Loss Functions for Detecting Outliers in Panel Data: An Introduction

Charles Coleman ()

MPRA Paper from University Library of Munich, Germany

Abstract: Loss functions are introduced for detecting outliers in panel data. The loss functions for nonnegative data take into account both the size of the base and the relative change. When the data generation processes take a particular form, an exact parametrization is available. The loss functions are extended to variables whose outlier criteria depend on another variable and to data of mixed sign. In the latter case, the geometry dictates one parametrization.

Keywords: Estimates; forecasts; outliers; data quality; panel data (search for similar items in EconPapers)
JEL-codes: C19 C33 C52 (search for similar items in EconPapers)
Date: 2003
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Published in The 13th Federal Forecasters Conference - 2003: Papers and Proceedings 2003 (2003): pp. 265-273

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:77844

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