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Many alternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. We propose and assess a stacked regression ensemble that optimally combines different mortality models to reduce out-of-sample mean squared errors and mitigate model selection risk. Stacked regression uses a meta-learner to approximate horizon-specific weights by minimizing a cross-validation criterion for each forecasting horizon. The horizon-specific weights determine a mortality model combination customized to each horizon. We use 44 populations from the Human Mortality Database to compare the stacked regression ensemble with alternative methods. We show that, using one-year-ahead to 15-year-ahead out-of-sample mean squared errors, the stacked regression ensemble improves mortality forecast accuracy by 13% - 49% for males and 19% - 90% for females over individual mortality models. The stacked regression ensembles also have better predictive accuracy than other model combination methods, including Simple Model Averaging, Bayesian Model Averaging, and Model Confidence Set. We provide an R package, CoMoMo, that combines forecasts for Generalized-Age-Period-Cohort models.

Mortality forecasting using stacked regression ensembles