Least squares learning and the US Treasury bill rate

Mishra,S and Dhole,S 2014, Least squares learning and the US Treasury bill rate, Economic Systems, vol. 38, no. 2, pp. 194-204, doi: 10.1016/j.ecosys.2013.09.004.

Attached Files
Name Description MIMEType Size Downloads

Title Least squares learning and the US Treasury bill rate
Author(s) Mishra,SORCID iD for Mishra,S orcid.org/0000-0003-0590-225X
Dhole,S
Journal name Economic Systems
Volume number 38
Issue number 2
Start page 194
End page 204
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014
ISSN 0939-3625
Keyword(s) Least squares learning
Structural break in monetary policy
Survey forecasts
US Treasury bill rate
Social Sciences
Economics
Business & Economics
MONETARY-POLICY
RATIONAL-EXPECTATIONS
INSTRUMENT
STABILITY
FORECASTS
MODELS
MARKET
RULES
STOCK
Summary Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.
Language eng
DOI 10.1016/j.ecosys.2013.09.004
Field of Research 150201 Finance
Socio Economic Objective 900199 Financial Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070619

Connect to link resolver
 
Link to Related Work
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 232 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Thu, 12 Mar 2015, 13:16:50 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.