This paper proposes a new quantile regression model to characterize the heterogeneity for distributional effects of maternal smoking during pregnancy on infant birth weight across different the mother's age.By imp...This paper proposes a new quantile regression model to characterize the heterogeneity for distributional effects of maternal smoking during pregnancy on infant birth weight across different the mother's age.By imposing a parametric restriction on the quantile functions of the potential outcome distributions conditional on the mother's age,we estimate the quantile treatment effects of maternal smoking during pregnancy on her baby's birth weight across different age groups of mothers.The results show strongly that the quantile effects of maternal smoking on low infant birth weight are negative and substantially heterogenous across different ages.展开更多
To characterize heteroskedasticity,nonlinearity,and asymmetry in tail risk,this study investigates a class of conditional (dynamic) expectile models with partially varying coefficients in which some coefficients are a...To characterize heteroskedasticity,nonlinearity,and asymmetry in tail risk,this study investigates a class of conditional (dynamic) expectile models with partially varying coefficients in which some coefficients are allowed to be constants,but others are allowed to be unknown functions of random variables.A three-stage estimation procedure is proposed to estimate both the parametric constant coefficients and nonparametric functional coefficients.Their asymptotic properties are investigated under a time series context,together with a new simple and easily implemented test for testing the goodness of fit of models and a bandwidth selector based on newly defined cross-validatory estimation for the expected forecasting expectile errors.The proposed methodology is data-analytic and of sufficient flexibility to analyze complex and multivariate nonlinear structures without suffering from the curse of dimensionality.Finally,the proposed model is illustrated by simulated data,and applied to analyzing the daily data of the S&P500 return series.展开更多
Editorial Introduction This special issue is dedicated to forecasting and modeling which are well regarded as two of the most challenging tasks in economics and finance because of the complexities of economic and fina...Editorial Introduction This special issue is dedicated to forecasting and modeling which are well regarded as two of the most challenging tasks in economics and finance because of the complexities of economic and financial data,such as nonlinearity,non-stationarity,and irregularities.How to forecast economic and financial data accurately is still an open question in the profession and practice.展开更多
基金financial supports from the National Natural Science Foundation of China(NSFC)for Distinguished Scholars(71625001)the NSFC key projects with grant numbers 71631004,72033008 and 71131008Science Foundation of Ministry of Education of China(19YJA910003).
文摘This paper proposes a new quantile regression model to characterize the heterogeneity for distributional effects of maternal smoking during pregnancy on infant birth weight across different the mother's age.By imposing a parametric restriction on the quantile functions of the potential outcome distributions conditional on the mother's age,we estimate the quantile treatment effects of maternal smoking during pregnancy on her baby's birth weight across different age groups of mothers.The results show strongly that the quantile effects of maternal smoking on low infant birth weight are negative and substantially heterogenous across different ages.
基金The authors thank the Guest Editors and the anonymous referees for their helpful and constructive comments.The authors also acknowledge gratefully the partial financial support from the National Science Fund for Distinguished Young Scholars#71625001the Natural Science Foundation of China grants#7the scholarship from China Scholarship Council under the Grant CSC N201706310023.
文摘To characterize heteroskedasticity,nonlinearity,and asymmetry in tail risk,this study investigates a class of conditional (dynamic) expectile models with partially varying coefficients in which some coefficients are allowed to be constants,but others are allowed to be unknown functions of random variables.A three-stage estimation procedure is proposed to estimate both the parametric constant coefficients and nonparametric functional coefficients.Their asymptotic properties are investigated under a time series context,together with a new simple and easily implemented test for testing the goodness of fit of models and a bandwidth selector based on newly defined cross-validatory estimation for the expected forecasting expectile errors.The proposed methodology is data-analytic and of sufficient flexibility to analyze complex and multivariate nonlinear structures without suffering from the curse of dimensionality.Finally,the proposed model is illustrated by simulated data,and applied to analyzing the daily data of the S&P500 return series.
文摘Editorial Introduction This special issue is dedicated to forecasting and modeling which are well regarded as two of the most challenging tasks in economics and finance because of the complexities of economic and financial data,such as nonlinearity,non-stationarity,and irregularities.How to forecast economic and financial data accurately is still an open question in the profession and practice.
