STUART SCHOOL OF BUSINESS

Econometric Analysis

Wal-Mart Stores

Rohan S. Patil

2/16/2008

Wal-Mart Sales Econometric Analysis MSF 562

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Wal-Mart: Net Monthly Sales Econometric Analysis

MSF 562

Abstract

This report contains a description of econometric analyses of net monthly sales of Wal-

Mart Stores, Inc. The analyses are focused on:

Macroeconomic factors indicating the state of the overall economy

Various macro-economic factors affecting the production costs

The overall consumer sentiment and disposable income

Miscellaneous factors affecting sales ( e.g. retail gas price)

I begin with the description of the business of Wal-Mart and their various internal

segments. In part II, I will explain all the macro-economic indicators and their intuitive

effects on the net sales of a company in the retail segment.

Part III explains the initial stages of the analyses wherein I shall de-trend the time-series

data and then remove the seasonality effects.

Part IV discusses the construction of the econometric model and tests of significance.

The advanced analysis is presented in part V wherein the model will be tested for

possible autocorrelation and heteroskedasticity.

In part VI, the concluding remarks, I shall elaborate on the pattern in the monthly net

sales of Wal-Mart as explained by all the significant independent variables. I have

appended an appendix at the very end of the report which supports the analyses with

graphs, tables and also shows all the important statistics.

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I. Introduction WWaall--MMaarrtt SSttoorreess

NYSE Code: WMT Sector: Variety Stores

Business Description:

Wal-Mart is the world’s largest general merchandise retailer, operating nearly 6,800

stores worldwide. It is the world's largest public corporation by rreevveennuuee,, according to the

2007, Fortune Global 500.

Wal-Mart has three main divisions as far as the revenues are concerned. They are Wal-

Mart stores, Sam’s club and international sales. Wal-Mart also deals with online product

selling through the Internet which is aggregated into three divisions mentioned above.

Among these business divisions, WWaall--MMaarrtt SSttoorreess DDiivviissiioonn,, UU..SS.. is Wal-Mart's largest

business subsidiary, accounting for 67.2% of net sales for financial year 2006. It consists

of three retail formats that have become commonplace in the United States: Discount

Stores, Supercenters, and Neighborhood Markets.

WWaall--MMaarrtt DDiissccoouunntt SSttoorreess are discount department stores which carry general

merchandise and a selection of food. Many of these stores also have a garden center, a

pharmacy, Tire & Lube Express, optical center, one-hour photo processing lab, portrait

studio, and a fast food outlet. Some also sell gasoline.

WWaall--MMaarrtt SSuuppeerrcceenntteerrss are hypermarkets with an average size of about 197,000 square

feet. These stock everything a Wal-Mart Discount Store does, and also include a full-

service supermarket, including meat and poultry, baked goods, frozen foods, dairy

products, garden produce, and fresh seafood. Many Wal-Mart Supercenters also have a

garden center, pet shop, pharmacy, Tire & Lube Express, optical center, one-hour photo

processing lab, portrait studio, and numerous alcove shops, such as cellular phone stores,

hair and nail salons, video rental stores, local bank branches, and fast food outlets. Some

also sell gasoline.

WWaall--MMaarrtt NNeeiigghhbboorrhhoooodd MMaarrkkeettss are grocery stores. They offer variety of products,

including full lines of groceries, pharmaceuticals, health and beauty aids, photo

developing services, and a limited selection of general merchandise.

SSaamm''ss CClluubb is a chain of warehouse clubs which sell groceries and general merchandise

in large quantities. Sam's has found a niche market in recent years as a supplier to small

businesses. According to Wal-Mart's 2007 Annual Report, Sam's Club's annual sales

were $42 billion, or 12.1% of Wal-Mart's total sales.

WWaall--MMaarrtt''ss iinntteerrnnaattiioonnaall operations currently comprise 2,980 stores in 14 countries

outside the United States. According to Wal-Mart's 2006 Annual Report, the International

division accounted for about 20.1% of sales. There are wholly-owned operations in

Argentina, Brazil, Canada, Puerto Rico (although PR is part of the US, the company's

operations there are managed through its international division), and the UK. With 1.8

million employees worldwide, the company is the largest private employer in the US and

Mexico, and one of the largest in Canada.

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II. Macroeconomic and other Indicators (Independent variables)

1. Consumer Price Index (CPI):

As a proxy for inflation CPI is used to analyze the effects of inflation in the

overall economy on the net sales.

As Wal-Mart sells a variety of products ranging from food to fuel, it is interesting

to analyze how the sales of essential and non-essential items are affected by

inflation.

Since the prices of essential items such as food and energy are much more volatile

compared to the other items I used several indicators such as CPI – for all items,

all but food, only food, food and energy, all but food and energy. 2. Unemployment Rate:

Keynesian economics emphasizes unemployment resulting from insufficient

effective demand for goods and service in the economy (cyclical unemployment).

Thus, this variable has been used as a proxy for the state of the overall economy.

3. Consumer Sentiment Index: (This is similar to the Consumer Confidence Index)

CSI is a closely watched barometer of where the economy might be headed next.

It is defined as the degree of optimism on the state of the economy that consumers

are expressing through their activities of savings and spending. The main Index of

Consumer Sentiment is based on the results of two subset indices: the Index of

Current Economic Conditions, which explores consumers’ thinking about their

current finances and buying plans, and the Index of Consumer Expectations,

which is designed to gauge consumers’ outlook over the coming one-and five-

year periods.

4. Gasoline Prices:

I have used the monthly prices of gasoline available to retail customers. Most of

the customers of Wal-Mart travel to the stores by a car. Thus, I think it is

interesting to analyze the relationship between gasoline prices and sales.

5. Consumer Credit Outstanding

Consumer debt is consumer credit which is outstanding. In macroeconomic terms,

it is debt which is used to fund consumption rather than investment.

The permanent income hypothesis suggests that consumers take debt to smooth

consumption throughout their lives, borrowing to finance expenditures earlier in

their lives and paying down debt during higher-earning periods. Thus the amount

of debt in the economy may affect retail sales.

Wal-Mart Sales Econometric Analysis MSF 562

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Dummy explanatory variables:

1. Dummy for December Sales:

After observing the data we find that the December sales are substantially higher

than the rest of the months. A plausible reason for this seasonal effect could be the

holiday season.

In order to offset this effect, a dummy variable has been used which captures the

increased sales due to holiday spending.

2. Dummy for November Sales:

The data on the November sales for the years 1996-2006 were considerably

higher than other months even though it was reported only for four weeks. Thus

we add a dummy variable for the sales in November.

3. Dummy for number of weeks (as reported):

This dummy variable is introduced in order to remove the effect of the number of

weeks over which the sales have been reported by the company.

The company publishes the data for monthly sales in the following pattern: 4,4,5 -

4,4,5 - 4,4,5 -4,4,5. These are the number of weeks included in the monthly sales.

This dummy variable has no economic significance and thus we will not discuss

this further.

Following is the list of all the variables with their names and short-forms:

Independent Variables.*

Short-Form Name

1 x1 Consumer Sentiment Index

2 x2 CPI- All

3 x3 CPI – All but food

4 x4 CPI- All but food energy

5 x5 CPI- All but energy

6 x6 CPI for food only

7 x7 Consumer(individual) loans-non-revolvingOutstanding

8 x8 Consumer credit( revolving) Outstanding

9 x9 Unemployment

10 x10 PPI- Finished Consumer Goods

11 x11 PPI- Fuels and Power

12 x12 PPI- Finished Consumer Foods

13 x13 Motor Gasoline Retail Prices, U.S. City Average

14 x14 Disposable Income

15 dummy1 Number of weeks ( 1 = 5 weeks, 0 = 4 weeks)

16 dummy2 December sales

17 dummy3 November sales

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III. De-trending, Seasonality and Co-linearity: In this section, we will start of with de-trending of the dependent and independent

variables. After this I adjusted the log (sales) for seasonality using the dummy

variables for the month of November and December. Additionally I added a dummy

for number of weeks. All my independent variables (macro-economic indicators) are

adjusted for seasonality.

Following is the regression that I carried out to de-trend y.

utimeliney 10 …… RReegg..11

*Refer appendix for details of this regression.

I obtained following equation after this regression:

utimeliney 0.0099882.126634

(0.0308) (0.000368)

n = 142, 2R = 83.84%

We find out the residuals from the above regression (i.e. )log( sales using the

following equation:

yyy ˆ 1

These residuals will be used for the purpose of our analysis in place of log (sales).

Similarly, all the independent variables were de-trended and henceforth the residuals

of these regressions will be used for the furtherance of the analysis.

Now let us turn to the adjustments for the seasonality in the net sales of Wal-Mart.

From the residual plot of regression 1*, we note that the net sales are substantially

higher in November and December of each year. In order to partial out this effect I

used two dummies (one for each month).

Following is the equation for removing seasonality:

udummydummydummyy 32 32110 …… RReegg..22

I obtained the following equation after this regression:

udummydummydummyy 321 0.126337 0.326261 0.228121-0.11455

(0.0084) (0.0153) (0.025511) (0.023636)

n = 142, 2R = 82.27%

We use the residuals of this regression for further analysis:

yyy

1. y is the predicted value of y by the equation. y is log (sales) after de-trending and y is after

de-seasonality.

* Refer the appendix for the plot.

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Proceeding with the analysis of the data, we check all probable independent variables

for a possible co-linearity. In case two variables are found to be highly collinear then

one of them is rejected.

After calculating the correlation between the independent variables I found the

following:

1. Variables x2 and x3 are almost the same.

2. Variable x2 is highly correlated with variables x10, x11 and x13.

3. Variable x4 is highly correlated with variable x5.

4. Variable x12 is highly correlated with variable x6.

5. Variables x10 and x13 are also correlated.

*Refer appendix for the entire correlation matrix. Thus we reject variables x3, x4, x5, x10, x11 and x13.

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IV. Econometric Model:

Following is the chart of y plotted against time:

There is a definite change in the slope of this graph after about 70 samples. The most

interesting thing here is sample 69 represents the month of September 2001. Thus, I

selected this point as my crossover point and performed a piece-wise linear

regression by including a binary variable as well as its interaction terms with all the

independent variables.

log(sales)After removing the seasonality

-0.25

-0.2

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-0.1

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0

0.05

0.1

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0 20 40 60 80 100 120 140 160

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Initial econometric model is setup as follows:

14141313121299887766221100 xxxxxxxxxdummyy

uxxxxxxxxxdummy )(* 1414131312129988776622111

…… RReegg..33

I found following variables to be significant (and on the verge of significance):

Variable β t-Stat

1 x6 -0.01252 -1.65666

2 x7 -0.00145 -3.45889

3 x8 0.00109 2.988242

4 x9 -0.02523 -1.43381

5 dummy*x7 -0.00024756 -1.77712

6 dummy*x12 2.27462E-06 2.2746675

* Refer appendix for further details.

Then, I ran the regression with only significant factors included in the model.

udummyxdummyxxxxy 121771998877660 * …… RReegg..44

Following are the significant betas and their t-stats.

Variable β t-Stat

1 x7 -0.0011185 -3.95484

2 x8 0.001177037 4.170627

3 x9 -0.04566579 -3.17542

4 dummy*x7 -0.00018785 -2.26428

5 dummy* x12 9.86155E-07 1.895653

We get an adjusted R2 of 22. 18 %.

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V. Heteroskedasticity and Autocorrelation:

I conducted the Breusch-Pagan test on the residuals from the regression 4 to

check for the existence of heteroskedasticity in the residuals.

BBrreeuusscchh--PPaaggaann tteesstt::

Model: vdummyxdummyxxxxu 121771998877660

2 *ˆ

…… RReegg..55 * Refer appendix for details.

Null hypothesis:

H0 : βi = 0 … for all i

Alternate Hypothesis:

H1 : βi ≠ 0 … for all i

The joint significant of all the β’s is 1.145223. Thus, we fail to reject the null

hypothesis.

Breusch- Pagan test does not indicate presence of heteroskedasticity. Thus, in order to

further analyze the relationship of u with x’s I performed the White Test.

WWhhiittee TTeesstt::

Model: vyyu 2

210

2 ……RReegg..66

* Refer appendix for details.

Null hypothesis:

H0 : βi = 0 … i [1, ]

Alternate Hypothesis:

H1 : βi ≠ 0 … i [1, ]

The joint significant of all the β’s is 1.213656. Thus, we fail to reject the null

hypothesis.Thus, after both these tests fail to identify I assumed that the

heteroskedasticity is absent.

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Now we shall test for the auto correlation in these residuals by running the following

regression:

vuu tt 110 ……RReegg..77

Null hypothesis:

H0 : β1 = 0 … i [1, ]

Alternate Hypothesis:

H1 : βi ≠ 0 … i [1, ]

We the following equation:

vuu tt 10.038965-0.00056

(0.0039) (0.0836)

n = 140, 2R = -0.558 %

The t- stat of β1 is 0.466. Thus, it is not statistically significant from zero.

Thus, the residual are serially uncorrelated.

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VI. Concluding Remarks: After the econometric analysis I got the following model:

udummyxdummyxxxxy 121771998877660 *

log (sales) -d-s* =

uxdummyxdummyxxx 127987 *07-9.861E* 0.000188- 0.0457- 0.00118 0.001118-0.22183

X7 Consumer loans Outstanding (individual-non-revolving)

X8 Consumer credit( revolving) Outstanding

X9 Unemployment

X12 PPI- Finished Consumer Foods

n= 142, 2R = 22.134%

BBeeffoorree SSeepptteemmbbeerr,, 22000011::

log (sales) –d-s = uxxx 987 0.04566- 0.001177 0.0011185-0.22183

AAfftteerr SSeepptteemmbbeerr,, 22000011::

log (sales) –d-s =

uxxxx 12987 *07-9.86155E 0.04566- 0.0011770.00130635 -0.22183

Interpretation of betas:

The beta corresponding to Consumer loans outstanding (individual non-revolving) is -

0.0011185. This goes to show that as the consumer debt (non-revolving) in the

economy increases, ceteris paribus, the net effect on Wal-Mart sales is negative. It

concurs with the fact that as people take on more and more debt their spending

capacity goes down resulting in lower sales.

It is interesting to note that the effect of revolving credit is exactly opposite. When the

revolving credit goes up, ceteris paribus, the sales also go up. Most of the people do

pay by credit card and thus defer the payment by some time. If the revolving credit is

going down, that means people are struggling to pay their last months credit. Thus,

the sales for that particular month would be low.

* log (sales)-d-s is the log(sales) after de-trending and removing the seasonality.

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Unemployment rate has a negative impact on the net sales. This makes sense because

the unemployment rate is low when the economy as a whole is doing good and it

increases during recessions. Thus intuitively the sign of this beta should be negative.

One more interesting thing to note is, post- September, 2001 the Producer’s Price

Index for finished consumer food is significant.

After analyzing the net sales of Wal-Mart I found the following factors to be

significant: 1. Consumer Loans (revolving and non-revolving credit)

2. Unemployment

3. Producer’s Price Index

With the help of these parameters, my econometric model could explain 22% of the

variations in the net-sales of Wal-Mart.

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VII. Further Studies: After regressing log (sales) with respect to time and time

2, I found a definite cycle of

sales as shown in the following graph.

This pattern needs further attention. In my opinion it would not be appropriate to use

piecewise linear regression four times on this graph.

Also, I have not considered the impact of the foreign exchange rate on the net sales.

In order to do that, one needs to analyze their operations in foreign markets in detail.

The data regarding this was not available to me.

Also, the store has a strategy of reducing there Wal-Mart stores and increasing the

number of Supercenters over last 10 years. The reasons behind this trend are

unknown. It is clear that this has a significant impact on the sales.

log(sales) regressed against t and t2

-0.2

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0

0.05

0.1

0.15

0.2

0 20 40 60 80 100 120 140 160

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VIII. Appendix: Part II. Following table shows the sources of data used in the econometric model:

Name Source

Consumer Sentiment Index Survey Research Center: University of Michigan*

CPI- All U.S. Department of Labor: Bureau of Labor Statistics*

CPI – All but food U.S. Department of Labor: Bureau of Labor Statistics*

CPI- All but food energy U.S. Department of Labor: Bureau of Labor Statistics*

CPI- All but energy U.S. Department of Labor: Bureau of Labor Statistics*

CPI for food only U.S. Department of Labor: Bureau of Labor Statistics*

Consumer(individual) loans-non-revolving Outstanding Board of Governors of the Federal Reserve System1

Consumer credit( revolving) Outstanding Board of Governors of the Federal Reserve System1

Unemployment U.S. Department of Labor: Bureau of Labor Statistics*

PPI- Finished Consumer Goods U.S. Department of Labor: Bureau of Labor Statistics*

PPI- Fuels and Power U.S. Department of Labor: Bureau of Labor Statistics*

PPI- Finished Consumer Foods U.S. Department of Labor: Bureau of Labor Statistics*

Motor Gasoline Retail Prices Energy Info. Administration**

Disposable Income Bureau of Economic Analysis

Net Sales of Wal-Mart www.walmartfacts.com

Part III: Regression 1

Summary Output for regression 1:

Regression Statistics

Multiple R 0.9163

R Square 0.8396

Adjusted R Square 0.8384

Standard Error 0.1824

Observations 143.0000

ANOVA

df SS MS

Regression 1.0000 24.5447 24.5447

Residual 141.0000 4.6907 0.0333

Total 142.0000 29.2354

Coefficients Standard Error t Stat

Intercept 2.1266 0.0308 68.9772

X Variable 1 0.0100 0.0004 27.1625

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Following chart shows the residuals of the above equation plotted against time:

We can clearly see the seasonality in the sales.

Regression 2

Following is the summary output for regression 2:

Regression Statistics

Multiple R 0.9091

R Square 0.8264

Adjusted R Square 0.8227

Standard Error 0.0765

Observations 143.0000

ANOVA

df SS MS F

Regression 3.0000 3.8765 1.2922 220.6083

Residual 139.0000 0.8142 0.0059

Total 142.0000 4.6907

Coefficients Standard

Error t Stat P-value

Intercept -0.1146 0.0084 -

13.6362 0.0000

X Variable 1 0.2281 0.0153 14.9359 0.0000

X Variable 2 0.3263 0.0255 12.7890 0.0000

X Variable 3 0.1263 0.0236 5.3450 0.0000

De-trending of log(sales)

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-0.2

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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 20 40 60 80 100 120 140 160

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Following chart shows the residuals of the above equation plotted against time:

Correlation Matrix of independent variables

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14

x1 100% -32% -34% -40% -37% -12% -25% -63% -54% -21% -22% -10% -17% 6%

x2 -32% 100% 100% 45% 65% 78% 70% -15% -28% 94% 88% 68% 88% 79%

x3 -34% 100% 100% 45% 63% 72% 66% -14% -28% 93% 88% 63% 88% 76%

x4 -40% 45% 45% 100% 93% 35% 3% 18% -1% 20% 5% 9% 17% 35%

x5 -37% 65% 63% 93% 100% 66% 30% 7% -9% 45% 26% 40% 39% 57%

x6 -12% 78% 72% 35% 66% 100% 74% -20% -20% 79% 59% 88% 69% 77%

x7 -25% 70% 66% 3% 30% 74% 100% 7% 13% 77% 68% 81% 72% 58%

x8 -63% -15% -14% 18% 7% -20% 7% 100% 79% -21% -15% -13% -23% -39%

x9 -54% -28% -28% -1% -9% -20% 13% 79% 100% -31% -30% -8% -30% -44%

x10 -21% 94% 93% 20% 45% 79% 77% -21% -31% 100% 94% 79% 92% 78%

x11 -22% 88% 88% 5% 26% 59% 68% -15% -30% 94% 100% 61% 89% 64%

x12 -10% 68% 63% 9% 40% 88% 81% -13% -8% 79% 61% 100% 64% 69%

x13 -17% 88% 88% 17% 39% 69% 72% -23% -30% 92% 89% 64% 100% 70%

x14 6% 79% 76% 35% 57% 77% 58% -39% -44% 78% 64% 69% 70% 100%

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-0.2

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-0.1

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0

0.05

0.1

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0 20 40 60 80 100 120 140 160

log(sales)After removing the seasonality

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Part IV:

Regression 3

Following is the summary output of the regression 3.

Regression Statistics

Multiple R 0.55637629

R Square 0.30955458 Adjusted R Square 0.22187897

Standard Error 0.04871806

Observations 143

ANOVA

df SS MS F Significance

F

Regression 16 0.134078 0.00838 3.530681 3.12103E-05

Residual 126 0.299055 0.002373

Total 142 0.433133

Coefficients Standard

Error t Stat P-value

Intercept 0.12715894 0.086435 1.471144 0.143745

X Variable 1 0.00017445 0.001213 0.14376 0.88592

X Variable 2 0.01051257 0.007505 1.400746 0.163748

X Variable 3 -

0.01251542 0.007555 -1.65666 0.100076

X Variable 4 -

0.00144774 0.000419 -3.45889 0.000741

X Variable 5 0.00108883 0.000364 2.988242 0.003374

X Variable 6 -0.0252253 0.017593 -1.43381 0.154105

X Variable 7 0.00172228 0.003178 0.541975 0.588793

X Variable 8 8.1943E-05 8.25E-05 0.993048 0.32259

X Variable 9 0.00218985 0.00184 1.190241 0.23619

X Variable 10 -

0.00136803 0.001372 -0.99728 0.320541

X Variable 11 -

0.00132252 0.001343 -0.98508 0.326474

X Variable 12 -

0.00024756 0.000139 -1.77712 0.077961

X Variable 13 9.2486E-05 7.89E-05 1.172521 0.2432

X Variable 14 -1.7361E-

05 1.49E-05 -1.1655 0.246019

X Variable 15 2.2746E-06 1E-06 2.274668 0.024616

X Variable 16 1.7078E-08 1.23E-08 1.385531 0.168338

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Regression 4

Following is the summary output of the regression 4.

Regression Statistics

Multiple R 0.520120024

R Square 0.270524839 Adjusted R Square 0.226974083

Standard Error 0.048558299

Observations 143

ANOVA

df SS MS F Significance

F

Regression 8 0.117173219 0.01464

7 6.21171397

7 8.11294E-

07

Residual 134 0.31595972 0.00235

8

Total 142 0.433132939

Coefficients Standard Error t Stat P-value

Intercept 0.227716535 0.071310393 3.19331

5 0.00175319

8

X Variable 1 0.00421282 0.005668712 0.74317

1 0.45867939

2

X Variable 2 -0.005142913 0.005017097 -1.02508 0.30717358

3

X Variable 3 -0.0011185 0.000282818 -3.95484 0.00012348

4

X Variable 4 0.001177037 0.000282221 4.17062

7 5.42361E-

05

X Variable 5 -0.045665729 0.014381006 -3.17542 0.00185683

X Variable 6 -0.000187854 8.29642E-05 -2.26428 0.02516273

6

X Variable 7 9.86155E-07 5.20219E-07 1.89565

3 0.06015950

5

X Variable 8 2.24494E-09 8.62679E-09 0.26022

9 0.79508657

6

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Part V:

BBrreeuusscchh--PPaaggaann tteesstt::

Regression Statistics

Multiple R 0.252974374

R Square 0.063996034 Adjusted R Square 0.0081152

Standard Error 0.004096669

Observations 143

ANOVA

df SS MS F Significance

F

Regression 8 0.00015376 1.92199E-05 1.145223322 0.337404081

Residual 134 0.002248882 1.67827E-05

Total 142 0.002402641

Coefficients Standard

Error t Stat P-value

Intercept 0.008096904 0.006016173 1.345856368 0.180621491

X Variable 1 -

0.000369668 0.000478247 -

0.772965274 0.440904263

X Variable 2 -

0.000216165 0.000423272 -

0.510699851 0.610401571

X Variable 3 -7.11742E-

07 2.38602E-05 -

0.029829667 0.976247281

X Variable 4 0 2.38098E-05 0 1

X Variable 5 -

0.001188953 0.001213268 -

0.979959067 0.328872274

X Variable 6 0 6.99936E-06 0 1

X Variable 7 0 4.38888E-08 0 1

X Variable 8 0 7.27808E-10 0 1

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WWhhiittee TTeesstt::

Regression Statistics

Multiple R 0.039348536

R Square 0.001548307

Adjusted R Square -0.00558349

Standard Error 0.04699267

Observations 142

ANOVA

df SS MS F Significance

F

Regression 1 0.000479422 0.000479422 0.21709916 0.641983254

Residual 140 0.309163549 0.002208311

Total 141 0.309642972

Coefficients Standard

Error t Stat P-value

Intercept -

0.000561449 0.003943545 -

0.142371653 0.88699114

X Variable 1 0.038964542 0.083625843 0.465939009 0.641983254

Wal-Mart Sales Econometric Analysis MSF 562

Page | 21

References:

1. Wooldridge J. M. , (2006), Introductory Econometrics –3rd

Edition

2. Baumohl B., (2007), The secrets of economic indicators –– 2nd

Edition

3. Morey E. (2003), Econ 6818: Econometric Methods and Applications. Retrieved:

February 15, 2008, from University of Colorado.

Website: http://www.colorado.edu/Economics/morey/6818/student/6818proj.html

4. Bureau of Economic Analysis. Retrieved: February 15th, 2008.

Website: http://www.bea.gov/

5. Federal Research Economic Data. Retrieved: January 30th, 2008.

Website: http://research.stlouisfed.org/fred2/