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Temporal Disaggregation, Missing Observations, Outliers, and Forecasting: A Unifying Non-Model Based Procedures

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Author Info
Marcellino, M.

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Abstract

We suggest a simple non model based procedure to recover a time series from its temporally aggregated realizations. If additional assumptions on the under lying process are intorduced, it is shown that the procedure is related to many of the former proposals in the literature. It can also be easily modified to deal with the estimation of missing observations and outliers, and with forecasting. Some important identification issues are finally discussed.

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Publisher Info
Paper provided by European University Institute in its series Economics Working Papers with number eco97/30.

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Length: 29 pages
Date of creation: 1997
Date of revision:
Handle: RePEc:eui:euiwps:eco97/30

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Related research
Keywords: TIME SERIES ; MODELS;

Find related papers by JEL classification:
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data

Cited by:
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  1. Olivier Darne, 2008. "Using business survey in industrial and services sector to nowcast GDP growth:The French case," Economics Bulletin, Economics Bulletin, vol. 3(32), pages 1-8. [Downloadable!]
  2. Ruist, Erik, 1996. "Temporal Aggregation of an Econometric Equation," Working Paper 52, National Institute of Economic Research. [Downloadable!]
  3. Jorda, Oscar & Marcellino, Massimiliano, 2000. "Stochastic Processes Subject to Time Scale Transformations: An Application to High-Frequency FX Data," Working Papers 00-2, University of California at Davis, Department of Economics. [Downloadable!]
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