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Factor models have become an important tool in the modern forecaster’s toolkit. They provide a means of producing forecasts when the number of indicator series, N , exceeds the number of time series observations, T . A further perceived benefit is that they relieve the pressure on the forecaster to select the preferred indicator(s), and in turn indicator-based forecasting model, from a potentially large set.1 Various different factor models have been proposed, from principal components to parametric dynamic factor models and spectral density methods, with the applied literature tending to conclude that factor models are helpful for the production of short-term point forecasts. Brauning and Koopman (2014, BK henceforth) build on this now considerable body of literature (recently reviewed by Stock & Watson, 2011) by proposing a new dynamic factor model – the Collapsed (Dynamic) Factor Model (CFM) – for short-term forecasting. This includes nowcasting, where the distinguishing feature is the use of mixed-frequency data and jagged or ragged edges. BK’s proposed model (see their Eq. (9)) can be seen as a generalisation of the widely used Stock-Watson approach, which augments the forecasting model with principal components. This is because BK’s model accommodates measurement error by treating the principal components as noisy estimates of the true factors. However, principal |
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