Quantitative Methods and Econometrics

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 .

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 2010 sh S18” contains data on a sample of 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 predicted CO2 emissions. Propose an economic theory to justify this model of CO2 emissions as a function of population, and explain what the parameters and variables in this model represent.

2. Now estimate this model in Excel, like we did in class, using ordinary least squares. Report and interpret your estimated parameters here. Specifically, what does each parameter estimate tell us?

3. What is the estimated elasticity of CO2 emissions with respect to population? (Always evaluate elasticities at the means of the two variables, unless we tell you otherwise.)

4. Propose an economic theory to justify adding the income variable to your regression model, briefly describing the theory and its assumptions and showing us what your multiple regression model looks like.

5. Now expand your Excel spreadsheet and use OLS to estimate your multiple regression model. Report and interpret your results.

6. What is the estimated elasticity of CO2 emissions with respect to income?

7. Does the inclusion of income in your regression model affect your estimated elasticity of CO2 emissions with respect to population? Why or why not?

8. Based on your findings, what would you conclude is the main driver of countries’ carbon footprints—population or income? Please explain.

9. Based on your regression model, do income and population explain the difference in observed CO2 emissions between China and the United States? Between the United States and Germany? Why or why not?

10. Compare the R-squared from the simple and multiple regression. Are they different? If so, what would explain the difference?