Regression Estimates, Maximum Likelihood, Ordinary Least Squares
3 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
Lu
le 14 Mai 2011
Réponse apportée : S.Behzad Hassani
le 30 Mai 2016
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
0 commentaires
Réponse acceptée
Oleg Komarov
le 14 Mai 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
Plus de réponses (2)
bym
le 14 Mai 2011
Seems like a simple multiple linear regression. Is e_t a constant?
5 commentaires
Voir également
Catégories
En savoir plus sur Linear and Nonlinear Regression dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!