Given the data in GDPandProductivity.xls we would like to measure the impact of productivity in manufacturing, and total productivity on GDP.
economics
Description
1.
Given
the data in GDPandProductivity.xls we
would like to measure the impact of productivity in manufacturing, and total
productivity on GDP.
GDP = Gross Domestic Product billions of dollars
Prod.Manuf.: Productivity of Manufacturing, index
2012=100
Prod.Bus.: Productivity of all Businesses, index
2012=100
a) Deseasonalize
the three series
b) Using
the deseasonalized series, detrend each of these series
c) Run a
regression using GDP as the dependent variable and both productivity indexes as
the independent ones.
d) Test
for autocorrelation and correct the model if necessary.
e) Comment
the results you obtained
2.
Given the data in Cons.xls we would like to calculate the
following model:
C=b0+b1Y
Where
C is consumption and Y is total Income.
a) Use the White and the Breusch-Pagan
tests for heteroscedasticity. Show your results.
b) If you found
heteroscedasticity assume that the variance of the error term is proportional
to the explanatory variable. Solve the model and show the results.
c) Now, assume that you do not
know the type of variance for the error term. Run the econometric model using
different, unknown, variances for the error term.
3.
Points)
Given the data in Copper.xls we would
like to estimate a model for the determinants of the price of Copper in the
U.S. from 1951 to 1980.
where: C= 12-month average U.S. domestic price of copper
(cents per pound)
G=annual
gross national product ($, billions)
I=12-month
average index of industrial production
L=12-month
average London Metal Exchange price of Copper (pounds sterling)
H=number
of housing starts per year (thousands of units)
A=12-month
average price of aluminum (cents per pound)
a) Run
this model (be careful of the specification of the model).
b) Use
the Durbin-Watson statistic and the Breusch-Godfrey tests to test for
autocorrelation. Show your results.
c) Assume
that the error terms follow an AR(1) process. Run a regression taking care of
this AR(1) process. Show the results.
