scholarly journals Likelihood-Preserving Normalization in Multiple Equation Models

2000 ◽  
Author(s):  
Daniel F. Waggoner ◽  
Tao A. Zha
Keyword(s):  
Multiple Equation Models

scholarly journals Semiparametric Bayesian inference in multiple equation models

2005 ◽  
Vol 20 (6) ◽  
pp. 723-747 ◽  
Author(s):  
Gary Koop ◽  
Dale J. Poirier ◽  
Justin Tobias
Keyword(s):  
Bayesian Inference ◽  
Semiparametric Bayesian Inference ◽  
Multiple Equation Models

SEMIPARAMETRIC ESTIMATION OF MULTIPLE EQUATION MODELS

2000 ◽  
Vol 16 (4) ◽  
pp. 551-575 ◽  
Author(s):  
Gabriel A. Picone ◽  
J.S. Butler
Keyword(s):  
Monte Carlo ◽  
Choice Model ◽  
Average Distance ◽  
Sample Selection ◽  
Semiparametric Estimation ◽  
Monte Carlo Experiment ◽  
Finite Sample ◽  
Semiparametric Estimator ◽  
Multinomial Choice ◽  
Multiple Equation Models

This paper proposes a semiparametric estimator for multiple equations multiple index (MEMI) models. Examples of MEMI models include several sample selection models and the multinomial choice model. The proposed estimator minimizes the average distance between the dependent variable unconditional and conditional on an index. The estimator is √N-consistent and asymptotically normally distributed. The paper also provides a Monte Carlo experiment to evaluate the finite-sample performance of the estimator.

SPECIFICATION AND ESTIMATION OF SEMIPARAMETRIC MULTIPLE-INDEX MODELS

2008 ◽  
Vol 24 (6) ◽  
pp. 1584-1606 ◽  
Author(s):  
Bas Donkers ◽  
Marcia Schafgans
Keyword(s):  
Method Of Moments ◽  
A Priori ◽  
Estimation Procedure ◽  
Outer Product ◽  
Moment Conditions ◽  
Asymptotically Normal ◽  
Nonparametric Kernel ◽  
Two Step Estimation ◽  
Derivatives Of ◽  
Multiple Equation Models

We propose an easy to use derivative-based two-step estimation procedure for semiparametric index models, where the number of indexes is not known a priori. In the first step various functionals involving the derivatives of the unknown function are estimated using nonparametric kernel estimators, in particular the average outer product of the gradient (AOPG). By testing the rank of the AOPG we determine the required number of indexes. Subsequently, we estimate the index parameters in a method of moments framework, with moment conditions constructed using the estimated average derivative functionals. The estimator readily extends to multiple equation models and is shown to be root-N-consistent and asymptotically normal.

scholarly journals Likelihood preserving normalization in multiple equation models

2003 ◽  
Vol 114 (2) ◽  
pp. 329-347 ◽  
Author(s):  
Daniel F. Waggoner ◽  
Tao Zha
Keyword(s):  
Multiple Equation Models
Sign in / Sign up

Export Citation Format

Share Document