ECON1272: Basic Econometrics Assignment, RMIT, Singapore: Use R to run a cross-sectional regression on energy use per capita for the listed countries

QUESTION 1

Use the dataset: Energy.RData,

Use R to run a cross-sectional regression on energy use per capita for the listed countries as follows:

lntpes_pc = ! + “lnypcpenn + #ln _gasprice + $ln _annnualprecip + %ffrents +
&lnpop + ‘lnland +

The variables are defined as follows:

lntpes_pc = log of total primary energy consumption per capita (ktoe)
lnypcpenn =log of GDP per capita (USD)
lnypcpenn2 = square of log of GDP per capita (USD)
ln_gasprice = log of pump price for gasoline (USD/liter)
ln_annualprecip= log of annual precipitation (mm)
ffrents = Fossil Fuel Rents (% of GDP)
lnpop = log of population (in millions)
lnland = log of land area (in km2)
Incomegroup = refers to income groups “1” , “2” and “3”, low, mid and high income countries. This is a categorical variable.
Bold letters denote vectors in the regression equation!

*”Log” always refers to natural logs or “ln” here.

1) Present your regression results in a table below (R output).
2) Interpret the constant and its p-value.
3) Interpret the coefficient on ln GDPPc and its p-value.
4) Interpret the coefficient on ln annual precipitation and its p-value.
5) Interpret the coefficient on ff-rents and its p-value.

6) Calculate the following statistics:
a. mean of the log energy use per capita variable
b. mean of the log energy use per capita variable for the low-income group
c. mean of the log energy use per capita variable for the middle-income group
d. mean of the log energy use per capita variable for the high-income group

7) Which of the income groups (categorical variable) is the base group Interpret the coefficients on the remaining categories (2 coefficients)!

8) Interpret the adjusted R squared of the regression.

9) Is the degrees of freedom high enough for standard normal critical values to be applied here? Give an explanation!

10) Define the 5 Gauss Markov assumptions (5*0.5 marks) and elaborate on whether they are likely to hold for this regression output/ model or not