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Smoothness priors and nonlinear regression

Author: Robert J Shiller; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, 1982.
Series: NBER technical working paper series, no. 25.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Abstract: In applications, the linear multiple regression model is often modified to allow for nonlinearity in an independent variable. It is argued here that in practice it may often be desirable to specify a Bayesian prior that the unknown functional form is "simple" or "uncomplicated" rather than to parametize the nonlinearity. "Discrete smoothness priors" and "continuous smoothness priors" are defined and it is  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Robert J Shiller; National Bureau of Economic Research.
OCLC Number: 71823251
Notes: Title from http://www.nber.org/papers/t0025 viewed May 6, 2013.
"August 1982."
Description: 1 online resource (29 pages) : illustrations.
Series Title: NBER technical working paper series, no. 25.
Responsibility: Robert J. Shiller.

Abstract:

Abstract: In applications, the linear multiple regression model is often modified to allow for nonlinearity in an independent variable. It is argued here that in practice it may often be desirable to specify a Bayesian prior that the unknown functional form is "simple" or "uncomplicated" rather than to parametize the nonlinearity. "Discrete smoothness priors" and "continuous smoothness priors" are defined and it is shown how posterior mean estimates can easily be derived using ordinary multiple linear regression modified with dummy variables and dummy observations. Relationships with spline and polynomial interpolation are pointed out. Illustrative examples of cost function estimation are provided.

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