Temporal Disaggregation: Methods, Information Loss, and Diagnostics

Duk B. Jun, Jihwan Moon, Sungho Park

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)53-61
Number of pages9
JournalJournal of Business and Economic Statistics
Issue number1
StatePublished - Jan 2 2016


  • Aggregation effect
  • Disaggregation
  • Information loss function
  • Kalman filter
  • Kalman smoother
  • State–space model

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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