Least squares learning and the US Treasury bill rate

Sagarika Mishra and Sandip Dhole

Economic Systems, 2014, vol. 38, issue 2, 194-204

Abstract: 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.

Keywords: Survey forecasts; Least squares learning; US Treasury bill rate; Structural break in monetary policy (search for similar items in EconPapers)
JEL-codes: D83 D84 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0939362514000235
Full text for ScienceDirect subscribers only

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:eee:ecosys:v:38:y:2014:i:2:p:194-204

DOI: 10.1016/j.ecosys.2013.09.004

Access Statistics for this article

Economic Systems is currently edited by R. Frensch

More articles in Economic Systems from Elsevier Contact information at EDIRC.
Bibliographic data for series maintained by Catherine Liu ().