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Abstract:
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A methodology for estimating high-frequency values of an unobserved multivariate time series from low-frequency values of and related information to it is presented in this paper. This is an optimal solution, in the multivariate setting, to the problem of ex post prediction, disaggregation, benchmarking, or signal extraction of an unobservable stochastic process. Also, the problem of extrapolation or ex ante prediction is optimally solved and, in this context, statistical tests are developed for checking on-line extreme values of the unobserved time series and consistency of future benchmarks with the present and past observed information. The procedure is based on structural or component models, whose assumptions and specification are validated with the data alone.
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