Bayesian Full Information Analysis of Simultaneous Equation Models Using Integration by Monte Carlo

1984 ◽  
Author(s):  
Luc Bauwens
Keyword(s):  
Monte Carlo ◽  
Simultaneous Equation ◽  
Full Information ◽  
Information Analysis ◽  
Simultaneous Equation Models

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

Testing for serial correlation in simultaneous equation models

1981 ◽  
Vol 17 (1) ◽  
pp. 99-105 ◽  
Author(s):  
A.C. Harvey ◽  
G.D.A. Phillips
Keyword(s):  
Serial Correlation ◽  
Simultaneous Equation ◽  
Simultaneous Equation Models
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