Dynamic Factor Analysis in The Presence of Missing Data
2009-02-06
Research Paper
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We develop a new model representation for high-dimensional dynamic multi-factor models. It allows the Kalman filter and related smoothing methods to produce optimal estimates in a computationally efficient way in the presence of missing data. We discuss the model in detail together with the implementation of methods for signal extraction and parameter estimation. The computational gains of the new devices are presented based on simulated data-sets with varying numbers of missing entries
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Automatically Extracted Terms
- vector
- model
- state
- formulation
- kalman filter
- factor
- section
- space
- state vector
- observation
- entry
- kalman
- factor model
- method
- matrix
- likelihood
- filter
- dimension
- mt +1
- state vector t
