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Nonparametric Seemingly Unrelated Regression

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Author Info
Smith, M.
Kohn, R.

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Abstract

This paper presnets a method for simultaneously estimating a system of nonparametric multiple regressions which may seem unrelated, but where the errors are potentially correlated between equations. We show that the prime advantage of estimating such a 'seemingly unrelated' system of nonparametric regressions is that substantially less observations can be required to obtain reliable functions estimates than if each of the regression equations was estimated separately and the correlation ignored.

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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 7/98.

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Length: 31 pages
Date of creation: 1998
Date of revision:
Handle: RePEc:msh:ebswps:1998-7

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Related research
Keywords: MODELS ECONOMETRICS

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Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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  1. W.E. Griffiths & Ma. Rebecca Valenzuela, 2004. "Gibbs Samplers for a Set of Seemingly Unrelated Regressions," Department of Economics - Working Papers Series 912, The University of Melbourne. [Downloadable!]
  2. Ericsson, Johan & Karlsson, Sune, 2003. "Choosing Factors in a Multifactor Asset Pricing Model: A Bayesian Approach," Working Paper Series in Economics and Finance 524, Stockholm School of Economics, revised 12 Feb 2004. [Downloadable!]
  3. Griffiths, W.E., 2001. "Bayesian Inference in the Seemingly Unrelated Regressions Models," Department of Economics - Working Papers Series 793, The University of Melbourne. [Downloadable!]
  4. Gary Koop & Dale Poirier & Justin Tobias, 2003. "Bayesian Semiparametric Inference in Multiple Equation Models," Discussion Papers in Economics 04/17, Department of Economics, University of Leicester. [Downloadable!]
  5. Anthony Hall & Soosung Hwang & Stephen E. Satchell, 2000. "Using Bayesian Variable Selection Methods to Choose Style Factors in Global Stock Return Models," Econometric Society World Congress 2000 Contributed Papers 1213, Econometric Society. [Downloadable!]
  6. Dale J. Poirier & Gary Koop & Justin Tobias, 2005. "Semiparametric Bayesian inference in multiple equation models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 723-747. [Downloadable!]
    Other versions:
  7. Anthony D. Hall & S. Hwang & Steve Satchell, 2000. "Using Bayesian Variable Selection Methods to Choose Style Factors in Global Stock Return," Research Paper Series 31, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]
  8. Jacobson, Tor & Karlsson, Sune, 2002. "Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach," Working Paper Series 138, Sveriges Riksbank (Central Bank of Sweden). [Downloadable!]
    Other versions:
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