3.2 Unit Root Testing
3.2. Unit Root Testing
A lot of macroeconomic data displays trends and non-stationarity. A stationary series is one whose statistical properties are constant over time. Non stationary variables can lead to a spurious relationship. A spurious relationship is when two or more variables are not causally related but may be perceived that way because of other factors e.g. similar trends. Therefore, to ensure the results are as accurate as can be, a unit root test was conducted.
The Augmented Dickey Fuller test was used to test for stationarity on all variables and, as expected, all variables were found to have a unit root. Because of this, the first difference of every variable was taken.
3.3. Variables
The variables I will use include:
Peace …show more content…
Education has a large influence on human capital also; therefore, an investment in this should lead to higher human capital, which is seen to encourage peacefulness in a country. I believe this figure can also inform us of the importance of education in that country, in comparison to other government expenditures.
Military Spending: World Bank WDI expresses military spending as a percentage of GDP. “Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces.” (World Bank, 2017).
This variable is seen as a hindrance to the level of peace. If there is an emphasis on military spending this could suggest negligence on spending of other sectors.
Population: I have used data from World Bank which both provides the average population of each country each year. A higher population means an increased demand for primary commodities – for which there is only a finite amount. This can also create tensions if there is competition for services such as education, healthcare and employment. I took the log of the population to improve its accuracy as otherwise the numbers would have been too
A lot of macroeconomic data displays trends and non-stationarity. A stationary series is one whose statistical properties are constant over time. Non stationary variables can lead to a spurious relationship. A spurious relationship is when two or more variables are not causally related but may be perceived that way because of other factors e.g. similar trends. Therefore, to ensure the results are as accurate as can be, a unit root test was conducted.
The Augmented Dickey Fuller test was used to test for stationarity on all variables and, as expected, all variables were found to have a unit root. Because of this, the first difference of every variable was taken.
3.3. Variables
The variables I will use include:
Peace …show more content…
Education has a large influence on human capital also; therefore, an investment in this should lead to higher human capital, which is seen to encourage peacefulness in a country. I believe this figure can also inform us of the importance of education in that country, in comparison to other government expenditures.
Military Spending: World Bank WDI expresses military spending as a percentage of GDP. “Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces.” (World Bank, 2017).
This variable is seen as a hindrance to the level of peace. If there is an emphasis on military spending this could suggest negligence on spending of other sectors.
Population: I have used data from World Bank which both provides the average population of each country each year. A higher population means an increased demand for primary commodities – for which there is only a finite amount. This can also create tensions if there is competition for services such as education, healthcare and employment. I took the log of the population to improve its accuracy as otherwise the numbers would have been too