TY  - CHAP
AB  - In this paper we consider a recently developed non parametric econometric method which is ideally suited to a wide range of marketing applications. We demonstrate the usefulness of this method via an application to direct marketing using data obtained from the Direct Marketing Association. Using independent hold-out data, the benchmark parametric model (Logit) correctly predicts 8% of purchases by those who actually make a purchase, while the nonparametric method correctly predicts 39% of purchases. A variety of competing estimators are considered, with the next best models being semiparametric index and Neural Network models both of which turn in 36% correct prediction rates.
VL  - 16
SN  - 978-1-84950-142-2, 978-0-76230-857-6/0731-9053
DO  - 10.1016/S0731-9053(02)16007-5
UR  - https://doi.org/10.1016/S0731-9053(02)16007-5
AU  - Jeffrey Racine S.
PY  - 2002
Y1  - 2002/01/01
TI  - ‘New and improved’ direct marketing: A non-parametric approach
T2  - Advances in Econometrics
T3  - Advances in Econometrics
PB  - Emerald Group Publishing Limited
SP  - 141
EP  - 164
Y2  - 2024/04/24
ER  -