Model-based clustering using Bayesian approach for binary panel Probit models

Research paper

Model-Based Clustering using Bayesian Approach For Binary Panel Probit Models Tags : Statswork | Statistical Analysis | Probit Models | Regression Model | Mixed Models | Cross-Validation | Posterior Distribution | Generalized Linear Mixed Models Research Planning | Data Collection | Semantic Annotation | Business Analytics | Bio Statistics | Econometrics

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Bayesian Modelling In common Statistical Analysis, the classical estimation for various situations may be invalid, in the sense that it may be lead to misinterpretations.

In Bayesian paradigm, each explanatory variable are assumed to be a random variables.

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With the advent of computational practice, Bayesian modelling becomes a interesting area of research in all the field of science.

03 02 To deliver more appropriate results for the study, Bayesian paradigms have emerged.

05 04 It involves formulating a suitable prior distribution for the data under study and result will yield in a posterior distribution.

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Probit Model A probit model is a type of Regression Model in which the response variable can take only binary outcomes (eg. yes or no, married or unmarried, male or female, etc.,).

Generally, Probit model are considered as class of Generalized Linear Mixed Models especially in the panel study.

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Probit Model Issues

Endogeneity and Heterogeneity in the probit or logit models are an important issue in estimating the variables since it estimates the cumulative functions.

Heterogeneity especially in non-linear model yields an attention to the researchers in recent years to provide better estimates to the variables.

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Probit Model Using Bayesian Perspective An approach of using marginal likelihood and a cross-validation technique is adopted to identify the number of clusters and a simulation study is adopted to assess these approaches in AĂ&#x;mann and Hogrefe (2011).

Bayesian flavour has been increasing in probit model to provide appropriate results.

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Clustering approach is served as a supporting tool for modelling the latent heterogeneity in the probit models.

Bayesian estimation can be used to estimate even smaller sample size & identifies unknown or uncertain parameters through a Posterior Distribution.

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Literature is abundant for estimating the latent heterogeneity through clustering approach using fixed and random effects.

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ILLUSTRATIVE EXAMPLE Research Planning | Data Collection | Semantic Annotation | Business Analytics | Bio Statistics | Econometrics

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The priors are chosen with normal and dirichlet distribution for the parameters under study and are presented in the below table.

The non-random and random cluster specification priors are used for the simulation.

Random coefficient specification is used for the empirical study.

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Simulation Study

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Estimation procedures adopted for the simulation study are Gibbs sampling, MCMC sampling, and Bridge sampling using maximum likelihood and CrossValidation technique and conducted eleven scenarios for this purpose.

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A comparison of the log-likelihood of the marginals and the out-of-sample prediction is presented with cluster and stratified clustering technique.

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Bayesian Probit Model Two cluster specific random effects (RE) specifications with uncorrelated and correlated random effects with stratified and unconditional probabilities are considered to model the latent heterogeneity using binary probit model using Bayesian estimation and the comparison results of the models are depicted in the below table.

The empirical study indicated that the proposed method provides better model specifications.

The proposed stratified clustering Bayesian probit model is illustrated with a real time dataset from Bertschek and Lechner (1998).

The data involves investments of 1270 competitive firms from the years 1984 to 1988.

Contd...

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It is clear that random coefficient specification is preferred as normally described in Jefferys’ scale.

Uncorrelated random effects shows a better characterization of latent heterogeneity.

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Determination Of No. Of Clusters Stratified and non-stratified probabilities are used and it yields similar results in estimating via marginal likelihood.

In the out-of-sample method, AUC measure indicated the better cluster strategy to use i.e the non-stratified clusters and the Bayesian estimation for the preferred specifications are tabulated and it concludes that the firm has no positive effect.

The variables substantiate the effect of the firm innovation is that the log scale, investment and the firm size among the other variables.

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Summary

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The issue of model selection under the latent heterogeneity is analyzed using the Bayesian probit model via clustering approach and the results of the study revealed that the model selection using marginal likelihood is preferred in Bayesian point of view.

The proposed stratified clustering technique yields better performance in different scenarios than compared with classical non-stratified clustering method.

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The illustrative example revealed that the there exists a strong evidence in capturing the latent clusters using this latent heterogeneity methods.

In out of sample method, the stratified clustering doesn’t give satisfactory results because of the AUC measure and concludes that there is a need for more appropriate methodology to give consistent results in model selection process.

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