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.

Key words:

• Forecasting models
• causal models
• simplicity
• complexity

Notes

Scott Armstrong and Fred Collopy provided helpful comments.