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Academy of Economic Studies
International Business and Economics


ECONOMETRICS ASSIGNMENT
“REGRESSION MODEL”
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 the Advertising 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/dollar as 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) 
Source: www.unctad.org and A.A., Advertising Statistics Yearbook 1998, NTC Publications), https://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  
R Square  
Adjusted R Square  
Standard Error  
Observations 
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 

1.9216E10 

Residual  
Total  
Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 

Intercept  
GDP 
4E06 
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
SRegression = _{}
SSResidual = _{}
MSRegression =_{}, where 1 = degrees of freedom
MSResidual =_{}, where 23 = degrees of freedom
Standard error_{(b)} = _{}
t Stat = _{}
Year 
Advertising expenditure* (yi) 
GDP**(xi) 
Yi 
(Yiyavg)^2 
(yiYi) 
(yiYi)^2 
(xixavg)^2 
* 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’s been 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:
This will be useful for future predictions of UK’s advertising expenditures, in a faster way now just by looking at the country’s GDP in the year needed.
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