Economic research provides evidence for a positive relationship

Asylum Applications and GDP

Economic research provides evidence for a positive relationship between Gross Domestic Product (GDP) and Asylum Applications across the EU. This provides support for the view that a strong economy tend to have higher level of asylum applications.

The Table below gives Asylum Applications and an index of GDP per capita (written as GDP for short) for each year from 2003 to 2014. A regression analysis with Asylum Applications as the response variable and GDP as the predictor variable is shown at the end of the question.

Table: Asylum Applications and GDP in each year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

GDP

140

145

147

148

148

134

129

130

131

132

133

137

Asylum Applications

7,483

4,766

4,323

4,314

3,985

3,866

2,860

1,935

1,290

955

945

1,450

Regression Analysis: Asylum Applications versus GDP (GDP per Capita (index))

1. Discuss the interpretation of the panel in the Regression Analysis output below which relates to the regression coefficients stating the Null Hypothesis
2. Obtain and interpret a confidence interval for the slope parameter and explain how it relates to the corresponding t-test of part (a)

1. How would you interpret the expected response of the Asylum Applications to an increase of 1 unit in GDP per capita?

1. The output contains intervals corresponding to values for GDP of 140 and 177 respectively. Explain how the two types of interval should be interpreted for both levels of absences

1. Given the true GDP value in 2015 turned out to be 177 and the actual level of Asylum Applications was 3,276, would you be willing to use this model for future predictions?Explain your answer taking into consideration the Adj-R squared value

Explain your answer taking into consideration the Adj-R squared value

Regression Equation

Asylum Applications = -19.720 + 166 GDP

Predictor

Coef

SE Coef

T-Value

P-Value

Constant

-19,720

9,104

-2.16

0.06

GDP

166

66

2.51

0.03

Adj-R-Squared

33%

Analysis of Variance

Source

DF

SS (000)

MS(000)

F-Value

P-Value

GDP

1

16,695

16,695

6.33

0.031

Error

10

26,355

2,635

Total

11

43,050

Predicted Values for New Observations

GDP

Fit

SE Fit

95% CI

95% PI

140

3,526

469

(2,440, 4,611)

(-235, 7,305)

177

9,668

2,634

(3,839, 15,499)

(2,806, 16,351) XX

XX denotes an extremely unusual point relative to predictor levels used to fit the model