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  1. Optimal industrial classification
    [an application to the German industrial classification system]
    Autor*in: Chipman, John Somerset; Winker, Peter
    Erschienen: 1994

    A widely used method in the analysis of complex econometric models is to replace the "true model" by an aggregative one in which the variables are grouped and replaced by sums or weighted averages of the variables in each group. The analysis of the... mehr

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 421 (236)
    Fernleihe:
    keine Fernleihe

     

    A widely used method in the analysis of complex econometric models is to replace the "true model" by an aggregative one in which the variables are grouped and replaced by sums or weighted averages of the variables in each group. The analysis of the problem of choosing an aggregative model optimally for modes of aggregation specified in advance leads to a formula for the aggregation bias based on the mean-square forecast error. Taking this formula as objective function one would wish to choose a grouping that minimizes aggregation bias. Unfortunately this results in an optimization problem of a high degree of complexity, which means that there is probably no exact optimization algorithm that works in economic Computing time. In the last few years however, many efficient multiple-purpose optimization heuristics have been developed for complex problems such as the traveling salesman problem, optimal chip layout or optimal portfolio composition. One example of such an algorithm is the Threshold Accepting Algorithm (TA). We implement TA for the optimal aggregation of price indices. The algorithm and the resulting groupings are presented. The results show that the use of Standard or "official" modes of aggregation will in general be far from being optimal.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/101775
    Schlagworte: Aggregation; Mathematische Optimierung; Preisindex; Statistische Methode; Theorie; Schätzung; Deutschland
    Umfang: 35 S., graph. Darst., 30 cm
    Bemerkung(en):

    Literaturverz. S. 31 - 35
  2. Optimal industrial classification with heteroskedasticity correction
    an application to the Swedish industrial classification system
    Autor*in: Chipman, John Somerset; Winker, Peter
    Erschienen: 1994

    Aggregation may be harmful but cannot always be avoided in the analysis of complex econometric models. It should be carried out intelligently by choosing ein aggregative model optimally for modes of aggregation speeified in advance, i.e. minimizing... mehr

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 421 (237)
    Fernleihe:
    keine Fernleihe

     

    Aggregation may be harmful but cannot always be avoided in the analysis of complex econometric models. It should be carried out intelligently by choosing ein aggregative model optimally for modes of aggregation speeified in advance, i.e. minimizing the bias introduced by aggregation and mea-sured by a formula based on the mean-square forecast error. This leads to an integer programming problem of high computational complexity. In this paper the optimization heuristic Threshold Accepting is used to over-come this problem. It is implemented for the optimal aggregation of a long series of Swedish internal and external price indices. The problem of heteroskedasticity due to inflation is tackled by introducing an estimator of the sample covariance matrix in the formula for the mean-square forecast error and employing Euclidean distance; this is compared with results obtained by using an alternative objective funetion based on Mahalanobis distance. The algorithm and the resulting groupings are presented.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/101551
    Schlagworte: Aggregation; Mathematische Optimierung; Preisindex; Statistische Methode; Theorie; Schweden
    Umfang: 23 S., graph. Darst.
    Bemerkung(en):

    Literaturverz. S. 21 - 23