IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Evolutionary Strategies vs. Neural Networks; New Evidence from Taiwan on the Divisia Index Debate

Listed author(s):
  • Graham Kendall, Jane Binner and Alicia Gazely
Registered author(s):

    In recent years the relationship between ÎmoneyÌ and the macroeconomy has assumed prominence in the academic literature and in Central Banks circles. Although some Central Bankers have stated that they have formally abandoned the notion of using monetary aggregates as indicators of the impact of their policies on the economy, research into the link between some kind of monetary aggregate and the price level is still prevalent. Attention is increasingly turning to the method of aggregation employed in the construction of monetary indices. The most sophisticated index number used thus far relies upon the formulation devised by Divisia. The construction has it roots firmly based in microeconomic aggregation theory and statistical index number theory. Our hypothesis is that measures of money constructed using the Divisia index number formulation are superior indicators of monetary conditions when compared to their simple sum counterparts. Our hypothesis is reinforced by a growing body of evidence from empirical studies around the world which demonstrate that weighted index number measures may be able to overcome the drawbacks of the simple sum, provided the underlying economic weak separability and linear homogeneity assumptions are satisfied. Ultimately, such evidence could reinstate monetary targeting as an acceptable method of macroeconomic control, including price regulation. We offer an exploratory study of the relevance of the Divisia monetary aggregate for Taiwan over the period 1978 to date. We adopt the principles of Ford (1997) by allowing both for a period of gradual learning by individuals as they adapt to the financial changes and secondly by incorporating a mechanism to accommodate the changing perceptions of individuals to the increased productivity of money. Individuals are thus assumed to adjust their holdings of financial assets until the diffusion of financial liberalisation is complete. The novelty of this paper lies in the use of evolutionary strategies (ES) to examine TaiwanÌs recent experience of inflation. This is an unusual tool in this context and represents the first known application of its kind. Results are compared to those already produced for Taiwan using the Artificial Intelligence technique of neural networks to compare the explanatory power of both Divisia and simple sum measures of broad money as indicators of inflation. Evidence suggests that ESÌs compete favourably in small samples with the neural network results we have previously reported, with the added benefit that the architecture is simpler and thus easier to implement. In addition, the variety of parameters is reduced and thus further limits the decisions that have to be made when implementing the algorithm. Our preferred inflation forecasting model is achieved using networks that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. The paper concludes with a discussion of the promise of artificial intelligence techniques as new tools in the macroeconomic policy arena.

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below under "Related research" whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 176.

    in new window

    Date of creation: 01 Apr 2001
    Handle: RePEc:sce:scecf1:176
    Contact details of provider: Web page:

    More information through EDIRC

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:sce:scecf1:176. 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)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.