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Simple versus complex models: Evaluation, accuracy, and combining*

Simple versus complex models: Evaluation, accuracy, and combining* This paper argues that it is premature to decide whether simple forecasting models in demography are more (or less) accurate than complex models and whether causal models are more (or less) accurate than noncausal models. It is also too early to say under what conditions one type of model can outperform another. The paper also questions the wisdom of searching for a single best model or approach. It suggests that combining forecasts may improve accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mathematical Population Studies Taylor & Francis

Simple versus complex models: Evaluation, accuracy, and combining*

Mathematical Population Studies , Volume 5 (3): 10 – Jul 1, 1995

Simple versus complex models: Evaluation, accuracy, and combining*

Mathematical Population Studies , Volume 5 (3): 10 – Jul 1, 1995

Abstract

This paper argues that it is premature to decide whether simple forecasting models in demography are more (or less) accurate than complex models and whether causal models are more (or less) accurate than noncausal models. It is also too early to say under what conditions one type of model can outperform another. The paper also questions the wisdom of searching for a single best model or approach. It suggests that combining forecasts may improve accuracy.

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References (25)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1547-724x
eISSN
0889-8480
DOI
10.1080/08898489509525406
Publisher site
See Article on Publisher Site

Abstract

This paper argues that it is premature to decide whether simple forecasting models in demography are more (or less) accurate than complex models and whether causal models are more (or less) accurate than noncausal models. It is also too early to say under what conditions one type of model can outperform another. The paper also questions the wisdom of searching for a single best model or approach. It suggests that combining forecasts may improve accuracy.

Journal

Mathematical Population StudiesTaylor & Francis

Published: Jul 1, 1995

Keywords: Forecasting models; causal models; simplicity; complexity

There are no references for this article.