• We are pleased to announce that the winner of our Feedback Prize Draw for the Winter 2024-25 session and winning £150 of gift vouchers is Zhao Liang Tay. Congratulations to Zhao Liang. If you fancy winning £150 worth of gift vouchers (from a major UK store) for the Summer 2025 exam sitting for just a few minutes of your time throughout the session, please see our website at https://www.acted.co.uk/further-info.html?pat=feedback#feedback-prize for more information on how you can make sure your name is included in the draw at the end of the session.
  • Please be advised that the SP1, SP5 and SP7 X1 deadline is the 14th July and not the 17th June as first stated. Please accept out apologies for any confusion caused.

definition of prediction error

D

DanielZ

Member
Prediction error is defined as process error + parameter error.
Why does this not include model error?

EDIT: I guess you could say this just comes down to a matter of definition, i.e. prediction error is the inherent uncertainty of a prediction given a specific choice of model.

Thanks
 
Last edited by a moderator:
What about q&A 4.18i

This question asked to explain the components of the variances of stochastic reserve predictions.

  • I argued that firstly there is model error. This can be measured by developing a range of best estimates from different models. Why is this incorrect?
  • The model solutions list estimation variance that reflects in perfections derived from the model due to a number of reasons: one of which being the choice of model itself. Isn't this contradictory to the subdivision we make in the notes? Isn't estimation error simply the error derived from choosing incorrect assumptions/parameters?
 
Have a re-read of Section 3.1 of Chapter 14.

There is sometimes some ambiguity about how these terms are used.

In General:

Prediction variance = Estimation variance + Process variance

Where Estimation variance can be further sub-divided between model error and parameter error.
 
Back
Top