Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity

Over the course of several decades, extensive research has been dedicated to Market Response models, often lacking validation in purely predictive tasks and frequently overlooking the adherence of methods to underlying assumptions and conditions, such as the capacity to delineate widely accepted eff...

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Detalles Bibliográficos
Autores: Martínez, Andres, Salafranca, Alfonso, Sipols, Ana E., Simón de Blas, Clara, Van Hengel, Daniel
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/28339
Acceso en línea:https://hdl.handle.net/10115/28339
Access Level:acceso abierto
Palabra clave:Distributed Lag Model
Advertising
Lagged effects
Machine learning
Prediction
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spelling Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacityMartínez, AndresSalafranca, AlfonsoSipols, Ana E.Simón de Blas, ClaraVan Hengel, DanielDistributed Lag ModelAdvertisingLagged effectsMachine learningPredictionOver the course of several decades, extensive research has been dedicated to Market Response models, often lacking validation in purely predictive tasks and frequently overlooking the adherence of methods to underlying assumptions and conditions, such as the capacity to delineate widely accepted effects of advertising actions. This study introduces an improved method for market response models that aligns with these underlying assumptions. The proposed method is grounded in Distributed Lag Models and distinguishes itself by incorporating regularization in its estimation, employing a cross-validation framework, and implementing hold-out testing. Additionally, it presents an empirical approach to extracting the effects of the model. This methodology facilitates the construction of models in an exploratory and straightforward manner, thereby unlocking the potential to uncover underlying effects and proving suitable for extensive samples and numerous variables. To illustrate its practical application, a real-world data example is provided, accompanied by an unprecedented set of empirical explainability assessments and a high level of predictive capability under circumstances similar to those encountered in corporate decision-making processes.For many decades, considerable research has been conducted on Market Response models. Mostly without any attempts to validate the results in strictly predictive tasks and often ignoring if the methods comply with the underlying assumptions and conditions, like the method’s ability to outline the broadly accepted effects of advertising actions. This work presents an enhanced method for market response models consistent with the underlying assumptions of such. Our method is based on Distributed Lag Models with the novelty of introducing regularization in its estimation, a cross-validation framework, and hold-out testing, next to present an empirical manner of extracting its effects. This approach allows the construction of models in an exploratory and simple manner, unlocking the possibility of extracting the underlying effects and being suitable for large samples and many variables. Last, we conduct a practical example using real-world data, accompanied by an unprecedented set of empirical explainability assessments next to a high level of predictive capability in similar circumstances to how it would be used for decision-making in a corporate setup.palgrave macmillan202420242022info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/28339reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésinfo:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/283392026-06-24T12:48:17Z
dc.title.none.fl_str_mv Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
title Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
spellingShingle Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
Martínez, Andres
Distributed Lag Model
Advertising
Lagged effects
Machine learning
Prediction
title_short Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
title_full Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
title_fullStr Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
title_full_unstemmed Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
title_sort Distributed lags using elastic‑net regularization for market response models: focus on predictive and explanatory capacity
dc.creator.none.fl_str_mv Martínez, Andres
Salafranca, Alfonso
Sipols, Ana E.
Simón de Blas, Clara
Van Hengel, Daniel
author Martínez, Andres
author_facet Martínez, Andres
Salafranca, Alfonso
Sipols, Ana E.
Simón de Blas, Clara
Van Hengel, Daniel
author_role author
author2 Salafranca, Alfonso
Sipols, Ana E.
Simón de Blas, Clara
Van Hengel, Daniel
author2_role author
author
author
author
dc.subject.none.fl_str_mv Distributed Lag Model
Advertising
Lagged effects
Machine learning
Prediction
topic Distributed Lag Model
Advertising
Lagged effects
Machine learning
Prediction
description Over the course of several decades, extensive research has been dedicated to Market Response models, often lacking validation in purely predictive tasks and frequently overlooking the adherence of methods to underlying assumptions and conditions, such as the capacity to delineate widely accepted effects of advertising actions. This study introduces an improved method for market response models that aligns with these underlying assumptions. The proposed method is grounded in Distributed Lag Models and distinguishes itself by incorporating regularization in its estimation, employing a cross-validation framework, and implementing hold-out testing. Additionally, it presents an empirical approach to extracting the effects of the model. This methodology facilitates the construction of models in an exploratory and straightforward manner, thereby unlocking the potential to uncover underlying effects and proving suitable for extensive samples and numerous variables. To illustrate its practical application, a real-world data example is provided, accompanied by an unprecedented set of empirical explainability assessments and a high level of predictive capability under circumstances similar to those encountered in corporate decision-making processes.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/28339
url https://hdl.handle.net/10115/28339
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv palgrave macmillan
publisher.none.fl_str_mv palgrave macmillan
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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