Reference no: EM133084308

Please answer the questions concisely in your report, and include figures and tables of regression results were called for. These can be copied and pasted from Excel or R. Make sure they are sized and formatted to be legible, number them, and refer to them by number in the text (e.g., "... as can be seen in Table 2...").

Required Data Set

Download the Global Fuel Data 2010 and read the "Notes" tab to make sure that you are familiar with all the variables.

Using the data set on this page, answer the following questions.

1. Fuel demand in OECD countries: This largely replicates what you saw in the lectures.
   • Restricting  data to the OECD subset, for now, create two scatter plots showing the relationship between fuelcon and fuelprice and between fuelcon and gdppc. Briefly describe what they imply about the relationships between these variables.
   • Use multiple regression (for the OECD sample) to estimate the demand function for combined motor fuels (again using fuelcon and fuelprice), with quantity as a function of both the price and income (per capita GDP). Run the regression once in a linear specification and once in terms of the natural logs of the variables. Display the tables of results.
   • Discuss the regression results: interpret the slope coefficients, discuss their statistical significance, and discuss the goodness of fit of the regressions. Are the results consistent with what you would expect for a demand relationship?
2. Fuel demand for non-OECD countries:
   • Repeat all parts of #1 for the sample of non-OECD countries.
   • Compare the results for the OECD and non-OECD samples. Do the intercepts and the effects of price and income appear to be similar across these groups of countries? In comparing across regressions, use the 95% confidence intervals to give a rough idea whether the estimates are "close" to each other or quite different. What do you think might account for any substantial differences?
3. A skeptic suggests that price and income are far less important in determining cross-country differences in fuel consumption than is the average distance people have to drive to get where they need to go. Using variables available in the spreadsheet, see if you can test some version of this conjecture, with a scatter plot and multiple regression(s). Make sure you explain what you have done, and include controls for price and income in all regressions. Does the evidence bear out the skeptic's claim? Does this purported effect help account for any major differences between the OECD and non-OECD samples?
4. For the OECD sample, examine the gasoline and diesel fuel markets separately. Are these estimates roughly similar to the estimates for combined fuel? Make sure to include and refer to plots and tables as appropriate.

Requirements

• Using the data delivered in the downloadable above, deliver a report in the form of a Word or PDF document and upload it to this page.
• In discussing regression results, always interpret the the estimates in words (such as, "this coefficient indicates that a 1% increase in price is associated with a 5% reduction in quantity, other variables held constant"), make sure to consider both economic (size of effect) and statistical significance, and discuss goodness of fit (adjusted R-squared).
• Make comparisons between regression results where called for, and cite specific evidence from your results to answer the questions.