Ees601: Quantitative Methods Ii Question Paper
Ees601: Quantitative Methods Ii
Course:Ph.D In Economics
Institution: Kenyatta University question papers
Exam Year:2012
KENYATTA UNIVERSITY
UNIVERSITY EXAMINATIONS 2011/2012
SECOND SEMESTER EXAMINATION FOR THE DEGREE OF DOCTOR
OF PHILOSOPHY IN ECONOMICS
EES 601:
QUANTITATIVE METHODS II
===============================================================
DATE: FRIDAY 30TH MARCH 2012
TIME: 2.00 P.M. – 5.00 P.M.
INSTRUCTIONS
Attempt ALL questions
All questions carry equal marks
Question 1
You are interested in explaining how Income (Y) influences the choice of health care
delivery. If there are three options as follows; self medication, Hospital and traditional
healer.
i)
Using a logit model demonstrate how the coefficients can be interpreted.
ii)
Assuming that the choices are collapsed to only two and a logit model yield in
In Q = 0.02 + 0.03 Y. Show that 0.03 is not a marginal effect
iii)
Assuming a scenario where in (ii) the probability of attending a formal health
care delivery is 0.6, how much a change in income is likely to influence choice of
health care delivery.
Question 2
a)
Given a timeseries model
Y ? X B ? u where u ~ IID ,
0
[
2
? ?), , ? ? ?
t
1
t
t
i)
Explain the effect of using OLS method in estimating the equation
Page 1 of 2
ii)
If the population variance is known, demonstrate that premultiplication by a
matrix P can solve the problem
iii)
Show this particular matrix P.
b)
Y ? ? B
? ? u where X and Y are matrices of endogenous and exogenous variables
respectively , demonstrate what would happen if someone was to run a regression
using CLS method directly.
Question 3
a)
Given a timeseries model with variables Y and X where both Yt and Xt are ?(o)
i)
Derive an impulse responses model and show this implication
ii)
Explain the concept of Granger causality and show how it can be tested.
iv)
Explain the distinction between Exogeneity and Granger causality
b)
y ? ? ? ? X ? u
t
o
1
t
T
Show DF test for unit root.
Question 4
a)
Given y ? ? ? X B ? u
i t
i
i t
i t
Compute the variance of y
Under
a)
Fixed effect
b)
Random effect
b)
i)
Explain the principle of Maximum – likelihood estimation
ii)
If Y ~ N(Y , 2
? u] show how you can use MLE to estimate all the unknown
parameters
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