Imperial College London
Portrait Picture

Emeritus ProfessorNigelMeade

Business School

Emeritus Professor of Quantitative Finance
 
 
 
//

Contact

 

n.meade

 
 
//

Location

 

53 Prince's GateSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Meade:2023:10.1016/j.ijforecast.2022.02.005,
author = {Meade, N and Driver, C},
doi = {10.1016/j.ijforecast.2022.02.005},
journal = {International Journal of Forecasting},
pages = {772--790},
title = {Differing behaviours of forecasters of UK GDP growth},
url = {http://dx.doi.org/10.1016/j.ijforecast.2022.02.005},
volume = {39},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The literature suggests that the dispersion of agents’ forecasts of an event flows from heterogeneity of beliefs and models. Using a data set of fixed event point forecasts of UK GDP growth by a panel of independent forecasters published by HM Treasury, we investigate three questions concerning this dispersion: (a) Are agent’s beliefs randomly distributed or do agents fall into groups with similar beliefs? (b) as agents revise their forecasts, what roles are played by their previous and consensus forecasts? and (c) is an agent’s private information of persistent value? We find that agents fall into four clusters, a large majority, a few pessimists, and two idiosyncratic agents. Our proposed model of forecast revisions shows agents are influenced positively by a change in the consensus forecast and negatively influenced by the previous distance of their forecast from the consensus. We show that the forecasts of a minority of agents significantly lead the consensus.
AU  - Meade,N
AU  - Driver,C
DO  - 10.1016/j.ijforecast.2022.02.005
EP  - 790
PY  - 2023///
SN  - 0169-2070
SP  - 772
TI  - Differing behaviours of forecasters of UK GDP growth
T2  - International Journal of Forecasting
UR  - http://dx.doi.org/10.1016/j.ijforecast.2022.02.005
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000984036800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://doi.org/10.1016/j.ijforecast.2022.02.005
UR  - http://hdl.handle.net/10044/1/107707
VL  - 39
ER  -
Download