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Unified specification tests in partially linear time series models

题目Unified specification tests in partially linear time series models
作者孙爽; 宋泽宁; 宋晓军
作者单位Tsinghua Univ, Ctr Stat Sci, Beijing, Peoples R China Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China Nankai Univ, Sch Stat & Data Sci, LPMC & KLMDASR, Tianjin, Peoples R China Peking Univ, Guanghua Sch Management, Dept Business Stat & Econometr, Beijing, Peoples R China
关键词：OF-FIT TESTS SINGLE-INDEX FUNCTIONAL FORM CONSISTENT TEST REGRESSION VOLATILITY BOOTSTRAP CHECKS DIFFERENCE INFERENCE
时间：2025年3月1日
出版者：COMPUTATIONAL STATISTICS & DATA ANALYSIS
摘要Based on a residual marked empirical process, Cram & eacute;r-von Mises and Kolmogorov-Smirnov tests are proposed for the correct specification of the nonparametric components in partially linear time series models. The tests are unified in the sense that the asymptotic distribution of residual marked empirical process is invariant across different n(v)-consistent estimators in calculating residuals (where v > 1/4) under the null. In addition, the residual marked empirical process has the same power property under the sequence of local alternatives regardless of the estimators used. Achieved through a projection method, these features also enable using a computationally convenient multiplier bootstrap to approximate the unified null distributions of the test statistics. Simulations show satisfactory finite-sample performance of the proposed method. The application to validate the parametric form of conditional variance in the ARCH-X model is also highlighted, along with an empirical analysis of the conditional variance of the FTSE 100 index return series.
URLhttp://hdl.handle.net/20.500.11897/725796
ISSN0167-9473
DOI10.1016/j.csda.2024.108074
收录情况SCI（E）
作者单位 Tsinghua Univ, Ctr Stat Sci, Beijing, Peoples R China Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China Nankai Univ, Sch Stat & Data Sci, LPMC & KLMDASR, Tianjin, Peoples R China Peking Univ, Guanghua Sch Management, Dept Business Stat & Econometr, Beijing, Peoples R China 时间 2025年3月1日
出版者 COMPUTATIONAL STATISTICS & DATA ANALYSIS URL http://hdl.handle.net/20.500.11897/725796
ISSN 0167-9473 DOI 10.1016/j.csda.2024.108074
收录情况 SCI（E） 分类
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