000120212 001__ 120212
000120212 005__ 20241125101127.0
000120212 0247_ $$2doi$$a10.1111/jtsa.12668
000120212 0248_ $$2sideral$$a131008
000120212 037__ $$aART-2023-131008
000120212 041__ $$aeng
000120212 100__ $$0(orcid)0000-0002-0437-7812$$aOlmo, Jose
000120212 245__ $$aA nonparametric predictive regression model using partitioning estimators based on Taylor expansions
000120212 260__ $$c2023
000120212 5060_ $$aAccess copy available to the general public$$fUnrestricted
000120212 5203_ $$aThis article proposes a nonparametric predictive regression model. The unknown function modeling the predictive relationship is approximated using polynomial Taylor expansions applied over disjoint intervals covering the support of the predictor variable. The model is estimated using the theory on partitioning estimators that is extended to a stationary time series setting. We show pointwise and uniform convergence of the proposed estimator and derive its asymptotic normality. These asymptotic results are applied to test for the presence of predictive ability. We develop an asymptotic pointwise test of predictive ability using the critical values of a Normal distribution, and a uniform test with asymptotic distribution that is approximated using a p-value transformation and Wild bootstrap methods. These theoretical insights are illustrated in an extensive simulation exercise and also in an empirical application to forecasting high-frequency based realized volatility measures. Our results provide empirical support to the presence of nonlinear autoregressive predictability of these measures for the constituents of the Dow Jones index.
000120212 536__ $$9info:eu-repo/grantAgreement/ES/DGA/ARAID$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104326GB-I00
000120212 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000120212 592__ $$a0.875$$b2023
000120212 593__ $$aApplied Mathematics$$c2023$$dQ1
000120212 593__ $$aStatistics, Probability and Uncertainty$$c2023$$dQ2
000120212 593__ $$aStatistics and Probability$$c2023$$dQ2
000120212 590__ $$a1.2$$b2023
000120212 591__ $$aSTATISTICS & PROBABILITY$$b74 / 168 = 0.44$$c2023$$dQ2$$eT2
000120212 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b92 / 135 = 0.681$$c2023$$dQ3$$eT3
000120212 594__ $$a2.0$$b2023
000120212 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000120212 773__ $$g44, 3 (2023), 294-318$$pJ. time ser. anal.$$tJOURNAL OF TIME SERIES ANALYSIS$$x0143-9782
000120212 8564_ $$s2452365$$uhttps://zaguan.unizar.es/record/120212/files/texto_completo.pdf$$yVersión publicada
000120212 8564_ $$s2296929$$uhttps://zaguan.unizar.es/record/120212/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000120212 909CO $$ooai:zaguan.unizar.es:120212$$particulos$$pdriver
000120212 951__ $$a2024-11-22-11:58:04
000120212 980__ $$aARTICLE