Dynamic Factor Analysis in The Presence of Missing Data
AbstractThis paper concerns estimating parameters in a high-dimensional dynamic factormodel by the method of maximum likelihood. To accommodate missing data in theanalysis, we propose a new model representation for the dynamic factor model. Itallows the Kalman filter and related smoothing methods to evaluate the likelihoodfunction and to produce optimal factor estimates in a computationally efficient waywhen missing data is present. The implementation details of our methods for signalextraction and maximum likelihood estimation are discussed. The computational gainsof the new devices are presented based on simulated data sets with varying numbersof missing entries.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 09-010/4.
Date of creation: 12 Feb 2009
Date of revision: 11 Mar 2011
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High-dimensional vector series; Kalman filtering and smooting; Maximum likelihood; Unbalanced panels of time series;
Find related papers by JEL classification:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-02-28 (All new papers)
- NEP-ECM-2009-02-28 (Econometrics)
- NEP-ETS-2009-02-28 (Econometric Time Series)
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