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EFFECT OF THINNING INTERVALS ON BAYESIAN VARIANCE COMPONENT ESTIMATION: A SIMULATION STUDY OF INTERGENERATIONAL INCOME MOBILITY
Keywords:
Bayesian, VCE, Gibbs sampling, thinning interval, REMLDOI:
https://doi.org/10.17654/0972361722034Abstract
The aim of this study is to determine the effect of using different thinning intervals in Markov chains on the estimations of variance components. For this purpose, three different scenarios and three different thinning intervals are used in hierarchical intergenerational income mobility data obtained through simulation. In the simulation, incomes of a total of 1210 individuals with a relationship matrix are created with an average of 100 and a standard deviation of 10. The parts of the total variance explained by the relationships of individuals are 5%, 50% and 75%, respectively, in the reference REML estimates in scenarios I1, I2 and I3. Thinning intervals of 1, 10 and 25 are used in Markov chains created for all three scenarios. As a result of the Bayesian analyses performed using the Gibbs sampler, it was determined that the deviations for the random effects increased as the variance explanation rate increased. Contrary to this situation, a decrease in estimation deviations is observed due to the increase in intergenerational income mobility coefficients. Autocorrelation is detected in the posterior distribution of the variance components with a thinning interval of 1. As a result, MCMC may not be thinned in studies aimed at obtaining basic statistics using Bayesian methods. However, thinning must be done to avoid autocorrelation in the estimation of the variance component or some complex parameter estimations.
Received: March 5, 2022
Accepted: April 11, 2022
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