contagion effect of greece debt crisis

Economics 2012

Contagion Effect of Greek Debt Crisis

Contagion effect of Greek debt crisis

Economics 2012

Table of Contents

1.Introduction..................................................................................................................................................7

1.1 Greek Debt Crisis............................................................................................................................................7

1.2 Trade in Euro Region......................................................................................................................................7

1.3 Previous Observations....................................................................................................................................7

2. Methodology................................................................................................................................................9

2.1 Research Framework.....................................................................................................................................9

2.2 Type of Research..........................................................................................................................................10

2.3 Primary scales used in SPSS analysis.............................................................................................................10

2.4 Analysis tool used.........................................................................................................................................10

2.4.1.VAR Analysis..........................................................................................................................................10

2.4.2 Linear Regression Analysis.....................................................................................................................11

Names for X and Y..........................................................................................................................................12

2.4.3.Correlation Analysis...............................................................................................................................14

2.4.4.Descriptive statistics..............................................................................................................................15

2.4.5.Graphical Analysis..................................................................................................................................16

2.5 Software package/ tools used......................................................................................................................16

3. Sources of Data..........................................................................................................................................17

3.1 Secondary data:............................................................................................................................................17

3.2 Nature of Sampling......................................................................................................................................17

Probability Sampling:......................................................................................................................................17

3.3 Sampling Type..............................................................................................................................................17

Fixed Sampling:...............................................................................................................................................17

3.4 Sample Size...................................................................................................................................................18

3.5 Target Sample...............................................................................................................................................18

3.6 Data Collection Methods..............................................................................................................................18

Stage:1............................................................................................................................................................18

Stage:2............................................................................................................................................................18

Stage:3............................................................................................................................................................18

3.7 Data Collected and Sources..........................................................................................................................18


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Table of Contents

4. Analysis and Results...................................................................................................................................19

4.1 Focus of analysis...........................................................................................................................................19

4.2 Analysis of Data set-1...................................................................................................................................19

4.2.1 Multiple Regression Analysis.................................................................................................................20

4.2.2 Results of regression analysis...............................................................................................................24

4.2.3 Correlation Analysis...................................................................................................................................25

4.2.4 Results of correlation analysis...............................................................................................................27

4.2.5 Descriptive Statistical analysis...................................................................................................................28

4.2.6 Results of Descriptive Statistics.............................................................................................................29

4.2.7 VAR ( Vector autoregressive regression Analysis ).................................................................................29

4.3 Government Debt to GDP.............................................................................................................................31

4.4 Analysis of Data set- II..................................................................................................................................31

4.4.1 Multiple Regression Analysis.................................................................................................................32

Variables Entered/Removed(b)......................................................................................................................32

4.4.2 Results of regression analysis................................................................................................................35

4.4.3 Correlation Analysis...............................................................................................................................36

4.4.4 Results of correlation analysis..............................................................................................................36

4.4.5 Descriptive Statistics for Debt to GDP....................................................................................................37

4.4.6 Results of Descriptive Statistics.............................................................................................................37

4.4.7 Country wise Correlation Coefficient trend...........................................................................................39

5. Key Observations........................................................................................................................................40

5.1 Observations and Findings............................................................................................................................40

6. Conclusion..................................................................................................................................................41

7. Appendix - 1...............................................................................................................................................42

8. Appendix - 2...............................................................................................................................................43

9.Glossary......................................................................................................................................................44

10. References................................................................................................................................................47


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List of Tables

Table No Title Page No

Table 2.1 Regression X-Y table 11

Table 2.2 Software package/tools used 15

Table 4.1 General government net debt in Billions (1996-2004) 18

Table 4.2 General government net debt in Billions (2005-2012) 19

Table 4.3 Spss Output : Variable Entered 20

Table 4.4 Spss Output : Model Summary 20

Table 4.5 Spss Output: Annova 21

Table 4.6 Spss Output: Coefficients 21

Table 4.7 Spss Output: Correlation matrix 24,25

Table 4.8 Spss Output: Descriptive Statistics 27

Table 4.9 Forecasted Govt net Debt value ( 2012 - 2017 ) 29

Table 4.10 Government Debt to GDP 29

Table 4.11 Spss Output : Variable Entered 30

Table 4.12 Spss Output: Annova 30

Table 4.13 Spss Output: Coefficients 31

Table 4.14 Spss Output: Excluded variables 31

Table 4.15 Excel Spreadsheet Output : Correlation Matrix 34

Table 4.16 Spss Output: Descriptive Statistics 35


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List of Figures/Charts

Chart No Title Page No

Figure 2.1 Research Frame work 8

Chart 1.1 Euro Area Sovereign Bond Yield 7

Chart 2.1 VAR Analysis example 10

Chart 2.2 Regression Analysis Example 12

Chart 2.3 Correlation Analysis Example 13

Chart 4.1 Partial Regression Plot : Greece- Belgium 22

Chart 4.2 Partial Regression Plot : Greece- Italy 22

Chart 4.3 Partial Regression Plot : Greece- Netherland 22

Chart 4.4 Partial Regression Plot : Greece- Portugal 22

Chart 4.5 Partial Regression Plot : Greece- Spain 22

Chart 4.6 Partial Regression Plot : Greece- Finland 22

Chart 4.7 VAR Analysis 28

Chart 4.8 Partial Regression Plot : Greece- Germany 32

Chart 4.9 Partial Regression Plot : Greece- Netherland 32

Chart 4.10 Partial Regression Plot : Greece- Portugal 32

Chart 4.11 Partial Regression Plot : Greece-Spain 32

Chart 4.12 VAR Analysis 36

Chart 4.13 Country wise correlation coefficients graph 37

1.Introduction

1.1 Greek Debt Crisis


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Euro debt crisis is currently one of the major financial crisis that has affected entire globe. Euro debt crisis initially started in Greece when government borrowings went up with the inability to repay the debt amount leading to severe debt crisis in Greece. As of now Greece is having the highest debt to GDP ratio in Europe and second in globe. But gradually within a short span Greece debt crisis have passed away its financial shockwaves to its neighboring countries that have led to economic slowdown in entire Eurozone. This crisis had impacted globally.This study reveals that other euro countries are also affected by Greek debt crisis and hence it is contagion.

1.2 Trade in Euro Region

The nature of trade in Euro countries makes it more vulnerable to global crisis when one/few countries undergo financial instability. The fiscal, monetary and forex exchange policy are standardized over entire euro area and regulated uniquely so that the whole Eurozone is benefited out of the trade but when the conditions are adverse, again the same Eurozone is deeply affected. The complex chain of commercial banks and government lending’s influences one country to lend other and again borrow it back from them is a cyclic process leading to cob web network of government transaction in debt and government bonds. When one country would undergo a crisis , than this cycle breaks and due to the robust network of such transactions, several banks gets affected further affecting the financial stability of central government.

1.3 Previous Observations

A financial research done concluded that that Greece debt crisis have infected other countries by negatively influencing the market. The research suggested that 4 out 6 countries observed, Portugal, Spain, Italy and Belgium were contagion to Greece economy. Also it mentioned that countries like Portugal and Spain were affected by lower credit ratings made by credit rating agencies. However this research is being concluded through data available up to 2010.

Chart 1.1


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In case of our research we too had similar conclusion, however we have included 10 countries in our sample for close observation with data available till 2012. Also future scenarios have been forecasted using standard statistical tools.

References : Sabastian Misso,Sabastian Watzka, (Aug 2011), “Financial Contagion and European Debt Crisis", Ludwig Maximilian - University of Munich, pp.2-4


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2. Methodology

2.1 Research Framework Figure:2.1


Problem Definition

Research Objectives

Research Design

Source of Data

Data Collection

Data Analysis

(Primary)

Report

Contagion effect of Greek Debt Crisis

1. To identify whether Euro Economy is contagion to Greek Debt crisis or not

2. Which countries in Euro Zone are most likely to be effected by Greek debt crisis?

3. To design a cause effect relationship/X-Y/ statistical equation to prove relationship between Greek Debt Crisis and Debt crisis in Euro countries.

Secondary Data from Journals & websites

Online Journals and review of literature Government & IMF Financial data

1. VAR Analysis2. Linear Regression Analysis3. Correlation Analysis4. Descriptive statistics5. Graphical Analysis

Causative Research: How Greek Debt crisis will affect Economy of other countries.

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2.2 Type of Research

Causal Research: It is done to establish a cause and effect relationship between Greek Debt crisis and its impact on debt crisis of other economy.

2.3 Primary scales used in SPSS analysis

1. Nominal Scale : This serves only as labels or tags for identifying and classifying objects. In this research, name of countries are part of nominal scale.

2. Ratio Scale : Numerically equal distances on the scale represent equal values in the characteristic being measured. In this research, Government net GDP and Government

Debt to GDP ratio is being taken in ratio scale.

2.4 Analysis tool used

2.4.1.VAR Analysis

Vector auto regression (VAR) is a statistical model used to capture the linear interdependencies among multiple time series. VAR models generalizes the univariate auto regression (AR) models. All the variables in a VAR are treated symmetrically; each variable has an equation explaining its evolution based on its own lags and the lags of all the other variables in the model.

A VAR model describes the evolution of a set of k variables (called endogenous variables) over the same sample period (t = 1, ..., T) as a linear function of only their past evolution. The variables are collected in a k × 1 vector yt, which has as the ith element yi,t the time t observation of variable yi. For example, if the ith variable is GDP, then yi,t is the value of GDP at t.

A (reduced) p-th order VAR, denoted VAR(p), is

2.4.1.1 Multivariate Time Series Data

Often, the first step in creating a multiple time series model is to obtain data. There are two types of multiple time series data:

Response data. Response data corresponds to yt in the multiple time series models defined in Types of VAR Models.


http://www.mathworks.in/help/toolbox/econ/bswxr8u-1.html#bswxr8u-2

http://en.wikipedia.org/wiki/Lag

http://en.wikipedia.org/wiki/AR_model

http://en.wikipedia.org/wiki/Time_series

http://en.wikipedia.org/wiki/Linear_dependence


http://en.wikipedia.org/wiki/Statistical_model

http://en.wikipedia.org/wiki/Autoregression

http://en.wikipedia.org/wiki/Vector

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Exogenous data. Exogenous data corresponds to Xt in the multiple time series models defined in Types of VAR Models.

Chart 2.1

2.4.1.2 VAR Forecasting

When models with parameters are known or can be estimated), it possible to examine the predictions of the models.

The main methods of forecasting are:

Generating forecasts with error bounds Generating simulations Generating sample paths

These functions base their forecasts on a model specification and initial data. The functions differ in their innovations processes:

The error bounds given by transforms ofvgxpred error bounds are not valid bounds. In contrast, the error bounds given by the statistics of transformed simulations are valid.Forecasting with vgxpred. vgxpred enables to generate forecasts with error estimates. vgxpred requires:

2.4.2 Linear Regression Analysis

In statistics, regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.


http://www.mathworks.in/help/toolbox/econ/bswxr8u-1.html#bswxr8u-2

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Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution.

Regression is a generic term for all methods attempting to fit a model to observed data in order to quantify the relationship between two groups of variables. The fitted model may then be used either to merely describe the relationship between the two groups of variables, or to predict new values.

The two data matrices involved in regression are usually denoted X and Y, and the purpose of regression is to build a model Y = f(X). Such a model tries to explain, or predict, the variations in the Y-variable(s) from the variations in the X-variable(s). The link between X and Y is achieved through a common set of samples for which both X- and Y-values have been collected.

Names for X and YThe X- and Y-variables can be denoted with a variety of terms, according to the particular context (or culture). The most common ones are listed in the table below:Usual names for X- and Y-variables.

Table 2.1

Context X Y

General Predictors Responses

Multiple Linear Regression

(MLR)

Independent

Variables

Dependent Variables

Designed DataFactors, Design

VariablesResponses

Spectroscopy Spectra Constituents

Dependent variable : Gr , Debt crisis at Greece


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Independent variable : F(X), Factors influenced by Greece debt crisis

Y = F(X) + C

Where is C is constant value

Chart 2.2

Once a regression model has been constructed, it may be important to confirm the goodness of fit of the model and the statistical significance of the estimated parameters. Commonly used checks of goodness of fit include the R-squared, analyses of the pattern of residuals and hypothesis testing. Statistical significance can be checked by an F-test of the overall fit, followed by t-tests of individual parameters.

Interpretations of these diagnostic tests rest heavily on the model assumptions. Although examination of the residuals can be used to invalidate a model, the results of a t-test or F-test are sometimes more difficult to interpret if the model's assumptions are violated. With relatively large samples, however, a central limit theorem can be invoked such that hypothesis testing may proceed using asymptotic approximations.

2.4.3.Correlation Analysis


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A correlation function is the correlation between random variables at two different points in

space or time, usually as a function of the spatial or temporal distance between the points. The

main result of a correlation is called the correlation coefficient (or "r"). It ranges from -1.0 to

+1.0. The closer r is to +1 or -1, the more closely the two variables are related. If r is close to 0,

it means there is no relationship between the variables. If r is positive, it means that as one

variable gets larger the other gets larger. If r is negative it means that as one gets larger, the

other gets smaller (often called an "inverse" correlation).

While correlation coefficients are normally reported as r = (a value between -1 and +1),

squaring them makes then easier to understand. The square of the coefficient (or r square) is

equal to the percent of the variation in one variable that is related to the variation in the other.

After squaring r, ignore the decimal point. An r of .5 means 25% of the variation is related (.5

squared =.25). An r value of .7 means 49% of the variance is related (.7 squared = .49).

A correlation report can also show a second result of each test - statistical significance.

In this case, the significance level will tell you how likely it is that the correlations reported may

be due to chance in the form of random sampling error. If you are working with small sample

sizes, choose a report format that includes the significance level. This format also reports the

sample size.

The Pearson correlation technique works best with linear relationships: as one variable

gets larger, the other gets larger (or smaller) in direct proportion. It does not work well with

curvilinear relationships (in which the relationship does not follow a straight line). They are

related, but the relationship doesn't follow a straight line.

Chart 2.3

2.4.4. Descriptive statistics


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Descriptive statistics, quantitatively describes the main features of a collection of data like central tendencies, Mean, Median, Mode and deviations like standard deviation, variance, standard error. Descriptive statistics is the discipline of quantitatively describing the main features of a collection of data.

2.4.4.1 Univariate analysis

Univariate analysis involves describing the distribution of a single variable, including its central tendency (including the mean, median, and mode) and dispersion (including the range and quantiles of the data-set, and measures of spread such as the variance and standard deviation). The shape of the distribution may also be described via indices such as skewness and kurtosis. Characteristics of a variable's distribution may also be depicted in graphical or tabular format, including histograms and stem-and-leaf plots.

Mean

The most common expression for the mean of a statistical distribution with a discrete random variable is the mathematical average of all the terms. To calculate it, add up the values of all the terms and then divide by the number of terms.

This expression is also called the arithmetic mean. There are other expressions for the mean of a finite set of terms but these forms are rarely used in statistics. The mean of a statistical distribution with a continuous random variable, also called the expected value, is obtained by integrating the product of the variable with its probability as defined by the distribution.

Median

The median of a distribution with a discrete random variable depends on whether the number of terms in the distribution is even or odd. If the number of terms is odd, then the median is the value of the term in the middle. This is the value such that the number of terms having values greater than or equal to it is the same as the number of terms having values less than or equal to it. If the number of terms is even, then the median is the average of the two terms in the middle, such that the number of terms having values greater than or equal to it is the same as the number of terms having values less than or equal to it. The median of a distribution with a continuous random variable is the value m such that the probability is at least 1/2 (50%) that a randomly chosen point on the function will be less than or equal to m, and the probability is at least 1/2 that a randomly chosen point on the function will be greater than or equal to m.

Mode


http://en.wikipedia.org/w/index.php?title=Stem-and-leaf_plots&action=edit&redlink=1

http://en.wikipedia.org/wiki/Histograms

http://en.wikipedia.org/wiki/Kurtosis

http://en.wikipedia.org/wiki/Skewness

http://en.wikipedia.org/wiki/Standard_deviation

http://en.wikipedia.org/wiki/Variance

http://en.wikipedia.org/wiki/Quantiles

http://en.wikipedia.org/wiki/Range

http://en.wikipedia.org/wiki/Mode

http://en.wikipedia.org/wiki/Median

http://en.wikipedia.org/wiki/Mean

http://en.wikipedia.org/wiki/Distribution

http://en.wikipedia.org/wiki/Univariate_analysis

http://en.wikipedia.org/wiki/Data

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The mode of a distribution with a discrete random variable is the value of the term that occurs the most often. It is not uncommon for a distribution with a discrete random variable to have more than one mode, especially if there are not many terms. This happens when two or more terms occur with equal frequency, and more often than any of the others. A distribution with two modes is called bimodal. A distribution with three modes is called trimodal. The mode of a distribution with a continuous random variable is the maximum value of the function. As with discrete distributions, there may be more than one mode.

Range

The range of a distribution with a discrete random variable is the difference between the maximum value and the minimum value.

For a distribution with a continuous random variable, the range is the difference between the two extreme points on the distribution curve, where the value of the function falls to zero. For any value outside the range of a distribution, the value of the function is equal to 0

2.4.5.Graphical AnalysisIt gives graphical chart or plot of summary data in form of bar chart, pie chart, scatter diagram, linear curve chart .

2.5 Software package/ tools used

Table: 2.2

SOFTWARE TOOL ANALYSIS TOOL

IBM SPSS STATISTICS V.14Correlation, Regression, Descriptive statistics, Central

Tendencies, Plotting charts, Plotting Graphs

Microsoft Excel V.2010VAR analysis, Correlation, Forecasting, Percentage calculation,

Ratios, Graphical Analysis

3. Sources of Data

3.1 Secondary data:


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Secondary research such as website search was done for to get an idea about debt crisis at various Euro countries including Greece

Online journals, review literatures were used to know the opinions and results of other research done in similar field or very close to it.

Data were collected from government bodies recognized in the same industry.

3.2 Nature of Sampling

Probability Sampling: Nature of sampling used in this research is probability sampling in which each

population element has a known and equal chance of being included in the sample. Any probability ratio can be calculated keeping this population e in denominator

3.3 Sampling Type

Fixed Sampling: This is a type of sampling in which samples are chosen pre decided from the entire

pool of population.

Each possible sample of a given size (n) has a known and equal probability of being the sample actually selected.

3.4 Sample Size

The analysis has a sample size of 10, where the samples are taken from10 selective countries from Euro Area that have high chances of being contagion to Greek Debt crisis.

Sample Size, N = 10

3.5 Target Sample

The countries that are selected for observation are expected to be contagion to Greek debt crisis concluded after undergoing a background research work through review of literature.

1. Austria, 2. Belgium, 3.Finland, 4.France, 5.Germany,

6. Greece, 7. Italy, 8. Portugal, 9.Netherland, 10. Spain


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3.6 Data Collection Methods

Stage:1 To undergo contagion effect on euro zone it was important to collect data suggested by proven studies. Online Journals were used to make a note of those selected countries, that were proven to have a contagion relationship with debt crisis at Greece.

Stage:2 Once those countries were identified, relevant data on annual government net debt crisis of each of those countries were gathered from reliable secondary resources as mentioned below. Since government net debt were not sufficient and Debt to GDP ratio was an important concern. The same were collected from reliable resources.

Stage:3 To cross check the authenticity of data, the collected data were randomly tested to see if it matched with the data provided by other sources.

3.7 Data Collected and Sources

Data Set-1

11 years data on “Government Net Debt” were collected for each of the targeted country from the year 2000-2011.

The data were collected from International Monetary Fund ( IMF) official website. Further, IMF approves and quotes the name of the government/regulatory body for a

respective country from where the data has been referred to produce the above data.

Data Set-2

5 years data on “Government Debt to GDP” were collected for each of the targeted country from the year 2007-2011.

The data were collected from TradingEconomics official website which is globally recognized for providing reliable statistics all around the world with latest available data

TradingEconomics collects data from authorized government institutions, annually/quarterly declared fiscal results and central banks to collect the data.


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4. Analysis and Results

4.1 Focus of analysisOur prime focus of analysis will be to identify the contagion effect of Greek debt crisis in euro countries like Austria, Belgium, Finland, France, Germany, Italy, Netherland, Portugal and Spain by establishing a relationship among them.

4.2 Analysis of Data set-1

Table : 4.1

General government net debt in Billions( Unit in respective National Currency) 1996-2004

Country1996 1997 1998 1999 2000 2001 2002 2003 2004

Austria 90.014 84.585 85.735 90.37 90.01 93.833 95.65 96.863105.93

2

Belgium245.79

8247.61

6247.55

1245.8

41246.01

4246.50

9250.16

1248.935

243.536

Finland-

39.571-

47.809

-101.22

4

-61.49

9

-41.102

-44.085

-44.987

-55.947-

71.068

France 621.1 655.7 689.8 710.9 740.4 767.4 819.6 901.1 971.2

Germany772.48

4817.02

3849.39

3876.9

63841.97

4890.10

6955.4

1,042.85

1,115.94

Greece 65.504 73.791 83.12693.64

2105.48

8118.83

2133.86

5167.724

183.123

Italy1,076.

201,090.

201,104.3

81,097.

761,115.

681,161.

111,162.

591,186.6

21,230.

58Netherland

s124.03

6122.09

120.068

101.582

103.909

108.794

116.842

128.219137.96

3

Portugal 54.259 47.446 47.29749.72

253.31 62.221 67.434 73.25 79.299

Spain284.65

4301.68

4309.53

316.188

317.352

324.063

321.135

323.935324.91

4

Sources: IMF data, April 2012


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Table : 4.2

General government net debt in Billions ( Unit in respective National Currency) 2005-2011

Country 2005 2006 2007 2008 2009 2010 2011

Austria 108.86 111.549 111.982 118.802 135.732 148.916 158.065Belgium 248.705 245.996 245.541 254.249 271.384 284.385 307.108

Finland -92.24 -115.08-

130.415-96.841

-108.271

-116.242

-114.705

France 1,043.601,072.6

01,123.60 1,203.80

1,360.00

1,478.60 1,604.90

Germany 1,189.511,227.1

01,223.27 1,236.82

1,345.10

1,406.90 1,441.26

Greece 195.387 224.204 239.364 262.318 298.706 328.588 355.78

Italy 1,276.981,333.7

21,350.48 1,398.43

1,476.14

1,538.26 1,573.30

Netherlands

133.951 132.164 123.687 122.566 131.786 161.858 192.033

Portugal 89.092 94.194 107.785 115.85 132.833 153.973 172.33Spain 316.888 302.108 281.191 335.048 445.333 522.401 611.265

Sources: IMF data, April 2012

4.2.1 Multiple Regression Analysis

Regression analysis includes techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.


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4.2.1.1 Independent Variable A: Government Net Debt of Austria B: Government Net Debt of Belgium Fi: Government Net Debt of Finland Fr : Government Net Debt of France Ge : Government Net Debt of Germany I: Government Net Debt of Italy N : Government Net Debt of Netherland P : Government Net Debt of Portugal S : Government Net Debt of Spain

4.2.1.2 Dependent variable

Gr: Government Net Debt of Austria

Sample : 2005 - 2011 data provided by IMF

4.2.1.3 SPSS OUTPUT

Table:4.3

Variables Entered/Removed(b)

Model

Variables Entered

Variables

Removed Method

1 Finland, Spain,

Netherland, Portugal,

Italy, Belgium(a)

. Enter

a Tolerance = .000 limits reached.b Dependent Variable: Greece

Table:4.4


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Model Summary(b)

Model R R Square

Adjusted R Square

Std. Error of the

Estimate Change StatisticsR Square Change F Change df1 df2

Sig. F Change

1 1.000(a) 1.000 . . 1.000 . 6 0 .a Predictors: (Constant), Finland, Spain, Netherland, Portugal, Italy, Belgiumb Dependent Variable: Greece

Table:4.5

ANOVA(b)

Model

Sum of Squares df

Mean Square F Sig.

1 Regression 20247.342

6 3374.557 . .(a)

Residual .000 0 .Total 20247.3

426

a Predictors: (Constant), Finland, Spain, Netherland, Portugal, Italy, Belgiumb Dependent Variable: Greece

Table:4.6

Coefficients(a)

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

95% Confidence Interval for B

BStd.

Error BetaLower Bound

Upper Bound

1 (Constant)

-714.616 .000 . . -714.616 -714.616

Belgium 1.450 .000 .586 . . 1.450 1.450Italy .493 .000 .942 . . .493 .493

Netherland

-.077 .000 -.034 . . -.077 -.077

Portugal .100 .000 .053 . . .100 .100Spain -.253 .000 -.553 . . -.253 -.253


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Finland -.017 .000 -.004 . . -.017 -.017Austria .075 .000 -.037 . . -.075 -.075France .396 .000 .047 . . .396 .396

Germany .402 .000 -.554 . . -.402 -.402a Dependent Variable: Greece

Chart 4.1 Chart 4.2

Chart 4.3 Chart 4.4

Chart 4.5 Chart 4.6


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4.2.1.4 Statistical Interpretation :

R square value: R Square value = 1.0.which shows that the relationship is 100% accurate to define the existing relationship between Debt Crisis at Greece (Ge) and debt crisis in other countries like Austria(A), Belgium(B), Germany(Gr), France(Fr), Finland(Fi), Italy(I), Netherland(N), Portugal(P) and Spain(S).T-test: The independent variable’s t-value is blank in Spss output since R square value =1, which shows that the Greece debt crisis have a greater impact on other euro countries. F-test Significance Level: All significant values are blank showing close to 0, which means p = 0 < 0.05, hence all the independent variables assumed are significant enough to support these analysis.

B value in output: Slopes of Fi,N,S are negatively related whereas slopes A,B,Ge,Fr,I,P are Positively related. Constant is negatively related

Multiple Regression linear Equations

Gr = C + F(X)

C = - 714.616

F(X) = 0.075 A + 1.45 B - 0.017 Fi + 0.396 Fr + 0.402 Ge + 0.493 I - 0.077 N + P - 0.253 S

Gr = 0.075 A + 1.45 B - 0.017 Fi + 0.396 Fr + 0.402 Ge + 0.493 I - 0.077 N + P - 0.253 S - 714.616

4.2.2 Results of regression analysis


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The debt crisis in Greece will have a negative impact on debt crisis of Netherland, Spain and Finland which may turn the debt value drawn more towards the negative value. Gr α 1/N,1/S,1/Fi

Higher the level of debt crisis occurs in Greece, it will be contagious to euro countries like Belgium, Austria , Italy, Germany France and Portugal and will impact there economy. Gr α B,A,I,Ge,Fr,P

Debt crisis in Greece will have highest impact on Belgium ( around 1.5 times ), where the adverse affect of shockwaves can generate even higher percentage of debt crisis in Belgium than Greece itself. Some other countries which would get badly affected are Italy , followed by Germany, France and Spain.

However Netherland, Finland and Austria will have very little impact caused due to Greek debt crisis and will remain financial unaffected by such crisis.

4.2.3 Correlation Analysis

A correlation function is the correlation between random variables at two different points in space or time, usually as a function of the spatial or temporal distance between the points. Correlation functions are a useful indicator of dependencies as a function of distance in time or space, and they can be used to assess the distance required between sample points for the values to be effectively uncorrelated. In addition, they can form the basis of rules for interpolating values at points for which there are observations.

For random variables X(s) and X(t) at different points s and t of some space, the correlation function is

4.2.3.1 Correlation Variable

A: Government Net Debt of Austria , B: Government Net Debt of Belgium, Fi: Government Net Debt of Finland, Fr : Government Net Debt of France , Ge : Government Net Debt of Germany, I: Government Net Debt of Italy, N : Government Net Debt of Netherland, P : Government Net Debt of Portugal, S : Government Net Debt of Spain, Gr: Government Net Debt of Austria

Sample : 2005- 2011 data provided by IMF

4.2.3.2 Correlation Matrix XPSS OUTPUT

Table:4.7


Economics 2012

Correlations Matrix

Greece

Austria

Belgium

France

Germany Italy

Netherland

Portugal

Spain

Finland

Pearson Correlation

Greece1.000 .976 .940 .989 .974

.998

.781 .991 .937 -.285

Austria.976 1.000 .982 .995 .994

.980

.866 .985 .988 -.202

Belgium.940 .982 1.000 .977 .965

.938

.924 .966 .995 -.135

France.989 .995 .977 1.000 .986

.989

.842 .996 .975 -.217

Germany

.974 .994 .965 .986 1.000.98

1.847 .975 .975 -.274

Italy.998 .980 .938 .989 .981

1.000

.777 .986 .941 -.271

Netherland

.781 .866 .924 .842 .847.77

71.000 .838 .910 -.178

Portugal.991 .985 .966 .996 .975

.986

.838 1.000 .958 -.270

Spain.937 .988 .995 .975 .975

.941

.910 .9581.00

0-.110

Finland-.285 -.202 -.135 -.217 -.274

-.271

-.178 -.270-.11

01.000

Sig. (1-tailed)

Greece. .000 .001 .000 .000

.000

.019 .000 .001 .267

Austria.000 . .000 .000 .000

.000

.006 .000 .000 .332

Belgium.001 .000 . .000 .000

.001

.001 .000 .000 .387

France.000 .000 .000 . .000

.000

.009 .000 .000 .320

Germany

.000 .000 .000 .000 ..00

0.008 .000 .000 .276

Italy .000 .000 .001 .000 .000 . .020 .000 .001 .278Netherland

.019 .006 .001 .009 .008.02

0. .009 .002 .351

Portugal.000 .000 .000 .000 .000

.000

.009 . .000 .279

Spain.001 .000 .000 .000 .000

.001

.002 .000 . .407

Finland.267 .332 .387 .320 .276

.278

.351 .279 .407 .

N Greece 7 7 7 7 7 7 7 7 7 7


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Austria 7 7 7 7 7 7 7 7 7 7Belgium 7 7 7 7 7 7 7 7 7 7France 7 7 7 7 7 7 7 7 7 7Germany

7 7 7 7 7 7 7 7 7 7

Italy 7 7 7 7 7 7 7 7 7 7Netherland

7 7 7 7 7 7 7 7 7 7

Portugal 7 7 7 7 7 7 7 7 7 7Spain 7 7 7 7 7 7 7 7 7 7Finland 7 7 7 7 7 7 7 7 7 7

4.2.3.4 Interpretation of correlation analysis.

All euro countries are strongly correlated with correlation coefficient value R > 0.75 and significance value T < 0.05

4.2.4 Results of correlation analysis

The debt crisis in Greece were strongly correlated with Italy, followed by Portugal and France showing that Greece debt crisis is much more contagious to these countries. However Austria, Belgium, Germany and Spain too have a significant impact caused by Greece debt crisis.

Netherland is partially correlated, whereas statistically Finland is not having a very strong impact caused by Greece as it is weekly correlated.

Finland is the only country in the sample that it least affected by euro crisis. Adverse effect of euro countries on Finland can be tolerated to a large extent, thus avoiding chances of financial instability.

Greece debt crisis is a growing concern for all the Eurozone countries where chances of countries like Austria, Belgium, France, Germany, Italy, Portugal and Spain is more than 90% to be affected by economic slowdown caused due to debt crisis in Greece.

However Finland and Netherland seems to be much more stable and can tolerate a turmoil caused due to deficit or government borrowing by Greece and other euro countries.

4.2.5 Descriptive Statistical analysis

Descriptive statistics quantitatively describe the main features of a collection of data. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that


Economics 2012

descriptive statistics aim to summarize a data set, rather than use the data to learn about the population that the data are thought to represent.

Sample : 2005- 2011 data provided by IMF

4.2.5.1 SPSS Output

Table 4.8Descriptive Statistics

N RangeMinimu

mMaxim

um Sum Mean

Std. Deviatio

nVarianc

eStatisti

c Statistic StatisticStatisti

c Statistic StatisticStd. Error Statistic Statistic

Greece7 160.39 195.39 355.78 1904.35

272.0496

21.95631

58.09094

3374.557

Austria7 49.21 108.86 158.07 893.91

127.7009

7.52566

19.91103

396.449

Belgium7 61.57 245.54 307.11 1857.37

265.3383

8.87711

23.48663

551.622

Finland7 38.18 -130.42 -92.24 -773.79

-110.542

0

4.86285

12.86590

165.531

France7 561.30 1043.60

1604.90

8887.101269.58

5781.62

193215.951

3446634.

981Germany

7 251.75 1189.511441.2

69069.96

1295.7086

38.01276

100.57232

10114.791

Italy7 296.32 1276.98

1573.30

9947.311421.04

4341.92

614110.926

1312304.

606Netherland

7 69.47 122.57 192.03 998.05142.577

99.604

3325.4106

6645.70

2Portugal

7 83.24 89.09 172.33 866.06123.722

411.69

85630.9514

9957.99

5Valid N (listwise)

7

4.2.6 Results of Descriptive Statistics


Economics 2012

The average debt crisis at Greek is around 272 billion over 2005-2012. It is to be noted that Greece, Germany, France and Italy undergo higher fluctuation in debt values whereas debt has been seen stable in Belgium, Finland, and Netherland and Portugal.

4.2.7 VAR ( Vector autoregressive regression Analysis )\

Vector auto regression (VAR) is a statistical model used to capture the linear interdependencies among multiple time series. All the variables in a VAR are treated symmetrically; each variable has an equation explaining its evolution based on its own lags and the lags of all the other variables in the mode

Chart 4.7


http://en.wikipedia.org/wiki/Lag

http://en.wikipedia.org/wiki/Time_series



http://en.wikipedia.org/wiki/Statistical_model

http://en.wikipedia.org/wiki/Autoregression

http://en.wikipedia.org/wiki/Vector

Economics 2012

2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

-500

0

500

1000

1500

2000

2500

AustriaBelgiumFinlandFranceGermanyGreeceItalyNetherlandsPortugalSpain

Year

Gov

t Net

Deb

t

Table:4.9 ( Forecasted value of Debt from 2012 – 2017)


Economics 2012

Country 2012 2013 2014 2015 2016 2017

Austria 168.426 175.515 181.167 186.555 191.756 191.756Belgium 317.98 326.345 331.398 333.342 334.614 334.864

Finland-

111.875-110.24

-109.512

-109.077

-109.242

-110.005

France1,715.3

21,798.2

61,865.3

21,915.0

01,942.1

01,949.65

Germany1,431.7

71,453.2

41,464.8

81,502.3

61,540.6

61,579.9

8Greece 332.355 339.421 334.098 331.058 327.285 322.708

Italy1,606.8

71,629.8

81,655.5

31,678.2

71,699.7

81,719.0

1Netherland

s219.605 250.291 280.639 306.911 328.901 346.132

Portugal 185.976 194.174 199.145 202.993 206.437 209.86Spain 712.215 775.35 835.163 884.586 931.416 983.022

4.3 Government Debt to GDPIt is not always a good idea to conclude based on actual government net debt figures, but it is much more important to understand how much debt is remaining to be paid over how much revenue is generated by current economy. So a ratio of debt over GDP gives a clear idea in percentage terms, that how much of amount is to be paid back over their gross revenue.

4.4 Analysis of Data set- II

Table 4.10 ( Government debt to GDP )

Country 2007 2008 2009 2010 2011Austria 60 63 69 71 72Belgium 84 89 95 96 98Finland 35 33 43 48 48France 44 54 69 79 85

Germany 64 66 74 83 81Greece 167 174 194 200 211

Italy 105.00 113.00 129.00 145.00 165.00

Netherlands 45 58 60 62 65

Portugal 103 105 116 118 120Spain 36 40 53 61 68

Sources : Tradingeconomic.com ( 2008-2011) data


Economics 2012

4.4.1 Multiple Regression Analysis

4.4.1.1 Independent Variable A: Government Debt of GDP in Austria B: Government Debt of GDP in Belgium Fi: Government Debt of GDP in Finland Fr : Government Debt of GDP in of France Ge : Government Debt of GDP in of Germany I: Government Debt of GDP in of Italy N : Government Debt of GDP in of Netherland P : Government Debt of GDP in of Portugal S : Government Debt of GDP in of Spain

4.4.1.2 Dependent variable

Gr: Government Net Debt of Austria

Sample : Tradeeconomic.com ( 2008-2011) data

Table 4.11

Variables Entered/Removed(b)

Model

Variables Entered

Variables Removed Method

1 Spain, Netherlan

d, Germany, Portugal(

a)

. Enter

a Tolerance = .000 limits reached.b Dependent Variable: Greece

Table 4.12

ANOVA(b)

Model

Sum of Squares Df

Mean Square F Sig.

1 Regression 1338.800

4 334.700 . .(a)

Residual .000 0 .Total 1338.80

04


Economics 2012

a Predictors: (Constant), Spain, Netherland, Germany, Portugalb Dependent Variable: Greece

Table 4.13Coefficients(a)

Model

Unstandardized Coefficients

Standardized

Coefficients t Sig.95% Confidence Interval

for B

BStd.

Error BetaLower Bound

Upper Bound

1 (Constant)

64.378 .000 . . 64.378 64.378

Germany .541 .000 -.253 . . -.541 -.541Netherland

.147 .000 .062 . . .147 .147

Portugal .884 .000 .378 . . .884 .884Spain 1.101 .000 .817 . . 1.101 1.101

a Dependent Variable: Greece

Table 4.14Excluded Variables(b)

Model Beta In t Sig.

Partial Correlation

Collinearity StatisticsToleranc

e VIFMinimum Tolerance

1 Austria .(a) . . . .000 . .000Belgium .(a) . . . .000 . .000Finland .(a) . . . .000 . .000France .(a) . . . .000 . .000Italy .(a) . . . .000 . .000

a Predictors in the Model: (Constant), Spain, Netherland, Germany, Portugalb Dependent Variable: Greece


Economics 2012

Chart 4.8 Chart 4.9

Chart 4.10 Chart 4.11


Economics 2012

4.4.1.3 Interpretation of regression analysis

R square value: R Square value = 1.0.which shows that the relationship is 100% accurate to define the existing relationship between Debt Crisis of GDP in Greece (Ge) and debt crisis in other countries like Austria(A), Belgium(B), Germany(Gr), France(Fr), Finland(Fi), Italy(I), Netherland(N), Portugal(P) and Spain(S) .T-test: The independent variable’s t-value is blank in Spss output since R square value =1, which shows that the Greece debt in GDP crisis have a greater impact on other euro countries.

F-test Significance Level: All significant values are blank showing close to 0, that means p = 0 < 0.05,hence all the independent variables assumed are significant enough to support these analysis. However A,B,Fi,Fr and I variables are excluded value in output: Slopes N,P,S,Gr are Positively related. Constant is negatively related

Multiple Regression linear Equations

Gr = C + F(X)

C = 64.38

F(X) = 0.147 N + 0.884 P + 1.101 S + 0.541 Gr

Gr = 0.147 N + 0.884 P + 1.101 S + 0.541 Gr - 64.38

4.4.2 Results of regression analysis

The debt to GDP ratio in Greece has impact on debt to GDP ratio in Netherland, Spain, Portugal and Germany. Gr α N,P,S,Gr

The debt crisis in Greece will be contagious to all the above mentioned four countries. Debt to GDP ratio in Greece will have highest influence on Spain ,where the adverse effect

of shockwaves can generate equivalent percentage of debt to GDP crisis in Spain. Other countries which would get badly affected are Portugal followed by Germany.

Netherland seems to be stable and unaffected.


Economics 2012

4.4.3 Correlation Analysis

Table : 4.15

Correlation Matrix Generated by Excel Spreadsheet

4.4.3.1 Interpretation of correlation analysis

All euro countries are strongly correlated with correlation coefficient value above R > 0.75 .

4.4.4 Results of correlation analysis

The debt crisis to GDP in Greece were strongly correlate with Spain followed by France and Portugal. showing that Greece debt crisis is much more contagious to these countries.

In general all euro countries are very strongly correlated to each other. Euro crisis in one country will be a growing concern for all other Eurozone countries.

Chances of countries like Austria, Belgium, France, Italy, Finland, Portugal and Spain is more than 98% to be affected by economic slow down caused due to debt crisis in Greece.

However Netherland seems to be little bit stable with crisis at Greece and will have lesser impact compared to other euro countries.


Economics 2012

4.4.5 Descriptive Statistics for Debt to GDP.

Table 4.16

Descriptive Statistics

N RangeMinimu

mMaximu

m Mean

Std. Deviatio

nVarianc

e

Statistic Statistic Statistic Statistic StatisticStd. Error Statistic Statistic

Austria 5 12.00 60.00 72.00 67.0000 2.34521 5.24404 27.500Belgium 5 14.00 84.00 98.00 92.4000 2.58070 5.77062 33.300Finland 5 15.00 33.00 48.00 41.4000 3.17175 7.09225 50.300France

5 41.00 44.00 85.00 66.2000 7.6380617.0792

3291.700

Germany 5 19.00 64.00 83.00 73.6000 3.82884 8.56154 73.300Greece

5 44.00 167.00 211.00189.200

08.18169

18.29481

334.700

Italy5 60.00 105.00 165.00

131.4000

10.85173

24.26520

588.800

Netherland 5 20.00 45.00 65.00 58.0000 3.44964 7.71362 59.500Portugal

5 17.00 103.00 120.00112.400

03.50143 7.82943 61.300

Spain5 32.00 36.00 68.00 51.6000 6.07124

13.57571

184.300

Valid N (listwise)

5

4.4.6 Results of Descriptive Statistics

Within the Euro countries, Greece is having the highest debt crisis of 189.2 followed by Italy ( 131.4) and Portugal (112.4).

The debt crisis is very instable in case of Italy and France where chances of figure getting fluctuated is at higher risk. Austria and Belgium have a very consistent debt to GDP over period of time.


Economics 2012

Chart 4.12

2006.5 2007 2007.5 2008 2008.5 2009 2009.5 2010 2010.5 2011 2011.50

50

100

150

200

250

AustriaBelgiumFinlandFranceGermanyGreeceItalyNetherlandsPortugalSpain

Year

Govt

Deb

t to

GDP


Economics 2012

4.4.7 Country wise Correlation Coefficient trend

Chart : 4.13

References : Sabastian Misso,Sabastian Watzka, (Aug 2011), “Financial Contagion and European Debt Crisis", Ludwig Maximilian - University of Munich, pp.2-4


Economics 2012

5. Key Observations

5.1 Observations and Findings

1. All major euro countries under observation for this research are contagion to Greek Debt crisis.

2. Spain, Belgium, Italy, Portugal and France are the countries that get affected worst by such crisis. Chances of getting impacted are above 90%.

3. However Finland and Netherland are the two countries that remain stable and very little affected by debt crisis in Greece. Chances of getting affected for Finland are below 40%.

4. Finland is the country that is least affected by debt crisis by any other country in euro zone.

5. Greece is the worst affected country in terms of debt with an average of 189 debt to GDP followed by Italy,Portugal and Belgium, as the research itself says that these are the countries that are most vulnerable to Greece debt crisis.

6. Finland is under surplus of funds and is not running under any debt crisis.7. Chances of Debt to GDP value of France and Italy varying over its average is higher

than any other countries and thus need to be updated on financial situation in Greece. 8. Austria is a country that would fluctuate least over its average value when affected by a

debt crisis cause by Greece.

9. The average debt crisis caused due to all 10 countries under observation, is 88.32% of GDP with a standard deviation of 11.54.

10.8Belgium,Portugal and Spain should closely observe the Greece market as the trend analysis concludes a very similar pattern of rise and fall in these countries.

11.Euro countries are very much correlated to each other in terms of trade, growth and financial stability. The correlation matrix shows that other than Finland all countries are interrelated and vulnerable to each other’s economy. One country getting affected can slow down the economy over other country. The main reason behind this is the governing body which is standardized over this region leading to standard financial practices, fiscal and monetary policy and also the nature of consistent trade in between these countries.


Economics 2012

6. Conclusion

Greece debt crisis is contagion in nature and can disturb the financial stability of euro countries to a great extent especially in countries like Spain, Belgium, Portugal and Italy. Thus it is matter of concern for common governing body to observe and evaluate the financial stability of Eurozone, estimate the risk involved and devise a contingency plan in such a way that when one country undergoes financial crisis, it is well informed beforehand and a backup plan is ready to be executed to avoid these crisis getting contagion to neighboring countries.


Economics 2012

7. Appendix - 1

General government net debt in Billions ( Unit in respective National Currency) 1996-2011

Country1996 1997 1998 1999 2000 2001 2002 2003 2004

Austria 90.014 84.585 85.735 90.37 90.01 93.833 95.65 96.863105.9

32

Belgium 245.798 247.616247.55

1245.8

41246.01

4246.50

9250.16

1248.93

5243.5

36

Finland -39.571 -47.809-

101.224

-61.49

9

-41.102

-44.085

-44.987

-55.947

-71.06

8France 621.1 655.7 689.8 710.9 740.4 767.4 819.6 901.1 971.2

Germany 772.484 817.023849.39

3876.9

63841.97

4890.10

6955.4

1,042.85

1,115.94

Greece 65.504 73.791 83.12693.64

2105.48

8118.83

2133.86

5167.72

4183.1

23

Italy 1,076.20 1,090.201,104.3

81,097.

761,115.

681,161.

111,162.

591,186.

621,230.

58

Netherlands 124.036 122.09120.06

8101.5

82103.90

9108.79

4116.84

2128.21

9137.9

63

Portugal 54.259 47.446 47.29749.72

253.31 62.221 67.434 73.25

79.299

Spain 284.654 301.684 309.53316.18

8317.35

2324.06

3321.13

5323.93

5324.9

14

Country 2005 2006 2007 2008 2009 2010 2011

Austria 108.86 111.549 111.982 118.802 135.732 148.916 158.065Belgium 248.705 245.996 245.541 254.249 271.384 284.385 307.108

Finland -92.24 -115.08-

130.415-96.841

-108.271

-116.242

-114.705

France 1,043.601,072.6

01,123.60 1,203.80

1,360.00

1,478.60 1,604.90

Germany 1,189.511,227.1

01,223.27 1,236.82

1,345.10

1,406.90 1,441.26

Greece 195.387 224.204 239.364 262.318 298.706 328.588 355.78

Italy 1,276.981,333.7

21,350.48 1,398.43

1,476.14

1,538.26 1,573.30

Netherlands

133.951 132.164 123.687 122.566 131.786 161.858 192.033

Portugal 89.092 94.194 107.785 115.85 132.833 153.973 172.33


Economics 2012

Spain 316.888 302.108 281.191 335.048 445.333 522.401 611.265

Sources : IMF ( April 2012)

8. Appendix - 2

( Government debt to GDP )

Country 2007 2008 2009 2010 2011Austria 60 63 69 71 72Belgium 84 89 95 96 98Finland 35 33 43 48 48France 44 54 69 79 85

Germany 64 66 74 83 81Greece 167 174 194 200 211

Italy 105.00 113.00 129.00 145.00 165.00

Netherlands 45 58 60 62 65

Portugal 103 105 116 118 120Spain 36 40 53 61 68

Sources : Tradingeconomic.com ( 2008-2011) data


Economics 2012

9.Glossary

* Analysis of Variance (ANOVA): A statistical method establishing the existence of a difference between several sample means.

* Autocorrelation: The same variable is observed over time. The observations produce different values which are correlated.

* Confidence Level: A probability that is used to determine, with confidence, that the true population value is represented in the statistical distribution.

* Correlation Analysis: A statistical technique that helps in determining the strength of the relationship between variables.

* Dependent Variable: A concept that's value changes as an independent variable changes. Statistics are used to

explain the strength of the relationship between the two variables. Can also be called a criterion variable.

*F-Test: A statistical probability test measuring a calculated value’s ability to occur due to chance.

* Focus Group:A marketing research technique for qualitative data that involves a small group of people (6-10) that share a common set characteristics (demographics, attitudes, etc.) and participate in a discussion of predetermined topics led by a moderator

* Independent Variable:A variable that is controlled or manipulated by the researcher.

* Mean: An average found by summing all observations then dividing the total number of observations.

* Multiple Regression Analysis: Statistical procedure identifying the relationship between two or more independent variables in an effort to identify patterns within the relationship.

* Nominal Scale: A measurement scale identifying variable categories. For example, male/female, user/nonuser.


Economics 2012

* Non-Probability Sample: A sample of the population chosen by the investigator rather than by using probability to choose the participants. By doing this, a true representative cross section of the population is foregone.

* Population: The entire set of subjects that an experiment is attempting to identify.

* Primary Research:Research conducted in search of new data to solve a marketing information discrepancy.

* Probability Sample:Each element in the population has a known nonzero probability of being selected for inclusion in a study. Also called random sampling.

* Range: The spread of data, from the lowest variable to the highest variable.

* Ratio Scale:A response scale for a survey or questionnaire that categorizes responses ranking them from smallest to largest and has a consistent range between each of the category choices.

* Reliability:A consistent method that often yields the same results each time that it is measured.

* Sample: A group that is selected to study as a representative of the true

* Sample Population:The description of the characteristics that define a particular population.

* Sample Size:Number of sample units to be included in the sample.

* Scale:A technique used for participants to measure an object based on set characteristics. Scales are close-ended questions that require one of the offered responses as the respondent’s answer.

* Secondary Research:The analysis of research that had been collected at an earlier time (for reasons unrelated to the current project) that can be applied to a study in progress.

* Standard Deviation:A measure of dispersion that is found mathematically by the positive square root of the average squared difference between the mean and the sample or population values.


Economics 2012

* Standard Error:The error between the mean and the actual value as defined by the standard deviation. Standard error can also be found by taking the square root of the variance.

* T-Test:A statistically hypothesis test that is based on a single mean when the sample size is not large enough to use the Z-test.

* Variable:A quantity with an assigned value that may change during research.

* Variance:Variance measures the dispersion of a variable about its mean.


Economics 2012

10. References

Referred Document:

Sabastian Misso,Sabastian Watzka, (Aug 2011), “Financial Contagion and European Debt Crisis", Ludwig Maximilian - University of Munich, pp.2-4

Website Links:

DATA SET-I : www.imf.org/download)

DATA SET-2: http://www.tradingeconomics.com/government-debt-to-gdp-list-by-country

Statistical Data : http:// www.mathstool.com/stats/

Glossary : http://www.marketresearchterms.com/xyz.php


http://www.marketresearchterms.com/xyz.php

http://www.mathstool.com/stats/

http://www.tradingeconomics.com/government-debt-to-gdp-list-by-country

http://www.imf.org/download

contagion effect of greece debt crisis

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