Question 1 (25 points): Jack lives in Boston. Sam lives in Seattle.
Fill in the blank cells of the table below. Show your work by typing in the formulas you use.
Visit the Inflation page at the U.S. Bureau of Labor Statistics web site (http:
www.bls.gov/cpi/). Download data for the nonseasonally-adjusted CPI-U All Items for Boston and Seattle. Use annual averages, which have the column heading: “Annual.”
Compound Annual Percent Change
2015
2016
XXXXXXXXXX
Jack earned in Boston
$40,000
$54,000
Sam earned in Seattle
$57,000
$85,000
CPI-U All Items for Boston
CPI-U All Items for Seattle
Jack’s real earnings in Boston
Sam's real earnings in Seattle
a) From the CPI-U data above, are prices higher in Boston or Seattle? Explain fully.
) What happened to real earnings between 2015 and 2016 for Jack and Sam?
Question 2: (25 points): Please answer both parts of the question. Fully explain which curve(s) shift and why.
a) Suppose that the real wealth rises, other things constant. Using the IS/LM model, demonstrate the impact that this would have on equili
ium real income and the interest rate.
To place lines on the graph:
Click on the Insert Ta
Click on the Shapes Button
Select a straight line (by clicking on it)
This will turn the cursor into cross hairs
Click and drag on the graph to draw your line
To insert a text box:
Click on the Insert Ta
Click on the Text Box Button
Select Simple Text Box
Drag the text box to place on graph
Alternatively, you may find it easier to draw the graph in the Powerpoint file (Homework_2_Question_2_template.pptx) and copy/paste into this document.
) Suppose that the real money supply increases, other things constant. Using the IS/LM model, demonstrate the impact that this would have on equili
ium real income and the interest rate.
To place lines on the graph:
Click on the Insert Ta
Click on the Shapes Button
Select a straight line (by clicking on it)
This will turn the cursor into cross hairs
Click and drag on the graph to draw your line
To insert a text box:
Click on the Insert Ta
Click on the Text Box Button
Select Simple Text Box
Drag the text box to place on graph
Alternatively, you may find it easier to draw the graph in the Powerpoint file (Homework_2_Question_2_template.pptx) and copy/paste into this document.
Question 3: (20 points): Use dataZoa to build a table with the following monthly data, which is available at the Federal Reserve Bank of St. Louis FRED website (https:
fred.stlouisfed.org/). Include six months of data in the table. FRED variable names are in parentheses. You can search FRED using these variable names.
· U.S. real personal income, seasonally adjusted annual rate (RPI)
· U.S. real disposable personal income, seasonally adjusted annual rate (DSPIC96)
· U.S. total nonfarm employment, seasonally adjusted (PAYEMS)
· U.S. civilian unemployment rate, seasonally adjusted (UNRATE)
· U.S. civilian employment-population ratio, seasonally adjusted (EMRATIO)
· U.S. CPI-U, all items, all cities, seasonally adjusted (CPIAUCSL)
· U.S. CPI-U all items less food and energy, all cities, seasonally adjusted (CPILFESL)
Show the levels for all series and also include year-to-year growth rates for all series except:
· U.S. civilian unemployment rate, seasonally adjusted
· U.S. civilian employment-population ratio, seasonally adjusted
Using a dZboard, share the link to your table here:
Question 4: (30 points)
Download the data on U.S. total nonfarm employment (monthly, seasonally adjusted) that you used in Question 3 to a spreadsheet file. You can download the data either from dataZoa (in a CSV file which can be opened in Excel) or directly from the FRED website (in an Excel file).
Use the observations from September 2010 to the most recent available month, generate forecasts for total nonfarm employment in (a) the next month after the end of the available data, and (b) 3 months ahead, using two techniques:
a) Take first differences and use the recursive sample average technique (Week 1, Forecast Method 1)
a. Hint: forecast the first difference using the recursive sample average technique, then use that to calculate the forecast employment level
) use a linear trend regression via the TREND function in Excel
How similar are the two types of forecasts in this case? Explain why the two methods lead to similar or different results. Would either of these two methods be appropriate for making forecasts for total nonfarm employment with a forecast horizon of two years (in other words, forecasting two years into the future)? Why or why not? Please remember to also submit the Excel spreadsheet you used to create your forecasts.
Page 3 of 4Copyright © Arizona Board of Regents