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Large dynamic factor models, forecasting, and nowcasting in Statsmodels
I'll see the paper! Thanks!
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Because it is a state space model, where the unobserved state has a defined transition equation, it can produce an estimate for the factor in April even if you had no data for the month (i.e. it just estimates April using its estimate for March combined with the definition of how the state transitions between periods). As you start to observe parts of the data for April, it updates its estimate using whatever data is available. A more detailed description of how this works can be found in, e.g., Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data