A Transformed Random Effects Model with Applications
This paper proposes a transformed random effects model for analyzing non-normal panel data where both the response and (some of) the covariates are subject to transformations for inducing flexible functional form, normality, homoscedasticity, and simple model structure. We develop a maximum likeliho...
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sg-smu-ink.soe_research-15222010-09-23T05:48:03Z A Transformed Random Effects Model with Applications YANG, Zhenlin Huang, Jianhua This paper proposes a transformed random effects model for analyzing non-normal panel data where both the response and (some of) the covariates are subject to transformations for inducing flexible functional form, normality, homoscedasticity, and simple model structure. We develop a maximum likelihood procedure for model estimation and inference, along with a computational device which makes the estimation procedure feasible in cases of large panels. We provide model specification tests that take into account the fact that parameter values for error components cannot be negative. We illustrate the model and methods with two applications: state production and wage distribution. The empirical results strongly favor the new model to the standard ones where either linear or log-linear functional form is employed. Monte Carlo simulation shows that maximum likelihood inference is quite robust against mild departure from normality. 2009-12-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/523 info:doi/10.1002/asmb.822 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University computational device flexible functional form maximum likelihood estimation one-sided LM tests robustness Econometrics |
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computational device flexible functional form maximum likelihood estimation one-sided LM tests robustness Econometrics YANG, Zhenlin Huang, Jianhua A Transformed Random Effects Model with Applications |
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This paper proposes a transformed random effects model for analyzing non-normal panel data where both the response and (some of) the covariates are subject to transformations for inducing flexible functional form, normality, homoscedasticity, and simple model structure. We develop a maximum likelihood procedure for model estimation and inference, along with a computational device which makes the estimation procedure feasible in cases of large panels. We provide model specification tests that take into account the fact that parameter values for error components cannot be negative. We illustrate the model and methods with two applications: state production and wage distribution. The empirical results strongly favor the new model to the standard ones where either linear or log-linear functional form is employed. Monte Carlo simulation shows that maximum likelihood inference is quite robust against mild departure from normality. |
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YANG, Zhenlin Huang, Jianhua |
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YANG, Zhenlin Huang, Jianhua |
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YANG, Zhenlin |
title |
A Transformed Random Effects Model with Applications |
title_short |
A Transformed Random Effects Model with Applications |
title_full |
A Transformed Random Effects Model with Applications |
title_fullStr |
A Transformed Random Effects Model with Applications |
title_full_unstemmed |
A Transformed Random Effects Model with Applications |
title_sort |
transformed random effects model with applications |
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Institutional Knowledge at Singapore Management University |
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2009 |
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https://ink.library.smu.edu.sg/soe_research/523 |
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