A note on exact correspondences between adaptive learning algorithms and the Kalman filter
Digressing into the origins of the two main algorithms considered in the literature of adaptive learning, namely Least Squares (LS) and Stochastic Gradient (SG), we found a connection between their non-recursive forms and their interpretation within a state-space unifying framework. Based on such connection, we extend the correspondence between the LS and the Kalman filter recursions to a formulation with time-varying gains of the former, and also present a similar correspondence for the case of the SG. Our correspondences hold exactly, in a computational implementation sense, and we discuss how they relate to previous approximate correspondences found in the literature.
|Date of creation:||2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (0)161 275 4868
Fax: (0)161 275 4812
Web page: http://www.socialsciences.manchester.ac.uk/subjects/economics/our-research/centre-for-growth-and-business-cycle-research/
More information through EDIRC
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.:
- George W. Evans & Seppo Honkapohja & Noah Williams, 2010.
"Generalized Stochastic Gradient Learning,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(1), pages 237-262, 02.
- Evans, G.W. & Honkapohja ,S. & Williams, N., 2005. "Generalized Stochastic Gradient Learning," Cambridge Working Papers in Economics 0545, Faculty of Economics, University of Cambridge.
- George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," NBER Technical Working Papers 0317, National Bureau of Economic Research, Inc.
- George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," CESifo Working Paper Series 1576, CESifo Group Munich.
- George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," University of Oregon Economics Department Working Papers 2005-17, University of Oregon Economics Department, revised 18 May 2008.
- Wiliam Branch & George W. Evans, 2005.
"A Simple Recursive Forecasting Model,"
University of Oregon Economics Department Working Papers
2005-3, University of Oregon Economics Department, revised 01 Feb 2005.
- James H. Stock & Mark W. Watson, 1994.
"Evidence on Structural Instability in Macroeconomic Time Series Relations,"
NBER Technical Working Papers
0164, National Bureau of Economic Research, Inc.
- Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
- James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
- Bullard, James, 1992. "Time-varying parameters and nonconvergence to rational expectations under least squares learning," Economics Letters, Elsevier, vol. 40(2), pages 159-166, October.
- Thomas J. Sargent & Noah William, 2005.
"Impacts of Priors on Convergence and Escapes from Nash Inflation,"
Review of Economic Dynamics,
Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 360-391, April.
- Thomas J. Sargent & Noah Williams, 2003. "Impacts of priors on convergence and escapes from Nash inflation," Working Paper 2003-14, Federal Reserve Bank of Atlanta.
- McGough, Bruce, 2003. "Statistical Learning With Time-Varying Parameters," Macroeconomic Dynamics, Cambridge University Press, vol. 7(01), pages 119-139, February.
When requesting a correction, please mention this item's handle: RePEc:man:cgbcrp:170. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marianne Sensier)
If references are entirely missing, you can add them using this form.