Bai, Jushan and Wang, Peng (2012): Identification and estimation of dynamic factor models.

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Abstract
We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations, and require fewer number of restrictions than when putting in a staticfactor form. Under these restrictions, a standard structural vector autoregression (SVAR) with or without measurement errors can be embedded into a dynamic factor model. More generally, we also consider overidentification restrictions to achieve efficiency. General linear restrictions, either in the form of known factor loadings or crossequation restrictions, are considered. We further consider serially correlated idiosyncratic errors with heterogeneous coefficients. A numerically stable Bayesian algorithm for the dynamic factor model with general parameter restrictions is constructed for estimation and inference. A squareroot form of Kalman filter is shown to improve robustness and accuracy when sampling the latent factors. Confidence intervals (bands) for the parameters of interest such as impulse responses are readily computed. Similar identification conditions are also exploited for multilevel factor models, and they allow us to study the spillover effects of the shocks arising from one group to another.
Item Type:  MPRA Paper 

Original Title:  Identification and estimation of dynamic factor models 
Language:  English 
Keywords:  dynamic factor models; multilevel factor models; impulse response function; spillover effects 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C10  General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C33  Models with Panel Data; Longitudinal Data; Spatial Time Series C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  38434 
Depositing User:  Peng Wang 
Date Deposited:  30. Apr 2012 01:28 
Last Modified:  12. Feb 2013 14:38 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/38434 