This paper estimated returns to education of Chinese residents using the optimal instrumental variables selection method based on simulated annealing algorithm. First, we introduced 38 excluded instrumental variables ...
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This paper estimated returns to education of Chinese residents using the optimal instrumental variables selection method based on simulated annealing algorithm. First, we introduced 38 excluded instrumental variables as the possible endogenous explanatory variable of years of education based on the rules of instrumental variables in current literature and then to get a two-stage least-squares (2SLS) regression with those variables. With this regression, we found out that there are weak instruments in the model. Then, based on simulated annealing algorithm, we chose spouses' education years (edus) as the most appropriate instrumental variable and others as redundant instruments and weak instruments. Furthermore, a heteroscedasticity test was conducted on the regression residual of the structural equation and got a heteroscedastic result among disturbance terms. After applying generalized method of moments (GMM) and a series of model specification tests, we finally got six exact excluded instrumental variables. Last but not least, after the final GMM estimation based on the selected optimal instrumental variables, the conclusion of this paper was that the returns to education of Chinese residents are 9.96%. (C) 2014 Published by Elsevier B.V.
This paper estimated returns to education of Chinese residents using the optimal instrumental variables selection method based on simulated annealing algorithm. First, we introduced 38 excluded instrumental variables ...
详细信息
This paper estimated returns to education of Chinese residents using the optimal instrumental variables selection method based on simulated annealing algorithm. First, we introduced 38 excluded instrumental variables as the possible endogenous explanatory variable of years of education based on the rules of instrumental variables in current literature and then to get a two- stage least-squares (2SLS) regression with those variables. With this regression, we found out that there are weak instruments in the model. Then, based on simulated annealing algorithm, we chose spouses’ education years (edus) as the most appropriate instrumental variable and others as redundant instruments and weak instruments. Furthermore, a heteroscedasticity test was conducted on the regression residual of the structural equation and got a heteroscedastic result among disturbance terms. After applying generalized method of moments (GMM) and a series of model specification tests, we finally got six exact excluded instrumental variables. Last but not least, after the final GMM estimation based on the selected optimal instrumental variables, the conclusion of this paper was that the returns to education of Chinese residents are 9.96%.
Small area statistics has received considerable attention in the last two decades from both public and private sectors. More recently, semiparametric mixed-effects models have been proposed for a more flexible modelli...
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Small area statistics has received considerable attention in the last two decades from both public and private sectors. More recently, semiparametric mixed-effects models have been proposed for a more flexible modelling. Surprisingly, although modelspecification testing is of particular importance in small area statistics, this has been less explored. Its importance is based on the fact that small area statistics applies model-based estimation and prediction. Local polynomials can nest typically used parametric models without bias - independent of the smoothing parameter - and are therefore particularly useful in practice. First, estimation and testing with local polynomials is introduced for mixed-effects models. Several extensions for further structural modelling with dimension-reducing effects are discussed. Second, different computationally attractive specificationtests are proposed and compared. The methods are compared along simulation studies. Its usefulness is underpinned by the small-area regression problems of forest stand and farm production.
In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a s...
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In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for testing the null hypothesis of random effects against fixed effects in a nonparametric panel data regression model. Simulations are used to examine the finite sample performance of the proposed estimators and the test statistics. Published by Elsevier B.V.
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