IDEAS home Printed from
   My bibliography  Save this paper

Optimal Industrial Classification in a Dynamic Model of Price Adjustment


  • John S.nChipman

    () (University of Minnesota)

  • Peter Winker

    () (Universitt Konstanz)


It is common practice in econometrics to base a model to be applied to data on pure theory, and yet to replace the variables of the pure theory by aggregates of them. But if one must aggregate, there are many alternative ways of doing so; we present an approach using heuristic optimization for optimal aggregation. The method is applied to the study of the international transmission of price changes. The basic idea of our approach is easily explained. One wishes to find a partition of industries into a certain number of groups so as to obtain the best possible prediction of the resulting indices of prices of the corresponding commodity groups within a country, given data on the corresponding indices of external prices. The criterion for the optimal prediction is mean-square forecast error, which is to be minimized. The problem of finding a partition of a given number of industries into a smaller number of groups that minimizes mean-square forecast error falls under the heading of integer programming problems. A simple enumeration algorithm is not feasible, since even for modestly problem instances the number of possible groupings is enormous. One way to by-pass this problem is represented by the use of heuristic combinatorial optimization algorithms. We use a refined local-search algorithm similar to the Simulated Annealing approach which is known as Threshold Accepting algorithm (cf. Dueck and Scheuer (1991)).

Suggested Citation

  • John S.nChipman & Peter Winker, "undated". "Optimal Industrial Classification in a Dynamic Model of Price Adjustment," Computing in Economics and Finance 1996 _013, Society for Computational Economics.
  • Handle: RePEc:sce:scecf6:_013

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Thompson, Gary D. & Lyon, Charles C., 1992. "A generalized test for perfect aggregation," Economics Letters, Elsevier, vol. 40(4), pages 389-396, December.
    2. Pesaran, M Hashem & Pierse, Richard G & Kumar, Mohan S, 1989. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," Econometrica, Econometric Society, vol. 57(4), pages 861-888, July.
    3. Winker, Peter, 1992. "Some notes on the computational complexity of optimal aggregation," Discussion Papers, Series II 184, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    4. Cotterman, R & Peracchi, F, 1992. "Classification and Aggregation: An Application to Industrial Classification in CPS Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(1), pages 31-51, Jan.-Marc.
    5. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    6. Edward E. Leamer, 1982. "Optimal Aggegation of Linear Systems," UCLA Economics Working Papers 240, UCLA Department of Economics.
    7. Winker, Peter, 1995. "Identification of multivariate AR-models by threshold accepting," Computational Statistics & Data Analysis, Elsevier, pages 295-307.
    8. Pesaran, M. H. & Pierse, R. G., 1989. "A proof of the asymptotic validity of a test for perfect aggregation," Economics Letters, Elsevier, vol. 30(1), pages 41-47.
    9. Geweke, John, 1985. "Macroeconometric Modeling and the Theory of the Representative Agent," American Economic Review, American Economic Association, vol. 75(2), pages 206-210, May.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sce:scecf6:_013. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.