2

ECON 6313-001

Fall Semester, 2013
Exam 2

Professor C. Brown

The data (in Excel format) you will need to complete this examination can be found at
http://www.clt.astate.edu/crbrown/6313.html

Answer all questions. The Exam is due on Wednesday, October 23.

  1. This question is worth 35 points—3 points each, except for subparts (a) and (j), which are worth 5 ½ points each. You will need to download data for question 1 from the course web page (see URL above) to complete this question. This file contains 76 quarterly observations (1992-I to 2010-IV) on the following variables:
    TSUV: Sales of new light passenger trucks and sport utility vehicles (SUVs) in the U.S.(, annual rate, in millions of dollars).
    U: Unemployment rate, civilian labor force, in the U.S. (percent)
    IR: Average rate of interest charged on new car loans by finance companies (in percentage points)
    SP500:Standard and Poor’s Index of 500 Stocks ,quarterly opening.

a)  Use regression to estimate the following model specification. Report the results of the regression—that is, report your estimates of β0 , β1 , β2, and β3.

b)  Are the signs of the (estimated) coefficients consistent with your (prior) expectations? Explain.

c)  The U.S. unemployment rate rose to from 6.0 percent in 2007-IV to 9.3 percent in2008-III. What is the predicted effect on truck and SUV (TSUV), other things being equal?

d)  If central bank policy were successful in shaving 1 ½ percentage points from the auto loan rate (IR), what impact on truck and SUV sales should be expected (based on your estimates), other things being equal?

e)  Report the t-statistic for the estimate of the SP500 () parameter. Set up a null and alternative hypothesis for this coefficient. Can you reject the null hypothesis at the .01 (1 percent) confidence level? Briefly Explain.

f)  Use the equation you estimated above to obtain a fitted value of TSUV for 2001-IV . Is the actual value of TSUV within one standard error of its fitted value for this quarter? Explain.

g)  Prepare a chart illustrating actual and fitted values of TSUV for the period 1992-I to 2010I-IV .

h)  Report the value of R2 and provide a (precise) interpretation.

i)  Set up an F-test. Can you reject null hypothesis at the 5 percent (.01) confidence level?

j)  Use the data contained in “sheet 2” of your spreadsheet to forecast the value of TSUV sales for 2013-IV and 2014-I. Report your results.

2.  This question is worth 35 points—3 points for each part except for subparts (e) and (j), which are worth 5 ½ points each. You will need to download data for question 2 from the website (see URL above) to complete this question. You have 175 monthly values of retail sales of women’s clothing (WCS) in the U.S. beginning in January 1999 and running through July 2013 (in millions of dollars, not seasonally adjusted).

a)  Forecast beer, wine, and liquor sales for October 2013 using a 4-month prior moving average technique.

b)  Compute root mean square error () for the in-sample forecast using the same technique as in part (a) above.

c)  Find the 2-decimal point smoothing constant () which gives the best fit for (based on the criterion) for the in-sample forecast using exponential smoothing technique. (Note: the “damping factor” in Excel is equal to).

d)  How does for the exponential smoothing technique compare to its value for the 4-period moving average technique?

e)  Forecast retail sales of women’s clothing (WCS) sales in October 2013 using the exponential smoothing technique (using the smoothing constant you found in part (c)).

f)  Estimate and report a linear trend component for the WCS time series using the ordinary least squares (OLS) technique.

g)  Compare the trend value of your series for February 2009 with its actual value in that month. What factors might account for the difference between the trend value and the actual value of WCS for February 2009?

h)  Compute a seasonal index using a 12 month centered moving average of the WCS series. What are the best months in the women’s clothing retailing business? What are the worst months?

i)  Do an in-sample forecast on CWL sales using the multiplicative time series technique (assume the cyclical components is equal to 1).

j)  Use the information contained in following table to perform a forecast of WCS sales for November and December 2009 using the multiplicative time series technique (Note: you will need to compute a trend component for these months using the equation you obtained in part (f)).

Month / Trend
Component / Seasonal
Component / Cyclical
Component / Forecast
(in millions of US $)
Nov-13 / ? / Use Nov-11 value / 0.944
Dec-13 / ? / Use Dec 11 value / 0.971

3.  This question is worth 30 points. Answer the following questions.

  1. See the diagram above that depicts the demand for non-diet soft drinks. Assume the current price of soft drinks is $3.58 per 12-pack. Compute point elasticity of demand at the current price. Are soft drink companies maximizing profits from the sale of soft drinks? Explain.
  2. Soft drink consumption is blamed for health problems such as childhood obesity and diabetes. Several states (including Arkansas) impose a soft drink tax, justified by public health concerns. Suppose a soft drink tax is imposed that raises the price of soft drinks (from the current price of $3.50) by 8 percent. How successful would the tax be in reducing soft drink consumption? Would it make a difference if the pre-tax price were $4.89 per 12-pack?
  3. Which figures stated below is likely to represent each of the following. Give the reasons for your choice in each case.
  4. Income elasticity of demand for low price cuts of meat;
  5. Income elasticity of demand for Apple iPads;
  6. Price elasticity of demand for gasoline
    -1.6 -0.1 +4.3
  7. When the price of Good X is $27, the quantity-demanded of Good Y is 1,200 units. When the price of Good X falls to $23 (the price of good Y unchanged), the quantity-demanded of Good Y falls to 800 units. Compute cross-price elasticity of demand between Goods X and Y. Are Goods X and Y substitutes, or complements? Explain.