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{Driver:2019:10.1002/for.2567,
author = {Driver, C and Meade, N},
doi = {10.1002/for.2567},
journal = {Journal of Forecasting},
pages = {236--255},
title = {Enhancing survey-based investment forecasts},
url = {http://dx.doi.org/10.1002/for.2567},
volume = {38},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.
AU  - Driver,C
AU  - Meade,N
DO  - 10.1002/for.2567
EP  - 255
PY  - 2019///
SN  - 0277-6693
SP  - 236
TI  - Enhancing survey-based investment forecasts
T2  - Journal of Forecasting
UR  - http://dx.doi.org/10.1002/for.2567
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000461059200006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://onlinelibrary.wiley.com/doi/10.1002/for.2567
UR  - http://hdl.handle.net/10044/1/107713
VL  - 38
ER  -
Download