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QUESTION 2 Use the dataset: Energy.RData, Use R to run a cross sectional regression on energy use per capita for the listed countries as follows: Intpes_pc = Bo + Bilnypcpenn + Bzlnypcpenn2+ Bzin_gasprice + +B4ffrents + BsInpop + BoInland + u The variables are defined as follows: Intpes_pe - log of total primary energy consumption per capita (ktoe) Inypcpenn-log of GDP per capita (USD) Inyrepenn2 = square of log of GDP per capita (USD) In_gasprice - log of pump price for gasoline (USD/liter) ffrents - Fossil Fuel Rents (% of GDP) Inpop - log of population (in millions) Inland - log of land area (in km2) I Incomegroup = refers to income groups "1","2" and "3", low, mid and high income countries. *"'Log" always refers to natural logs or "In" here. Log referrers to natural logarithm!
1) Present your regression results in a table below (R output): 5 marks 2) Interpret the constant and its p-value. 3) Interpret the coefficient on gas price and its p-value. 4) Interpret the coefficient on land area and carry out a t-test to determine the significance of the coefficient. 5) Interpret the coefficient on ff-rents and its p-value. 3x4=12 marks 6) The above model belongs to the class of nonlinear equations. Calculate the turning point of the nonlinear relationship. 3 marks 7) Is this a U-shaped or inverted U-shaped relationship? 1 marks 8) Define the 5 Gauss Markov assumptions (5*0.5 marks) and elaborate whether they are likely to hold for this regression output model or not (5*0.5 marks) 5 marks (Total: 26 marks)
FORMULA SIIEET Critical values for the standard normal distribution (2) Confidence level (1-c) Level of Significance (a) Two Sided Critical Value C:2 Onc-Sided, Upper-Tail Critical Value Cc 1.28 Onc-Sided, Lower-Tail Critical Value -ca -1.28 90% 10% 1.645 95% 5% 1.96 1.645 -1.645 99% 1% 2.58 2.33 -2.33 Formula for a t-statistic estimate - hypothesised value t = standard error * Formula for a (1-(2)% confidence interval CIL-u = (B - Ca/2 * se(B), B + C«!2 * se()) Logarithmic Quadratic/Interaction specifications For the model log(y) = R + fix. +32X2, the exact effect of a change in explanatory variable X2 is: %Aỳ = 100(exp(3,4x2) - 1] For a quadratic specification of the form: y = Bo + Bix + B2x2 + u The turning point (maximum minimum) is given by: ** = 1/(282) The approximation of the marginal effect of x on y is given by: Ду 8. +2, Ax For a interaction specification of the form: y = Be + B x1 + B2x1 * x2 + u The approximation of the marginal effect of x; on y is given by: 49. B. + 2x2
There is an excel sheet file that is needed for this QUESTION 2 Use the dataset: Energy.RData, Use R to run a cross sectional regression on energy use per capita for the lis
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