Large dynamic factor models, forecasting, and nowcasting in Statsmodels

statespace_large_dynamic_factor_models.ipynb

Thank you for your response. I apologize for the lack of clarity in my question. I have monthly information on a set of indicators. These indicators have 1, 2, and up to 3 months of lag. I want to obtain the latent factor of this information set. How does the model estimate the value of the factor in April if many indicators have missing data for that month and previous months? How does the model handle those missing values?

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

I'll see the paper! Thanks!