University |
The Royal Melbourne Institute of Technology (RMIT) |

Subject |
ECON1272: Basic Econometrics Assignment |

^{th}Jun 2023

# 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!

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***”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

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