Question

Model 1

Linear regression Number of obs F16, 121) Prob > F R-squared Root MSE 128 114.89 0.0000 0.8793 .44427 Intran_pc Coef. Robust Std. Err. t P t1 (95% Conf. Intervall Inypcpenn in_gasprice In annualprecip In_temperature inland .8959857 - 263207 - 0611447 .0072154 .0096112 .1702616 -2.263816 .0706039 .0715285 .0334971 0940917 .0241781 .1391713 .9382594 12.690.000 -3.68 0.000 -1.83 0.070 0.08 0.939 0.40 0.692 1.22 0.224 -2.41 0.017 7562067 - 4048165 -. 1274611 -. 1790639 - 0382557 -.1052647 -4.121348 1.035765 -. 1215975 .0051717 . 1934948 .057478 .445788 -.4062838 oecd _cons

Model 2

1. Countries have a keen interest in exploring the drivers of their sectoral energy consumption, including TRANSPORTATION energy use. These models will examine the log of final energy use by TRANSPORTATION “ln_tranpc” across 128 countries. All variables with names beginning “ln” are measured in natural logarithms. The variable oecd is a dummy variable equal to 1 for countries in the OECD and equal to zero otherwise. The variables are described below:

Lntran_pc = log of transportation energy consumption per capita (ktoe)

Lnypcpenn =log of GDP per capita (USD)

Lnypcpenn2 =log of GDP per capita SQUARED

Ln_gasprice = log of pump price for gasoline (USD/liter)

Ln_temperature = log of the average annual temperature (in C)

Ln_annualprecip= log of annual precipitation (mm)

Ln_land = log of the land area of a country

OECD = a dummy (indicator) that takes on the value of 1 if the country is OECD member, zero otherwise.

  

Q2:The second model has a quadratic specification of the log of per capita GDP (lnypcpenn for the level term lnypcpenn2 for the squared term) [Model 2].                  

1. Interpret the coefficient estimates for the quadratic specification of the log of per capita GDP (lnypcpenn) at the value lnypcpenn=9.

b. What are the major differences in the other coefficient estimates compared to model 1? Please comment on the size and statistical significance of the coefficient estimates.

c. Which model do you think is more appropriate (number 1 or 2)? Please justify your answer.

Answer 1

However we can still choose model 1 over model 2 because of its simplicity by being linear completely.

- en tempera Model 1: m198F$14.99, broj 2,2- 0.9993, entran_pc = lrypcpenn & In-gas price. 't ln-annual precept In temperative & Inland toedd & cons. - Model 2: M₂ = 128, f2 = 108.32, p = 0, R2 = 0 0.8809 entran pc = ln pcpenn & lupespennt en gas-price flu annualprice I t la temper lnu temperature + ln_animal precip-tenland toecd+cous. In Model 2, there is 1 extra indep var 'ln upcpenn @ quadratic specification is in Model 2 with term 'ln upcpenu) e and its coeffis = -0.828 2925. and coeff of ln up spenn = 1374417 At Onyepespenn" g. we have effect - 1.374417 X 9 -0.8282925 X 9 - = -54.7219395 ~ -54.722 At value luyp spenn = 9, there is an effect of -54.792 on entran po 6 The coefficients estimates of all the variables in 2 model is different just because of one additional quadrathe term en gpspenn Size ni = m = 128 (same for both) & pvalue = p 1 = p2=0 (<0.05) (for both models) which means tooth the models are statis cally significant Also R² = 0.8793 ~0.88 a R² = 0.88 or both the variables explam 88% variability for dep vasit for individual ubls, pvalue $0.05 those ubs are not significant Model 1: en ammalprecip, in temperature, Inland, oed & not sig. model 2: en uppenune lentemperature , lyland, reced are not sig. © Both muodels are equally appaiate interms of their rest siguificance 3 KY model

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