This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Marek Hlavacek
Michael Konak
Josef Cada

Additional information is available for the following registered author(s):

Abstract

One of the most significant factors influencing the liquidity of the financial market is the amount of currency in circulation. Although the central bank is responsible for the distribution of the currency it cannot assess the demand for the currency, as that demand is influenced by the non-banking sector. Therefore, the amount of currency in circulation has to be forecasted. This paper introduces a feedforward structured neural network model and discusses its applicability to the forecasting of currency in circulation. The forecasting performance of the new neural network model is compared with an ARIMA model. The results indicate that the performance of the neural network model is better and that both models might be applied at least as supportive tools for liquidity forecasting.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.cnb.cz/en/research/research_publications/cnb_wp/download/cnbwp_2005_11.pdf
File Format:
File Function:
Download Restriction: no

Publisher Info
Paper provided by Czech National Bank, Research Department in its series Working Papers with number 2005/11.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: Dec 2005
Date of revision:
Handle: RePEc:cnb:wpaper:2005/11

Contact details of provider:
Postal: Na Prikope 28, 115 03 Prague 1
Phone: 00420 2 2442 1111
Fax: 00420 2 2421 8522
Email:
Web page: http://www.cnb.cz/en/research/research_intro/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Jan Babecky).

Related research
Keywords: Neural network; seasonal time series; currency in circulation.;

Find related papers by JEL classification:
C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Koreisha, Sergio G. & Pukkila, Tarmo, 1998. "A two-step approach for identifying seasonal autoregressive time series forecasting models," International Journal of Forecasting, Elsevier, vol. 14(4), pages 483-496, December. [Downloadable!] (restricted)
  2. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-68, July.
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 8898, University Library of Munich, Germany. [Downloadable!]
Statistics
Access and download statistics

Did you know? IDEAS also indexes software components.

This page was last updated on 2009-11-24.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.