Country Name United States Afghanistan Albania Algeria Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cabo Verde Cambodia Cameroon Canada Central African Republic Chad Chile China Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany CO2(kt) POP(millions) GNI(Millions of US$) 5433056.54 309.33 15170300.00 8236.08 28.40 15998.78 4283.06 2.86 11807.46 123475.22 37.06 160996.42 373080.58 22.03 1096901.32 66897.08 8.39 393108.52 45731.16 9.05 49435.84 2464.22 0.36 7702.50 24202.20 1.25 23340.38 56152.77 151.13 124617.10 1503.47 0.28 4321.85 476.71 0.72 1497.42 15456.41 10.16 18785.53 31125.50 3.85 17126.87 5232.81 1.97 13197.27 419754.16 195.21 2104398.02 44678.73 7.40 47167.59 1683.15 15.54 9202.85 308.03 9.23 2014.76 355.70 0.49 1591.15 4180.38 14.36 10698.07 7234.99 20.62 23358.49 499137.37 34.01 1582763.45 264.02 4.35 1981.47 469.38 11.72 10302.35 72258.24 17.15 202873.97 8286891.95 1337.71 5904605.99 7770.37 4.67 35553.17 5804.86 18.98 23972.20 20883.57 4.42 57968.36 38364.15 11.28 63388.65 7708.03 1.10 22311.52 111751.83 10.47 191444.66 46303.21 5.55 325079.06 135.68 0.07 483.70 20964.24 10.02 51355.94 32636.30 15.00 68517.07 204776.28 78.08 214525.02 6248.57 6.22 20868.00 4679.09 0.70 9630.16 513.38 5.74 2097.41 18338.67 1.33 18239.97 6494.26 87.10 29825.57 1290.78 0.86 3043.13 61843.96 5.36 251109.55 361272.84 65.02 2700865.68 2574.23 1.56 12869.57 473.04 1.68 921.90 6241.23 4.45 11416.25 745383.76 81.78 3483764.77 Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hong Kong SAR, China Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Rep. Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Macao SAR, China Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia, Fed. Sts. Moldova Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway 8998.82 86717.22 260.36 11118.34 1235.78 238.36 1701.49 2119.53 8107.74 36288.63 39999.64 70655.76 406307.27 7157.98 1170715.42 20821.23 248728.94 12427.46 62.34 567567.26 93695.52 6398.92 1873.84 7616.36 20403.19 1030.43 10872.66 2013.18 1239.45 216804.04 1074.43 623.39 2588.90 102.68 2214.87 4118.04 443674.00 102.68 4855.11 11510.71 2581.57 50608.27 2882.26 3175.62 3755.01 182077.55 31550.87 4547.08 1411.80 78910.17 57186.87 24.26 11.15 0.10 14.34 10.88 1.59 0.79 9.90 7.62 7.02 4.56 7.62 59.28 2.69 127.45 6.05 16.32 40.91 0.10 49.41 2.99 5.45 6.40 2.10 4.34 0.53 2.10 21.08 15.01 28.28 0.33 13.99 0.41 0.05 3.61 1.28 117.89 0.10 3.56 2.71 0.62 31.64 23.97 2.18 26.85 16.62 4.37 5.82 15.89 159.71 4.89 31641.07 293454.05 731.14 40126.71 4302.27 846.57 2272.06 6644.82 15110.48 233476.93 183661.91 227769.19 2121166.42 12736.32 5643192.13 26218.06 128676.48 39852.51 211.80 1095599.47 126113.71 4489.30 6713.29 24579.69 37501.05 25370.95 9207.39 8643.30 5290.12 239358.02 1822.80 9003.28 7976.17 198.24 3444.76 9835.24 1042119.87 305.08 6316.18 5640.28 4086.06 88304.87 9834.28 10766.65 16116.35 841677.04 136188.35 8699.55 5674.42 349387.81 425901.89 Papua New Guinea 3135.29 Paraguay 5075.13 Peru 57579.23 Philippines 81590.75 Poland 317254.17 Portugal 52361.09 Qatar 70531.08 Romania 78745.16 Russian Federation 1740776.24 Rwanda 594.05 Samoa 161.35 Saudi Arabia 464480.56 Senegal 7058.98 Serbia 45962.18 Seychelles 704.06 Sierra Leone 689.40 Singapore 13520.23 Slovak Republic 36094.28 Slovenia 15328.06 Solomon Islands 201.69 South Africa 460124.16 Spain 269674.85 Sri Lanka 12709.82 St. Kitts and Nevis 249.36 St. Lucia 403.37 St. Vincent and the Grenadines 209.02 Sudan 14172.96 Suriname 2383.55 Swaziland 1023.09 Sweden 52515.11 Switzerland 38756.52 Tajikistan 2860.26 Tanzania 6846.29 Thailand 295281.51 Timor-Leste 183.35 Togo 1540.14 Tonga 157.68 Trinidad and Tobago 50681.61 Tunisia 25878.02 Turkey 298002.42 Turkmenistan 53054.16 Uganda 3784.34 Ukraine 304804.71 United Arab Emirates 167596.57 United Kingdom 493504.86 Uruguay 6644.60 Uzbekistan 104443.49 Vanuatu 117.34 Venezuela, RB 201747.34 Vietnam 150229.66 West Bank and Gaza 2365.22 6.86 6.46 29.26 93.44 38.18 10.57 1.75 20.25 142.39 10.84 0.19 27.26 12.95 7.29 0.09 5.75 5.08 5.39 2.05 0.53 50.90 46.58 20.65 0.05 0.18 0.11 35.65 0.52 1.19 9.38 7.82 7.63 44.97 66.40 1.07 6.31 0.10 1.33 10.55 72.14 5.04 33.99 45.87 8.44 62.77 3.37 28.56 0.24 29.04 86.93 3.81 9262.47 18618.46 137317.44 265929.44 458863.46 230038.36 112178.62 162254.86 1477812.94 5656.02 622.45 533855.47 12799.08 38478.03 926.08 2606.60 235074.91 86930.73 47507.02 508.30 357979.72 1411515.96 48950.36 663.27 1204.73 668.95 60504.61 4330.41 3802.06 501832.93 616380.88 5563.45 22626.29 305180.57 3295.00 2761.61 373.17 19669.16 42169.55 723965.76 20254.04 15713.33 134410.29 285949.29 2434464.28 37378.59 40491.77 679.09 387497.39 111512.78 9512.20 Yemen, Rep. Zambia Zimbabwe 21851.65 2427.55 9427.86 22.76 13.22 13.08 29984.28 18902.38 9263.90 Code EN.ATM.CO2E.KT NY.GNP.MKTP.CD SP.POP.TOTL Indicator Name CO2 emissions (kt) GNI (current US$) Population, total Data from database: World Development Indicators Last Updated: 03/12/2015 Long definition Carbon dioxide emissions are those stemming from the burning of GNI (formerly GNP) is the sum of value added by all resident pr Total population is based on the de facto definition of populati Source Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, U World Bank national accounts data, and OECD National Accounts data files. (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and V al Laboratory, Tennessee, United States. al Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national stati cations from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Dem munity: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database. Aaron Smith and J. Edward Taylor Your Name____________________________________ ARE 106 Quantitative Methods Problem Set 1 Be sure to write your name on this page. Enter your answers onto this Word document, expanding it to create the space you need underneath each question. Please answer each question completely, show your work, and attach your Excel output. You must submit Problem Set 1 in class on Friday, February 3, 2017 in order to receive credit. (You will lose half a grade point per day after that.) Carbon dioxide (CO2) emissions are widely believed to be a driver of global climate change. In this problem set you will use cross-section data to test what drives countries' \"carbon footprints,\" that is, their CO2 emissions. Is it population, or is income the bigger culprit? The data set \"CO2 by country 2010sh\" contains data on many countries' CO2 emissions, in kilotons; population, in millions; and gross national income (GNI), in millions of US dollars, for the year 2010. 1. Please propose a linear regression model to estimate the effect of population on CO2 emissions, and explain what the parameters and variables in this model are. 2. Now estimate this model in Excel, like we did in class, using ordinary least squares. Report and interpret your estimated parameters here. What is the elasticity of CO2 emissions with respect to population? 3. Propose an economic theory to justify adding the income variable to your regression, briefly describing the theory and its assumptions and showing us what your multiple regression model looks like here: 4. Now expand your Excel spreadsheet and use OLS to estimate your multiple regression model. Report and interpret your results here: 5. What is the elasticity of CO2 emissions with respect to GNI? 6. Does the inclusion of GNI in your regression model affect your estimated elasticity of CO2 emissions with respect to population? Why or why not? 7. Based on your findings, what would you conclude is the main driver of countries' carbon footprintspopulation or income? Please explain. 8. Based on your regression model, do income and population explain the difference in observed CO2 emissions between China and the United States? Explain. Aaron Smith and J. Edward Taylor Winter 2017 9. Compare the R-squared from the simple and multiple regression. What explains the difference between the two? 2 Question 1 The linear regression model is: y = 6538x - 25456 Where: y = Amount of CO2 in kt x = Population in Millions Question 2 Y = a + bX + e Y = -25456.24 + 6538.04 X - 2.58 Y = -25456.24 + 6538.04 X - 2.58 Y = 6538.04 X -25458.82 Elasticity = 2.78 Question 3 This can be achieved by introducing another predator variable using OLS. Question 4 The output produced is: Multiple R R Square Adjusted R Square Standard Error Observations 0.911679751 0.831159969 0.830056439 334301.9699 155 Question 5 Elasticity = 2.08 Question 6 The inclusion of GNI in regression model affect estimated elasticity of CO2 emissions with respect to population because the standard deviation reduces greatly and streamlines the multiple regression model. Aaron Smith and J. Edward Taylor Winter 2017 Question 7 Income because of its higher rate as indicated by the gradient of the regression line. Moreover, the R^2 is less for income than population. Question 8 Yes, because in both cases their multiple R is 0.91 which is close to 1. Question 9 2