Strategic prediction of the business cycle using the fuzzy regression model: a study of the Council of Economic Planning and Development in Taiwan
Lisa Y. Chen and
Bahaudin G. Mujtaba
International Journal of Business Forecasting and Marketing Intelligence, 2010, vol. 1, issue 3/4, 217-233
Abstract:
Understanding the current business cycle of a nation is essential for individuals, enterprises, and the government in order to make appropriate strategic decisions and take advantage of business opportunities. At a specific period of economic activities, the business cycle develops moderately and may lead to negative growth or economic recession when the economic activity expands to the peak. Business cycle functions as an indicator for the economic development of a nation and thus, it is an important tool for decision-makers. For example, the situation of the business cycle in Taiwan is acquired from the cyclical indicators, economic monitoring indicator, and reference dates of business cycles in Taiwan periodically announced by the Council of Economic Planning and Development (CEPD). This information provides a reference for individuals and investors to make decisions. Since business cycle is fuzzy in nature, this study uses the fuzzy regression analysis method to establish a regression model in order to provide a reference for enterprises and decision-makers to make the right investments.
Keywords: strategic decisions; economic activities; business cycles; economic monitoring indicators; fuzzy regression; Taiwan; regression models. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=36005 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbfmi:v:1:y:2010:i:3/4:p:217-233
Access Statistics for this article
More articles in International Journal of Business Forecasting and Marketing Intelligence from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().