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26/03/2014, 11:30 — 12:30 — Room P3.10, Mathematics Building

João F. D. Rodrigues, *IN+, Center for Innovation, Technology and Policy Research, Instituto Superior Técnico*

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The Prior Uncertainty and Correlation of Statistical Economic Data

Empirical estimates of source statistical economic data such as
transaction flows, greenhouse gas emissions or employment are
always subject to measurement errors but empirical estimates of
source data errors are often missing. This paper uses concepts from
Bayesian inference and the Maximum Entropy Principle to estimate
the prior probability distribution, uncertainty and correlations of
source data when such information is not explicitly provided. In
the absence of additional information, an isolated datum is
described by a truncated Gaussian distribution, and if an
uncertainty estimate is missing, its prior equals the best guess.
When the sum of a set of disaggregate data is constrained to match
an aggregate datum, it is possible to determine the prior
correlations among disaggregate data. If aggregate uncertainty is
missing, all prior correlations are positive. If aggregate
uncertainty is available, prior correlations can be either all
positive, all negative, or a mix of both. An empirical example is
presented, which reports uncertainty and correlation priors for the
County Business Patterns database.