Skip to main content

Combining probability forecasts

Buy Article:

$48.00 plus tax (Refund Policy)

Abstract:

Summary. 

Linear pooling is by far the most popular method for combining probability forecasts. However, any non-trivial weighted average of two or more distinct, calibrated probability forecasts is necessarily uncalibrated and lacks sharpness. In view of this, linear pooling requires recalibration, even in the ideal case in which the individual forecasts are calibrated. Towards this end, we propose a beta-transformed linear opinion pool for the aggregation of probability forecasts from distinct, calibrated or uncalibrated sources. The method fits an optimal non-linearly recalibrated forecast combination, by compositing a beta transform and the traditional linear opinion pool. The technique is illustrated in a simulation example and in a case-study on statistical and National Weather Service probability of precipitation forecasts.

Keywords: Calibration; Coherent combination formula; Forecast aggregation; Linear opinion pool; Model averaging; Probability forecasting; Reliability; Resolution; Sharpness

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1467-9868.2009.00726.x

Affiliations: 1: University of Washington, Seattle, USA 2: Universit├Ąt Heidelberg, Germany

Publication date: January 1, 2010

bpl/rssb/2010/00000072/00000001/art00005
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more