Polynomial approximations in cross‐sectional models
展示如何将时间序列中的分布滞后多项式方法应用于截面、空间和截面时间序列数据，通过年龄别家庭构成和犯罪空间分布两个实例说明其有效性。
Abstract The time‐series distributed lag techniques of econometrics can be usefully applied to cross‐sectional, spatial and cross‐section time‐series situations. The application is perfectly natural in cross‐section, time‐series models when regression coefficients evolve systematically as the cross‐section grouping variable changes. The evolution of such coefficients lends itself to polynomial approximation or more general smoothing restrictions. These ideas are not new, Gersovitz and McKinnon (1978) and Trivedi and Lee (1981) providing two of the earliest applications of cross‐equation smoothing techniques. However, their applications were in the context of coefficient variation due to seasonal changes and this may account for the non‐diffusion of these techniques. The approach here is illustrated in the context of age‐specific household formation equations based on census data, using Almon polynomials when the regression coefficients vary systematically by age group. A second application is provided, using spatial data, explaining the incidence of crime, by region; using polynomial and geometric smoothing to model distance declining regional effects.