 A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural NetworksNorman R Swanson and Halbert White Journal of Business & Economic Statistics, 1995, vol. 13, issue 3, 265-75 Abstract: We take a model selection approach to the question of whether forward interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model selection criteria: forecast mean squared error, forecast direction accuracy, and forecast-based trading system profitability. We also examine the usefulness of a class of novel prediction models called 'artificial neural networks,' and investigate the issue of appropriate window sizes for rolling-window-based prediction methods. Results indicate that the premium of the forward rate over the spot rate helps to predict the sign of future changes in the interest rate. Further, model selection based on an in-sample Schwarz Information Criterion (SIC) does not appear to be a reliable guide to out-of-sample performance, in the case of short-term interest rates. Thus, the in-sample SIC apparently fails to offer a convenient shortcut to true out-of-sample performance measures. Date: 1995 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:13:y:1995:i:3:p:265-75 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano More articles in Journal of Business & Economic Statistics from American Statistical Association |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to econpapers@oru.se.
EconPapers is hosted by the
School of Business at Örebro University.