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A neural network architecture for data editing in the Bank of ItalyÂ’s business surveys

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Author Info

  • Claudia Biancotti

    ()
    (Bank of Italy)

  • Leandro D'Aurizio

    ()
    (Bank of Italy)

  • Raffaele Tartaglia-Polcini

    ()
    (Bank of Italy)

Abstract

This paper presents an application of neural network models to predictive classification for data quality control. Our aim is to identify data affected by measurement error in the Bank of ItalyÂ’s business surveys. We build an architecture consisting of three feed-forward networks for variables related to employment, sales and investment respectively: the networks are trained on input matrices extracted from the error-free final survey database for the 2003 wave, and subjected to stochastic transformations reproducing known error patterns. A binary indicator of unit perturbation is used as the output variable. The networks are trained with the Resilient Propagation learning algorithm. On the training and validation sets, correct predictions occur in about 90 per cent of the records for employment, 94 per cent for sales, and 75 per cent for investment. On independent test sets, the respective quotas average 92, 80 and 70 per cent. On our data, neural networks perform much better as classifiers than logistic regression, one of the most popular competing methods, on our data. They appear to provide a valid means of improving the efficiency of the quality control process and, ultimately, the reliability of survey data.

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Bibliographic Info

Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 612.

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Date of creation: Feb 2007
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Handle: RePEc:bdi:wptemi:td_612_07

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Keywords: data quality; data editing; binary classification; neural networks; measurement error;

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