Q5. GLS and MLE Consider the model where yt = x₁p+ut, t= 1,..., T utput-1+€t, t=...,0,1,2,..., with |p|<1, and {e) is a sequence of i.i.d. disturbances, with E(e)=0, Var(e) = o², vt. a) Explain how the above linear regression could be transformed to make the disturbances i.i.d. (when p is un- known). b) Discuss how p and ß could be estimated in the above model by Generalized Least Square (GLS) method. c) Discuss how you could estimate the above model by Maximum Likelihood Estimation (MLE) method, i.e. write down the log-likelihood function, replace the parameter o2 in the likelihood and derive the first order condi- tions. d) Do you need any additional assumptions in order to apply MLE?
Q5. GLS and MLE Consider the model where yt = x₁p+ut, t= 1,..., T utput-1+€t, t=...,0,1,2,..., with |p|<1, and {e) is a sequence of i.i.d. disturbances, with E(e)=0, Var(e) = o², vt. a) Explain how the above linear regression could be transformed to make the disturbances i.i.d. (when p is un- known). b) Discuss how p and ß could be estimated in the above model by Generalized Least Square (GLS) method. c) Discuss how you could estimate the above model by Maximum Likelihood Estimation (MLE) method, i.e. write down the log-likelihood function, replace the parameter o2 in the likelihood and derive the first order condi- tions. d) Do you need any additional assumptions in order to apply MLE?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:Q5. GLS and MLE
Consider the model
Yt = x;B+ut, t=1,..., T
where
Uf = put-1+€t, t=.,0, 1,2,...,
with |p| < 1, and {e}, is a sequence of i.i.d. disturbances, with
t=1
E(€;) = 0, Var(e;) = o²,vt.
a) Explain how the above linear regression could be transformed to make the disturbances i.i.d. (when p is un-
known).
b) Discuss how p and 6 could be estimated in the above model by Generalized Least Square (GLS) method.
c) Discuss how you could estimate the above model by Maximum Likelihood Estimation (MLE) method, i.e. write
down the log-likelihood function, replace the parameter o? in the likelihood and derive the first order condi-
tions.
d) Do you need any additional assumptions in order to apply MLE ?
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