Assignment 2
125.785
Now due Thursday noon, 13 April.
1. Simultaneous Equations
The file loans.wf1 has data on the commercial loans made to businesses in the US.
The data is from the period 1979-1984.
The variables are:
Q- total commercial loans (billions of dollars)
R- average prime rate charged by banks
RS- 3-month Treasury Bills to represent an alternative investment for banks.
RD- AAA corporate bond rate to represent an alternative source of financing for the
firms
X- industrial production index, representing firm’s expectations about future
economic conditions.
Y- total bank deposits, representing a simple scale factor.
(a) The supply of loans is a function of R, RS and Y. The demand for loans is a
function of R, RD and X.
Write out the structural equations for this market.
(b) Estimate the structural equations using OLS regression.
(c) Estimate the structural equations using TSLS regression.
(d) Compare and contrast your answer. Which model do you prefer and why?
2. (From Chapter 7, Q11 of Studenmund)
Suggest the appropriate functional forms for the relationships between the following
variables. Be sure to explain your reasoning. Use sketch diagrams if you think this
will help.
a. The age of the ith house in a cross-sectional equation for the sales price of houses in
Cooperstown, New York. (NB: Cooperstown is known as a lovely town with a
number of elegant historic homes).
b. The price of natural gas in year t in a demands-de time-series equation for the
consumption of natural gas in the United States.
c. The income of the ith individual in a cross-sectional equation for the number of
suits owned by an individual.
d. A dummy variable for being a student (1=yes) in the equation specified in part c.
e. The number of long distance telephone calls handled per year in a cross-sectional
equation for the marginal cost of a telephone call faced by various competing longdistance telephone carriers.
3. The file mining.wf1 has 111 observations. These are seasonally adjusted quarterly
observations on the indices of mining production (pro) and electric power use for
mining (pow). The data set also includes the variable t, a time-trend variable.
(a) Estimate an OLS model of the data-set, where the log of pow is a function of the
log of pro and a time trend, t.
(b) Use the Lagrange Multiplier test to see if the time trend is a quadratic. If you
confirm the presence of a quadratic time trend, include this in your regression model.
(c) Use the Durbin-Watson and LM test for serial correlation to detect order 1 serial
correlation. Use the LM test to try and diagnose order 2 serial correlation.
(d) If you detect serial correlation, use the GLS AR method to estimate your model.
(e) Conduct t-tests on the coefficient of log(pro) to see if it equals 1. Compare the
results of your test before and after correcting for serial correlation.