Omestimering

Omestimering is a term used in Norwegian that refers to the process of re-estimating or updating a previous estimate when new information becomes available or when underlying assumptions change. It describes revising parameter values, forecasts, or other quantities to reflect the current data and conditions, with the aim of improving accuracy and relevance.

In practice, omestimering occurs across fields such as statistics, econometrics, finance, and actuarial science. It can involve re-fitting a model with additional data, changing the estimation method, or applying Bayesian updating to incorporate new evidence. In finance and risk management, re-estimation is common when market conditions shift or when new experience data arise, necessitating revised pricing, risk assessments, or reserve calculations.

Common methods used for omestimering include sequential or rolling-window estimation, where data windows are continually updated, and techniques like Kalman filtering or Bayesian updating, which formally incorporate new information into prior estimates. The choice of method depends on factors such as data availability, model structure, and the acceptable balance between responsiveness and stability of estimates.

Challenges in omestimering include data revisions, structural breaks in the system being modeled, and model misspecification. Re-estimation can improve accuracy but may also reduce stability if performed too frequently or on poorly quality data. Best practices emphasize model monitoring, out-of-sample validation, and transparent documentation of when and why estimates are updated.

See also: recalibration, re-estimation, model updating, Bayesian updating.