Some economic series in small economies exhibit meagre (i.e. non-positive) values, as well as seasonal extremes. For example, agricultural variables in countries with a distinct growing season may exhibit both of these features. Multiplicative seasonal adjustment typically utilises a logarithmic transformation, but the meagre values make this impossible, while the extremes engender huge distortions that render seasonal adjustments unacceptable. To account for these features, we propose a new method of extreme-value adjustment based on the maximum entropy principle, which results in replacement of the meagre values and extremes by optimal projections that utilise information from the available time series dynamics. This facilitates multiplicative seasonal adjustment. The method is illustrated in the New Zealand agricultural series.

Others in Series

Working Paper

Recurvsive Computation for Block-Nested Covariance Matrices

2017

Covariance matrices corresponding to samples of multivariate time series or spatial random fields have a block-Toeplitz structure that has a nested pattern.

Working Paper

Seasonal Adjustment Subject to Accounting Constraints

2018

Proposes utilizing adequacy of (disaggregate) component seasonal adjustments, modified by reconciliation, as an additional constraint to the accounting problem.

Working Paper

Counterexamples to Parsimony and BIC

April 01, 1991

This note presents examples of nested and non-nested regression model pairs for which the likelihood-ratio sequence is bounded in probability.

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