1 Question 1: NIPA (8 pts) 1.1 Sub-question a: GDP The Bureau of Economic Analysis (BEA) provides U.S. economic data on many macroeconomic aggre gates, like GDP, aggregate consumption, investment, imports, exports, income, and so on. 1. Find their website. 2. Find their \"interactive data" on GDP and the NIPA historical data. 3. Under \"Supplemental Tables\" you should nd Tables 7.1. Download it quarterly from 1960-2021 by clicking on the Table, clicking the \"modify\" option and choosing the proper dates and frequency. 4. Once you have this, download them in excel. Note that for some versions of excel, some data will be in "sheetO'I and other data will be in \"sheetl,'I with tabs amasible from the bottom left of your excel sheet. Combine the two. (If your excel won't let you go past a certain number of columns, try re-saving as an xlsx le). 5. On the same plot, graph real (\"chained 2012") and nominal (\"current dollars") GDP per capita. 6. Label your axes and what you're graphing in the chart title. Call this chart 1 {and plot it in your homework). How do real and nominal GDP compare? What year do they press in? Why? 1.2 Sub-question b: tietrending 1. Create three new rows, containing [1) the natural log of chained GDP (calculated, for in stance, as \"=ln(c19)\"). (2) The natural log of chained durable goods. (3} the natural log of the sum of chained nondurable goods, and chained services. 2. Add a row above them displaying the year (1960 Q1 would be 1960, 1960 02 would be 1960.25, and so on). I'll call this your \"year" variable, even though it is quarterly. 3. For each of the series, calculate the intercept and slope of each logged line by using the \"=INTERCEPT( LIST OF Y's , LIST OF X's)\" and \"=SLOPE( LIST OF Y's , LIST OF X's)\" function in excel. 4. Report your intercept and slope in a table like the one below (my numbers are not correct, but are in the ballpark of what you'll be getting. If you don't, you're likely using the wrong Xls or not using your logged values): Concept Intercept Slope GDP 25 0.03 Durable Goods 70 0.03 Nondurable 30 0.02 5. Create \"tted" values for each of your three data series by taking the t (intercept + slope'myear") for all quarters. 6. Create \"reneidual'I values for each of your three data series by subtracting the actual logged value [calculated in step 1) from its t {calculated in step 5). Graph them, properly labelling your axes and values. 7. The difference between naturally logged data and its t [datat) can be interpreted as a \"deviation as a percent of baseline.\" So a value of 0.1 would be \"10% higher than baseline.\" Which of these values is most volatile compared to its baseline? What's the economic reason why