伊宁市民族居住格局和民族关系

本文以伊宁市民族居住格局为研究对象,通过对伊宁市建城沿革和维、汉、回、哈民族居住史的回顾及对伊宁市的民族居住格局及其变迁状况的调查研究,分析、探讨了影响伊宁市民族居住格局变迁的因素,并在此基础上来研究伊宁市的民族关系.     

从回汉"社亲"关系看和谐文化的民间创造

在中原地区,一些回汉村庄结成数百年不渝、情同手足的友好关系,民间称之为"社亲".仁义、感恩、重诺、守信等传统美德是这种关系的道德基础,仪式化的情谊交流方式以及平等、宽容、相互尊重的文化理念是这种关系的保证.回汉村庄的这种情谊扎根在传统社会文化的土壤中,具有不可忽略的现代价值.     

现代民族素质的结构及其相互关系--民族素质研究之三

本文从智能素质在民族素质结构中的作用、体能素质与智能素质的关系、智能素质和传统文化及民族历史积淀对民族心理、意识素质的影响等方面,对民族素质的结构及其相互关系进行了开创性的研究.     

关于族群关系、民族关系与社会关系的关系

"族群"与"民族"概念上存在着交叉关联.族群关系、民族关系都是一种社会关系,但社会关系不一定都构成民族关系,民族关系有时也不一定能够准确地表述特殊的社会关系,族群关系却可以化解民族关系表述的"潜在危机".族群关系、民族关系与社会关系的关系具有多向性、非对称性和动态性的特点.     

明清时期国家与社会关系转型境遇下的回族社区——以历史上西安回族社区文化变迁为视点

本文以历史上西安回族社区为视点,运用人类学的理论和方法,在明清时期国家与社会关系转型的境遇下,分析了回族社区文化的变迁.     

“花儿”与伊斯兰文化的关系

阿拉伯歌曲的历史延续、我国元代以后的回回曲、西北地区伊斯 兰民族的经历等显示出"花儿"与伊斯兰文化的关系,"花儿"的现存唱词和有关 传说同样证实了这一点。     

Comment on “Constrained Optimization Approaches to Estimation of Structural Models”

We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (2012). Their implementation of the nested fixed point algorithm used successive approximations to solve the inner fixed point problem (NFXP‐SA). We redo their comparison using the more efficient version of NFXP proposed by Rust (1987), which combines successive approximations and Newton–Kantorovich iterations to solve the fixed point problem (NFXP‐NK). We show that MPEC and NFXP are similar in speed and numerical performance when the more efficient NFXP‐NK variant is used.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

A Dynamic Model of Demand for Houses and Neighborhoods

This paper develops a dynamic model of neighborhood choice along with a computationally light multi‐step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non‐marketed amenities—neighborhood air pollution, violent crime, and racial composition—in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.