Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

被引：3

作者：

Cortes-Toto, Daniela ^{[1
]}

Guerrero, Victor M. ^{[2
]}

Reyes, Hortensia J. ^{[1
]}

机构：

[1] Benemerita Univ Autonoma Puebla Puebla, Fac Ciencias Fis Matemat, Puebla, Mexico

[2] ITAM, Dept Estadist, Mexico City, DF, Mexico

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2017年 / 46卷 / 02期

关键词：

Hodrick-Prescott filter; Penalized least squares; Percentage of smoothness; Smoothing parameter; Time series decomposition; Trend estimation; BUSINESS CYCLES; TIME-SERIES; SELECTION; INFORMATION; SPLINES; SIMULATION; AKAIKE;

D O I：

10.1080/03610918.2015.1005236

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.

引用

页码：1492 / 1507

页数：16