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Academy of Economic Studies
International Business and Economics
ECONOMETRICS ASSIGNMENT
“REGRESSION MODEL”
1. Executive Summary
In this chapter I would like to justify why of all indicators and from all data bases existing I specifically chose verifying the intensity of the relationship between a country’s total advertising expenditures and its GDP.
For once, it will help me with my Dissertation paper which is on advertising, domain I’ve recently came in touch with by working at a production company.
Second, the overall increase in the importance given to this sector, with the gigantic amounts spent in producing a TV commercial are raising a good question mark regarding how much does a country’s GDP and welfare influences these spending.
I chose UK because they put a lot of emphasis on their advertising campaigns, having some of the biggest and wellknown leading worldwide
commercial production companies, among which I can mention: Radical Media (with who I got the opportunity to work), HIS, Factory Films, Tangerine Films, Independent Media, Mind Works Media UK and also hosting some of the largest global marketers that have a strong word when it comes to their contribution to the World’s GDP.
All the calculations made to prove the validity of the model were made in Excel and are further attached as ANEXES in the end of the Assignment.
2. Introduction
Advertising is a small part of the daytoday life of business, governments and of the publics with which each seeks to engage. It is, on the other hand, a business that offers the people who work in and with it endless excitement, fascination, frustration and, sometimes, satisfaction – together with the opportunity, from time to time, for a great deal of fun and even for making a massive contribution to the success of a brand.
There is an underlying reason why ad expenditures as a whole has not returned to its 1989 peak share of GDP in the UK. Advertising is not the whole of the communications mix, and the most advertising statistics do not include direct marketing, let alone PR, sales promotion, design and corporate identity, sponsorship and some mirror media – nor do they yet include the Internet, which is undoubtedly the fastest growing form of marketing communication, though still from a very small base everywhere outside the USA. All the available evidence shows that direct mail has been growing faster than media advertising in recent years, and PR expenditures have certainly grown very fast in the last three years. Data on sales promotion expenditure are extremely hard to come by, but US evidence, and broader estimates by WPP Group, suggest that advertising accounts for only 42% of total marketing communications expenditures worldwide (including market research), and less than 35% in the UK. As long as 1986, WPP annual report highlighted the rapid growth of sales promotion expenditures, and this remains a worldwide phenomenon.
3. Data Description
As we know, Gross Domestic
Product (GDP) is an integral part of the UK national accounts and provides a
measure of the total economic activity in a region. GDP is often referred to as
one of the main 'summary indicators' of economic activity and references to
'growth in the economy' are quoting the growth in GDP during the latest
quarter.
In the UK three different
theoretical approaches are used in the estimation of one GDP estimate.
GDP from the output or production approach  GDP(O) measures the sum of the value added created through the production of goods and services within the economy (our production or output as an economy). This approach provides the first estimate of GDP and can be used to show how much different industries (for example, agriculture) contribute within the economy.
GDP from the income approach  GDP(I) measures the total income generated by the production of goods and services within the economy. The figures provided breakdown this income into, for example, income earned by companies (corporations), employees and the self employed.
GDP from
the expenditure approach  GDP(E)
measures the total expenditures on all finished goods and services produced
within the economy.
The estimates are 'Gross'
because the value of the capital assets actually worn away (the 'capital
consumption') during the productive process has not been subtracted.
Thus, by analyzing the values of GDP by expenditure it will prevail how much its variation explains the variation in the total advertising figures.
Advertising is an activity with significance for many countries’ economies: total ad spending runs around +/  1% of GDP in most developed countries.
In the UK, while ad agencies as such, employ only some 15 000 people, it has been estimated that advertising as a whole is responsible for nearly 100 000 jobs, or 0.4% of total employment. This includes people working in business supplying the ad industry – studios, TV production houses, printers, etc. – and advertising staff in client organizations and the media.
Advertising expenditures, as shown in the statistics published by theAdvertising Association, consist of two elements: display advertising and classified advertising, of which display is the dominant sector, though classified is very important for some media. Advertising is sensitive to the state of the economy as a whole – it is not merely vulnerable to both downturns and upswings, but it moves rather rapidly in response to either. Classified advertising, in fact, is a valuable “lead indicator” of economic progress, because virtually half of it, at least in the UK, consists of recruitment advertising, which reflects companies’ experience and expectations of their markets precisely.
Advertising thus shows considerable “mood swings” in line with the growth or stagnation of the economy. Through much of the 1970s, the industry in the UK was in decline: the 1980s saw a sustained boom after the 198182 recession, followed by hard times in the early 1990s, and by 1998, display advertising had still not recovered to its 1989 percentage share of GDP.
4. Analysis
4. a) Collecting the data
In order to determine at what extent does the wealth of a country determines the total expenditures in the advertising sector, we will analyze a model taking as independent variable (x),UK’s GDP for a 20 year period starting with 1985 and proving how it influences the dependent variable (y), represented by the advertising expenditures in current prices.
The data was collected from several sources so that the GDP values initially expressed in million dollars were converted at a parity of 0.509 pound/dollaras shown in the following table:
Parity: £/ $ =0.509
Figure 1. UK advertising expenditure and GDP 19852004
Nr. crt 
Year 
Advertising expenditure (yi) 
GDP $mil by expenditure

GDP £ bn by expenditure (xi) 
1 
1985 
5.05 
455506.9902 
894.9056783 
2 
1986 
5.8 
558954.1052 
1098.14166 
3 
1987 
6.57 
685753.9263 
1347.257223 
4 
1988 
7.61 
833174.7712 
1636.885602 
5 
1989 
8.64 
841280.9635 
1652.811323 
6 
1990 
8.93 
989564.2668 
1944.134119 
7 
1991 
8.53 
1033481.752 
2030.416015 
8 
1992 
8.86 
1071585.965 
2105.276944 
9 
1993 
9.14 
962406.7387 
1890.779447 
10 
1994 
10.14 
1041342.663 
2045.859849 
11 
1995 
11.03 
1133689.667 
2227.288147 
12 
1996 
12.08 
1191280.39 
2340.432986 
13 
1997 
13.34 
1327035.159 
2607.141767 
14 
1998 
14.42 
1464975.281 
2878.14397 
15 
1999 
15.41 
1442777.295 
2834.532996 
16 
2000 
16.99 
1434896.459 
2819.050018 
17 
2001 
16.54 
1571371.904 
3087.174665 
18 
2002 
16.73 
1805663.111 
3547.471732 
19 
2003 
17.23 
2132156.066 
4188.911721 
20 
2004 
18.47 
2198795.754 
4319.834487 
Source: www.unctad.org and A.A., Advertising Statistics Yearbook 1998, NTC Publications), http://www.ipa.co.uk/resource_centre/totaladspend.cfm
4.b) Graphical representation – Scatter diagram
Specifying the econometrical model implies choosing a function f(x) which can describe the relationship between the 2 variables.
The graphical representation of the data presented in Table 1 is made through a scatter diagram which shows that there is a positive relationship between UK’s GDP by expenditure and the advertising expenditures from 1985 till 2004, since the two variables tend to move in the same direction forming a linear pattern as follows:
Figure 2. The relation between UK’s GDP and Total advertising expenditures in 19852004
Source: Figure 1. UK advertising expenditure and GDP 19852004
Both the equation and the value of R^{2 }displayed on the diagram, we shall see, that are the same as the ones obtained after making all the computations and their values will be described when analyzing the regression model.
4.c) The Regression
After collecting the data and drawing the graph, the next step is to create the regression model using the Regression function in Excel, which automatically generated the Summary output:
Figure 3. Summary output
SUMMARY OUTPUT 



Regression Statistics 

Multiple R 
0.9487517 
R Square 
0.90012979 
Adjusted R Square 
0.89458144 
Standard Error 
1.38047841 
Observations 
20 
Where:
It is calculated as a ratio between covariance and the product of the
standard deviation of the two variables, as in the formula:
r = _{}
The value of 0.94 obtained in the table shows that there is a strong positive association between UK’s GDP by expenditure and the advertising expenditures, since it falls in the interval (0.95 , 0.75] and every point falls on a increasing regression line.
R^{2 }=_{}
The value obtained in the summary output reveals that there is only a slight difference of ~ 1,39 between the real values and the theoretical ones.
Accounts for the number of predictors in your model and is useful for comparing models with different numbers of predictors.
Next, it’s easy to determine the equation of regression Y=a+bx+_{}
based on the coefficients obtained in the ANOVA table.
Figure 4. ANOVA
ANOVA 






df 
SS 
MS 
F 
Significance F 
Regression 
1 
309.1725235 
309.172524 
162.233917 
1.9216E10 
Residual 
18 
34.30297146 
1.90572064 


Total 
19 
343.475495 










Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Intercept 
1.2506 
0.867400354 
1.44178415 
0.16653638 
0.5717364 
GDP 
4E06 
0.00000034 
12.7371079 
1.9216E10 
3.6305E06 
So the equation will be :
Y= 1.2506 + 0.000004*x
Where: a = 1.2506 ( intercept) ; it indicates the value of Y when the x=0;
b = 0.0000004 (the slope of the line)
The interpretation of the equation is that an increase of 1 pound in UK’s GDP by expenditures in one year (xi), will determine an increase of 0.000004 pounds in the total advertising expenditures of UK in that year.
The formulas for the other coefficients that appeared whilst creating the regression model are encountered below along with the afferent explanations.
DF Regression = p
DF Error = n  p  1
Total = n  1
where n = number of observations and p = number of predictors.
MS Error = SS Error / DF Error
Figure 5. Table with all computation needed to make the regression model
Year 
Advertising expenditure* (yi) 
GDP**(xi) 
Yi 
(Yiyavg)^2 
(yiYi) 
(yiYi)^2 
(xixavg)^2 
1985 
5.05 
894.9056783 
5.14 
41.40 
0.09 
0.0083 
2171703966 
1986 
5.8 
1098.14166 
6.02 
30.81 
0.22 
0.0506 
2152803049 
1987 
6.57 
1347.257223 
7.11 
19.96 
0.54 
0.2895 
2129748026 
1988 
7.61 
1636.885602 
8.37 
10.29 
0.76 
0.5734 
2103099679 
1989 
8.64 
1652.811323 
8.44 
9.85 
0.20 
0.0414 
2101639239 
1990 
8.93 
1944.134119 
9.70 
3.51 
0.77 
0.5976 
2075013514 
1991 
8.53 
2030.416015 
10.08 
2.24 
1.55 
2.3967 
2067160278 
1992 
8.86 
2105.276944 
10.40 
1.37 
1.54 
2.3827 
2060358623 
1993 
9.14 
1890.779447 
9.47 
4.43 
0.33 
0.1096 
2079877219 
1994 
10.14 
2045.859849 
10.15 
2.05 
0.01 
0.0000 
2065756177 
1995 
11.03 
2227.288147 
10.93 
0.41 
0.10 
0.0092 
2049297047 
1996 
12.08 
2340.432986 
11.43 
0.02 
0.65 
0.4277 
2039065904 
1997 
13.34 
2607.141767 
12.59 
1.02 
0.75 
0.5692 
2015050025 
1998 
14.42 
2878.14397 
13.76 
4.79 
0.66 
0.4306 
1990793264 
1999 
15.41 
2834.532996 
13.57 
3.99 
1.84 
3.3703 
1994686862 
2000 
16.99 
2819.050018 
13.51 
3.73 
3.48 
12.1324 
1996070100 
2001 
16.54 
3087.174665 
14.67 
9.59 
1.87 
3.4873 
1972183767 
2002 
16.73 
3547.471732 
16.67 
25.99 
0.06 
0.0032 
1931512722 
2003 
17.23 
4188.911721 
19.46 
62.21 
2.23 
4.9842 
1875542902 
2004 
18.47 
4319.834487 
20.03 
71.51 
1.56 
2.4390 
1864220157 

231.51 
47496.45035 
231.51 
309.17 
0.00 
34.3030 
40735582521 
* Advertising expenditures (bn pounds in current prices)
**GDP in total expenditures (bn pounds)
4.d) Testing the Regression model
There are several methods used to test the accuracy of the model, among which the simplest one is to look at the P value in the ANOVA table (Figure 4) which is 1.9216*10^{10 }, obviously smaller than 0.05 degrees of freedom meaning that the model is correct.
As well, by comparing F value with F table (from the statistical tables), it can be proven the correctness of the model since F value > F table, with the values of 162.233917 > 4.4138734.
4.e) Testing the linear relationship between the two variables
In order to verify if between UK’s GDP by expenditure and the total advertising expenditures really is a linear relation, comparing t Stat value and the t table (from the statistical tables) and, as we see, 12.7371079 >2.10092204, therefore between the 2 variables is a linear relationship.
The regression model analyzed is a good proof that a country’s GDP is a top ranked indicator that influences all parts of the economy, true, in different extent but still plays a huge role in the development of some certain areas, like in the given example.
It’sbeen demonstrated that by choosing UK’s GDP as independent variable and the country’s advertising expenditures as dependent one, a positive linear relationship is established, and we have the formula to sustain that evidence:
ANEXES
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