Regression Estimates, Maximum Likelihood, Ordinary Least Squares

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Hi everyone!
I´m trying to estimate the following model:
r_t=a0+a1*spread_t+a2*depth_t+a3*liquidity+e_t
It is supposed to be really simple (nothing complicated) and I don´t have much knowledge in econometrics, so I don´t really know what model to use. At first I thought I should use Ordinary Least Squares, but then I thought using Maximum Likelihood Estimation because it is supposed to be more efficient. However, I don´t know if this is right. The data set is high frequency data, so I don´t know if that has an impact on the model to choose.
I would really appreciate any help :) or suggestions of what kind of model can I use.
Have a nice weekend!
Lourdes

Accepted Answer

Oleg Komarov
Oleg Komarov on 14 May 2011
When the errors are distributed normally then OLS (easiest) = MLE (numerical solution)
When the variance of the errors change from observation to observation (over time in your case) you have:
If the change is autocorrelated then you have to use arch models:
Which mean at least two courses in undergrad econometrics (OLS and Time Series). A good start could be Introductory econometrics by Wooldridge.
This link could be very useful and easy to understand:
Said that you could find a least squares solution to a system by simply doing:
x = A\b (OLS)
Good luck

More Answers (2)

bym
bym on 14 May 2011
Seems like a simple multiple linear regression. Is e_t a constant?
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S.Behzad Hassani
S.Behzad Hassani on 30 May 2016

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