| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Outline of Final Research Achievements |
In this project, we developed the Bayesian quantile regression models with endogeneous covariates and estimation methods for the proposed models. The proposed model consists of two equation as in the conventional instrumental regression where the second stage regression includes the residuals from the first stage regression in order to remove the bias from the endogeneity. We also considered introducing nonlinear effects of endogenous covariates on the quantiles of the response variable. Although the nonlinear quantile regression is a flexible approach to quantile estimation, the estimates are knwon to be unstable in when the sample size is small or when the quantiles in the tails are estimated. We replace the asymmetric Laplace distribution, whose shape is severely restrictive for a data distribution, with the generalised asymmetric Laplace distribution for improved estimation performance.
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