Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22056
Authors: 
Jung, Robert
Liesenfeld, Roman
Richard, Jean-François
Year of Publication: 
2008
Series/Report no.: 
Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2008,12
Abstract: 
We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated under alternative count distributions (Poisson and negative binomial). Maximum Likelihood estimation requires high-dimensional numerical integration in order to marginalize the joint distribution with respect to the unobserved dynamic factors. We rely upon the Monte-Carlo integration procedure known as Efficient Importance Sampling which produces fast and numerically accurate estimates of the likelihood function. The model is applied to time series data consisting of numbers of trades in 5 minutes intervals for five NYSE stocks from two industrial sectors. The estimated model accounts for all key dynamic and distributional features of the data. We find strong evidence of a common factor which we interpret as reflecting market-wide news. In contrast, sector-specific factors are found to be statistically insignifficant.
Subjects: 
Dynamic latent variables
Importance sampling
Mixture of distribution models
Poisson distribution
Simulated Maximum Likelihood
Document Type: 
Working Paper

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