and running. Since this document is on-line, you will be able to cut and paste the control files and get up and running very fast. Using the internet and cut and paste it is easy to run models.
Kohler & Kreuter (2012) provides more Stata detail, if that is needed. A student version of Stata is available from the UIC computer center for a nominal charge or can be accessed from various UIC labs.
Stata is available in SCE408, SELE 2249F, SELE 2249, and BSB 4133.
Students can obtain a copy of Stata that expires on 6/30/2015 for $90.00
Advanced references that are available for more detail on forecasting as needed.
- Chapter 2 of Specifying and Diagnostically Testing Econometric Models Houston H. Stokes, 2nd ed, Quorum Books 1997. Updated version available on line from web page.
- Chapter 7 of Specifying and Diagnostically Testing Econometric Models Houston H. Stokes, 2nd ed, Quorum Books 1997 Updated version available on line from web page.
- Chapter 9 of Specifying and Diagnostically Testing Econometric Models Houston H. Stokes, 2nd ed, Quorum Books 1997. Updated version available on line from web page.
- Stokes (200x)The Essentials of Time Series Modeling: An Applied Treatment with Emphasis on Topics Relevant to Financial Analysis Chapter 2 "Time series modeling objectives" available on line from web page.
- Stokes (200x)The Essentials of Time Series Modeling: An Applied Treatment with Emphasis on Topics Relevant to Financial Analysis Chapter 4 "Stationary Time Series Models" available on line from web page.
- Stokes (200x)The Essentials of Time Series Modeling: An Applied Treatment with Emphasis on Topics Relevant to Financial Analysis Chapter 5 "Estimation of AR(p), MA(q) and ARMA(p,q) Models" available on line from web page.
Quick Start: To get going assume you have a file of data on the age of 6 cars and their value
age value
1 1995
3 875
6 695
10 345
5 595
2 1795
Your goal is to estimate a model of the form
Your results should show
Value = 1852.9 -178.41 AgeR_2 = .672
(6.45)(-3.35)
which has been discussed in the overview notes on statistics.
A simple Stata setup for this problem is
input x y
1 1995
3 875
6 695
10 345
5 595
2 1795
end
list
summarize
regress y x
which if saved with the name
cars.do
can be is run in batch with the command
stata /e do cars.do
which produces in cars.log
______(R)
/__ / ____/ / ____/
___/ / /___/ / /___/ 12.1 Copyright 1985-2011 StataCorp LP
Statistics/Data Analysis StataCorp
4905 Lakeway Drive
College Station, Texas 77845 USA
800-STATA-PC
979-696-4600
979-696-4601 (fax)
Single-user Stata perpetual license:
Serial number: 3012042652
Licensed to: Houston H. Stokes
U of Illinois
Notes:
1. Stata running in batch mode
. do cars.do
. input x y
x y
1. 1 1995
2. 3 875
3. 6 695
4. 10 345
5. 5 595
6. 2 1795
7. end
. list
+------+
| x y |
|------|
1. | 1 1995 |
2. | 3 875 |
3. | 6 695 |
4. | 10 345 |
5. | 5 595 |
|------|
6. | 2 1795 |
+------+
. summarize
Variable | Obs Mean Std. Dev. Min Max
------+------
x | 6 4.5 3.271085 1 10
y | 6 1050 679.5219 345 1995
. regress y x
Source | SS df MS Number of obs = 6
------+------F( 1, 4) = 11.24
Model | 1702935.05 1 1702935.05 Prob > F = 0.0285
Residual | 605814.953 4 151453.738 R-squared = 0.7376
------+------Adj R-squared = 0.6720
Total | 2308750 5 461750 Root MSE = 389.17
------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------+------
x | -178.4112 53.20631 -3.35 0.028 -326.1356 -30.68683
_cons | 1852.85 287.3469 6.45 0.003 1055.048 2650.653
------
.
end of do-file
An alternative is to place this file in the Stata do file editor and execute it!
Data is saved on-line in the Excel directory. These can be accessed by Stata or Excel from your PC.
APPL Appliance Shipments Monthly 1983-1993
BEER Monthly Beer Production 1983-1993
BOATS Demand for Boats
CALDATA Monthly Sales of Electronic Calculators
CAMPERS Camp Development Decision
CAR Monthly Deliveries of Dorf Company
CASE10_1 Weekly Restaurant Sales
CASE10_4 Lydia Pinkham Annual Data 1907-1960
CASE2_2 Mr. Tux Rental Data
CASE4_1 Monthly Sales Data 1983-1995
CASE4_3 Consumer Credit Counseling Clients
CASE5_1 Solar Alternative Company Sales 93-94
CASE6_1 Tiger Transport Company - Weight & MPG
CASE6_2 Butcher Products Production Data
CASE6_3 Ace Personnel Company
CASE6_5 Credit clients
CASE7_1 Bond Market Dataset
CASE7_3 Mr. Tux with Dummy Variables
CASE7_4 Consumer Credit Counseling Extended Data
CASE8_1 Small Engine Doctor Data
CASE8_2 Case Study 8_2 Tux Data
CASE8_4 AAA Emergency Road Call Data 1988 - 1993
CASE9_5 Full AAA Emergency Data Jun 87 - Jul 93
CENEX Cenex Chemical Process
COMPANY Data Not Found
DAY90 Quarterly 90-day Treasury Bills
DISINC Disposable Income 1955 - 1985
EDPRICE Price of Higher Education 70-93
EGGS Production of Eggs 1961-1992
EMPLOYEE Employee Study
EX10_3 Daily Transportation Index Close
EX10_4 Readings from Atron Process
EX10_5 Errors of Atronm Quality Control
EX10_6 Errors for Ed Jones Quality Control
EX10_7 Closing Stocks of ISC
EX10_8 Keytron Sales
EX4_1 VCR's Sold
EX4_3 40 Random Numbers
EX4_4 Sears Sales
EX4_5 Outboard Marine 1984-1996
EX5_1 Acme Tool Company
EX5_4 Weekly Movie Video Rentals
EX6_1 Milk Sales
EX6_11 Hardware Advertising and Sales
EX7_1 Milk vs Price vs Advertising
EX7_12 Washington Power Usage
EX7_5 Job Performance
EX7_7 Food Expenditure
EX7_8 Zurenko Pharmaceutical Company
EX8_10 Quarterly Sales Outboard Marine
EX8_2 New Passenger Cars in the United States 60-92
EX8_8 Monthly Registration of Cars 1986-1992
EX9_1 Reynolds Metals 1976-1996
EX9_3 Novak Corp Sales 1980-1996
EX9_4 Yearly Sears Sales 1976-1966
FARMS Number of US Farms 1975-1993
FORREST Forest Products Car Loadings
FURNACE Shipments of Furnaces 1982-1990
GNP GNP 1950 - 1991
HANKEINFO Lists of Hanke Datasets
HOMES Demand for Motor Homes
HOUSEHOLD Household vs populations
IMPORTS National Imports for the years 1967 to 1986
INVEST Non residential Investment 1950-1989
KINSTON Monthly Sales of Kinston
MARRIAGE Number of Marriages 1965-1989
MEDIAN Population Dataset
MOODY Electric Utility Stocks Annual Ave
MOTEL Monthly Occupancy For Model 9 1987-1996
PAPER Monthly Demand for Paper Products
PE Quarterly Industrial P/E Ratio
PERFUME Monthly Demand for Perfume
PR10_10 80 obs of data
PR10_11 96 obs
PR10_12 IBM Stock Quotes
PR10_13 Daily DEF Corporation stock
PR10_14 Weekly Auto Accidents 1984-1985
PR10_15 Corn Price in Spokane Washington
PR10_7 Chips Bakery
PR10_8 126 obs of test data
PR10_9 80 Obs of test data
PR2_1 Customer Transactions
PR2_10 Books Sold vs Shelf Space
PR2_13 Random Stock Vs Temp Data
PR2_2 Housing Prices
PR2_7 Family Sizes
PR2_9 Maintenance of Buses
PR4_13 Marriages in US 85 - 92
PR4_17 Quarterly Loans Dominion Bank
PR4_20 Earning Per Share Price Company
PR5_11 Demand for Hughes Supply
PR5_12 Asset Value General American Investors
PR5_13 Revenues from Southdown Inc
PR5_14 Triton Sales per share
PR5_15 Revenues for the Consolidated Edison Company
PR5_6 Apex Mutual Fund Price
PR5_9 Davenport Bond Yield
PR6_11 Building Permits vs Interest Rate
PR6_12 Print Cost Data
PR6_13 Defective parts vs batch size
PR6_3 Sales vs Advertising
PR6_4 Checkout time vs Value of Purchase
PR6_5 Maintenance cost vs age
PR6_6 ooks Sold vs Shelf Space
PR6_7 Orders vs Catalogues
PR6_8 Yearly Investment vs Interest Rate
PR6_9 Forecasting a Competitor bid
PR7_10 Sales = f(# outlets, # cars)
PR7_11 # Registered autos = f( )
PR7_13 What Makes a winning baseball team
PR7_15 Presto Sales
PR7_8 Checkout time vs Purchase & # items
PR8_11 Capital Spending 1977 - 1993
PR8_13 Spending on TV Ads
PR8_19 Goodyear Tires Sales 1985-1996
PR8_20 Retail Sales Data
PR9_10 Gas Consumption in the US
PR9_11 Resort Study
PR9_12 Revenue Data
PR9_13 Shareholder Data
PR9_14 passengers who flew on Thompson Airline planes.
PR9_15 Thomas Furniture Company sales
PR9_16 Dicksen vs Industry Sales
PR9_17 Savings in period 1935 - 1954
PRES New Prescriptions 1990-1996
PRIME1 Quarterly Prime Rate 1985-1991
PRIME2 Monthly average prime 1945-1995
RAILROAD Railroad Labor Annual Average Cents Per Hour
REFILL Monthly refill data 1983-1990
RIDERSHIP Daily Bus Ridership
SALARY Age vs Salary Data
SEARINC Sears Sales 1955 - 1985
STATIONS Number of TV stations changing hands in 1991
STOCK S & P Monthly 1945-1995
TRAVEL US Citizen departures 1961-1991
WASH Boats - Motor Homes - Income
WELLHEAD Average Wellhead Price, Natural gas 72-93
WOOD Monthly Wood Production 1992-1996
Problem Sets, Final/Project:
There are 5 problem sets. These will be due on the 4th, 6th, 9th, 11th and 13th week for problem sets 1 - 5 respectively. Lates will not be accepted unless there is prior written approval. Problem set answers should be typed and carefully laid out. Since they will be 2/3 of the grade in the course, care should be taken in their preparation. If past classes are any indication, an impressive layout can be leveraged in a subsequent job interview to illustrate your capability to solve "real world" problems using modern methods of analysis. The remaining 1/3 of the grade is either the student project which involves obtaining data from the web or other sources and estimating a regression and ARIMA model OR taking the take home final. The objective of the project is to give students a paper which illustrates their capabilities which they can use in job interviews to further show their capability. If you have a job goal in one specific industry, it is a good idea to select a paper topic that is related to this area. Further information on the project is contained below.
Assignments
I. Introduction to Data Analysis
-Hanke-Wichern Chapters 1-2
-Stokes [5], [6]
II. Regression Analysis
-Hanke-Wichern Chapter 6, 7
-Stokes Econometric_Notes
-Stokes (1997) Chapter 2 listed as [2]
-Stokes (2004) Chapter 2 listed as [9]
III. Recursive Residual Analysis
-Stokes (1997) Chapter 9 listed as [4]
IV. ARIMAModelBuilding - Identification and estimation.
Stokes (1997) Chapter 7 listed as [3]
Stokes (2004) Chapter 4 "Stationary Time Series Models" listed at [10]
Stokes (2004) Chapter 5 "Estimation of AR(p), MA(q) and ARMA(p,q) Models" listed as [11]
Hanke-Wichern Chapter 8 and 9
Problem Sets
- Introductory Statistical Analysis - Due 4th week
2. Estimation and Testing of Regression Models. Due 6th week.
3. Applied Econometric Analysis. Due 9th week
4. Identification of ARIMA Models using real and generateddata. Due 12th week
5. Estimation of ARIMA Models. Due 14th week.
Terms and concepts to understand
OLS Model – define, state assumptions and discuss the effect on the estimates or standard errors if the assumptions of OLS are not met
Multicollinearity
Simultanity
Exogenous, Endogenous
Differences-in-Differences model – define and discuss interpretation
Regression discontinuity
Proxie variable
Probit, logit and tobit model – define and show how used.
Panel Data – define and slow the advantages and disadvantages of Fixed effects model, random effects model.
Instrumental variable - Define and show how used. Be able to discuss and give examples.
Effects of serial correlation, heteroskedasticity and model specification on estimated coefficients, estimated standard errors and the ability of a model to accurately draw inferences.
Population
Sample
Sample selection bias.
Datasets:
Datasets for the problem sets are on-line under the course web page.
Datasets for SAS are in the class FTP location in
ftp.uic.edu/pub/depts/econ/hhstokes/e323/sas_files/
Datasets for Excel / Stata are in the class FTP location in
ftp.uic.edu/pub/depts/econ/hhstokes/e323/excel_files/
Problem Set # 1 Introductory Statistical Analysis
Goals:Introduction to Computer
Use Data Sampling
- Hanke-Wichern (2009) page 50 # 12 list data on the number of books sold (BSOLD) and the feet of shelf space (SHELFS). You are asked to calculate the correlation BSOLD and SHELFS. In addition run a regression of BSOLD = f(constant SHELFS). Draw a skatter diagram. Use your model to predict how many books will be sold if the shelf space is 8.23. For software you can use any program that you would like.
The means obtained should be:
Variable Label # Cases Mean Std. Dev. Variance Maximum Minimum
WEEK 1 Week Data Collected 11 6.00000 3.31662 11.0000 11.0000 1.00000
BSOLD 2 Books Sold 11 210.182 54.7153 2993.76 295.000 125.000
SHELFS 3 Shelf Space 11 4.88182 1.42816 2.03964 7.70000 3.10000
CONSTANT 4 11 1.00000 0.00000 0.00000 1.00000 1.00000
Stata will run the problem with statements:
input week bsold shelfs
* Data from Hanke - Wichern Edition 9 page 50 # 12
* Data from Hanke - Wichern Edition 8 page 48
* Data from Edition 7 page 44
1 275 6.8
2 142 3.3
3 168 4.1
4 197 4.2
5 215 4.8
6 188 3.9
7 241 4.9
8 295 7.7
9 125 3.1
10 266 5.9
11 200 5.0
end
label variable week "Week Data Collected "
label variable bsold "Books Sold "
label variable shelfs "Shelf Space "
summarize
describe
set graphics on
graph twoway scatter bsold shelfs, saving(graphp1_1)
corr
regress bsold shelfs
2. Hanke-Wichern(2009) page 50 problem 13 shows a dataset of 200 weekly observations of temperature in Spokane (Temp) and the number of shares of stock trades for Sunshire Mining (Shares). The dataset is Pr2-9.xls, You are asked to calculate the correlation between Shares and Temp and run a model Shares = f(constant, Temp). Code to load from a file is shown below shown below together with You can either run this problem with a modified script in “batch mode” or load the datafile directly into Stata from the web and give the appropriate commands.
import excel using "c:\master\master1\class\e323\hanke\excel\ch2\pr2-9.xls",firstrow
summ
* list
corr
regress Shares Temp
*
* Alternative ways to do a bootstrap
*
regress Shares Temp, vce(bootstrap, reps(400) seed(10101))
*
* Here we see what the coef look like using resampling techniques.
bootstrap _b _se, reps(400) seed(10101):regress Shares Temp
matrix list e(b_bs)
*
regressShares Temp, robust
When you load the data means should be:
Variable | Obs Mean Std. Dev. Min Max
------+------
C1 | 200 100.5 57.87918 1 200
Shares | 200 48.86 29.28461 0 99
Temp | 200 47.75 28.176 1 99
Discuss in detail what you find in terms of coefficients and SE’s .