- Define your log regression model as you have mentioned.
- specify the log likelihood function assuming the error in normal as log(normpdf(ln(y), alpha + Beta*ln(x), sigma))
- Specify the prior distribution using ‘bayeslm’ function.
- Run the maximum likelihood to estimate the parameters.
Estimate Linear regression, then estimate normal learning model and see how parameters update over time
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I want to estmate a log linearized regression (ln(y) = alpha + Beta*ln(x) +e), and then see how particular parameters (alpha, Beta) update over time given observations via a normal bayesian learning model. I am new to normal learning models, and use matlab infrequently.
Do I need to run maximum likelihod on a log likelihood function then run 'bayeslm', or do I run 'bayeslm/empiricallm' and then 'estimate' for the posterior?
Additionally, do I set up a log likelihood, prior, and then estimate, or just the log likelihood and then define the functions?
I have read around some of the mathworks documents, but would like verification for this process before proceeding. Thank you!
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Answers (1)
Balaji
on 22 Sep 2023
Hi Joshua
I understand that you want to estimate a log-linearized regression model using a normal Bayesian learning approach in MATLAB. For this you can
For more information on ‘bayeslm’ function I suggest you refer to :
Hope this helps
Thanks
Balaji
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