Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/192277 
Authors: 
Year of Publication: 
2001
Series/Report no.: 
Discussion Papers No. 295
Publisher: 
Statistics Norway, Research Department, Oslo
Abstract: 
The econometric literature offers various modeling approaches for analyzing micro data in combination with time series of aggregate data. This paper discusses the estimation of a VAR model that allows unobserved heterogeneity across observation unit, as well as unobserved time-specific variables. The time-latent component is assumed to consist of a persistent and a transient term. By using a Helmert-type orthogonal transformation of the variables it is demonstrated that the likelihood function can be expressed on a state space form. The dimension of the state vector is low and independent of the time and cross section dimensions. This fact makes it convenient to employ an ECM algorithm for estimating the parameters of the model. An empirical application provides new insight into the problem of making forecasts for aggregate variables based on information from micro data.
Subjects: 
State space models
panel vector autoregressions
random components
latent time series
maximum likelihood
Kalman filter
Helmert transformation
aggregation
prediction.
JEL: 
C13
C15
C33
C53
Document Type: 
Working Paper

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