Evaluating PcGets and RETINA as Automatic Model Selection Algorithms*
介绍并比较了RETINA和PcGets两种自动模型选择算法，通过蒙特卡洛模拟评估它们在非线性函数下的零假设和非零假设拒绝频率。
Abstract The paper describes two automatic model selection algorithms, RETINA and PcGets, briefly discussing how the algorithms work and what their performance claims are. RETINA's Matlab implementation of the code is explained, then the program is compared with PcGets on the data in Perez‐Amaral, Gallo and White (2005 , Econometric Theory , Vol. 21, pp. 262–277), ‘A Comparison of Complementary Automatic Modelling Methods: RETINA and PcGets’, and Hoover and Perez (1999 , Econometrics Journal , Vol. 2, pp. 167–191), ‘Data Mining Reconsidered: Encompassing and the General‐to‐specific Approach to Specification Search’. Monte Carlo simulation results assess the null and non‐null rejection frequencies of the RETINA and PcGets model selection algorithms in the presence of nonlinear functions.