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    <title>Homelen, P. van</title>
    <link>http://repub.eur.nl/res/aut/9109/</link>
    <description>List of Publications</description>
    <language>en</language>
    <image>
      <url>http://repub.eur.nl/static-eur/img/logo.png</url>
      <title>RePub, Erasmus University Rotterdam</title>
      <link>http://repub.eur.nl</link>
    </image>
    <item>
      <title>Modeling item nonresponse in questionnaires (Article)</title>
      <link>http://repub.eur.nl/res/pub/2154/</link>
      <pubDate>1999-01-01T00:00:00Z</pubDate>
      <description>The statistical analysis of empirical questionnaire data can be hampered by the fact that not all questions are answered by all individuals. In this paper we propose a simple practical method to deal with such item nonresponse in case of ordinal questionnaire data, where we assume that item nonresponse is caused by an incomplete set of answers between which the individuals are supposed to choose. Our statistical method is based on extending the ordinal regression model with an additional category for nonresponse, and on investigating whether this extended model describes and forecasts the data well. We illustrate our approach for two questions from a questionnaire held amongst a sample of clients of a financial investment company.</description>
    </item> <item>
      <title>On forecasting excchange rates using neural networks (Article)</title>
      <link>http://repub.eur.nl/res/pub/2173/</link>
      <pubDate>1998-12-01T00:00:00Z</pubDate>
      <description>The paper considers the modelling, description and forecasting of four daily exchange rate returns relative to the Dutch guilder using artificial neural network models (ANNs). Based on simulations it is argued (i) that neglected GARCH does not lead to spuriously successful ANNs and (ii) that if there is some form of nonlinearity other than GARCH, ANNs will exploit this for improved forecasting. For the sample data it is found that ANNs do not yield favourable in-sample fits or forecasting performance. These results are interpreted as indicating that the nonlinearity often found in exchange rates is most likely due to GARCH and therefore ANNs are recommended as a diagnostic for mean nonlinearity.</description>
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