The Common Principal Component (CPC) Approach to Functional time Series (FTS) Models

Abstract

The functional time series (FTS) models are used for analyzing, modeling and forecasting age-specific mortality rates. However, the application of these models in presence of two or more groups within similar populations needs some modification. In these cases, it is desirable for the disaggregated forecasts to be coherent with the overall forecast. The 'coherent' forecasts are the non-divergent forecasts of sub-groups within a population. Reference [1] first proposed a coherent functional model based on product and ratios of mortality rates. In this paper, we relate some of the functional time series models to the common principal components (CPC) and partial common principal components (PCPC) models introduced by [2] and provide the methods to estimate these models. We call them common functional principal component (CFPC) models and use them for coherent mortality forecasting. Here, we propose a sequential procedure based on Johansen methodology to estimate the model parameters. We use vector approach and make use of error correction models to forecast the specific time series coefficient for each sub-group.