Testing for neglected nonlinearity in regression models based on the theory of random fields.
Within a flexible regression model (J.D. Hamilton, Econometrica 69 (3) (2001) 537) we offer a battery of new Lagrange multiplier statistics that circumvent the problem of unidentified nuisance parameters under the null hypothesis of linearity and that are robust to the specification of the covarianc...
| Publicado en: | Journal of Econometrics Vol. 114; no. 1; pp. 141 - 165 |
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| Autores principales: | , |
| Formato: | Artículo |
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Elsevier Science
May 2003
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=513135824&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513135824 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: May 2003 vid: 114 iid: 1 pid: 1004 pub: Elsevier Science artinfo: ui: 513135824 10.1016/S0304-4076(02)00222-1 ppf: 141 ppct: 24 formats: tig: atl: Testing for neglected nonlinearity in regression models based on the theory of random fields. aug: au: Dahl, Christian M. González-Rivera, Gloria su: Statistical hypothesis testing Regression analysis Nonlinear theories Lagrange multiplier sug: subj: Statistical hypothesis testing Regression analysis Nonlinear theories Lagrange multiplier ab: Within a flexible regression model (J.D. Hamilton, Econometrica 69 (3) (2001) 537) we offer a battery of new Lagrange multiplier statistics that circumvent the problem of unidentified nuisance parameters under the null hypothesis of linearity and that are robust to the specification of the covariance function that defines the random field. These advantages are the result of (i) switching from the L2 to the L1 norm; and (ii) assuming that the random field is sufficiently smooth for its covariance function to be locally approximated by a high order Taylor expansion. A Monte Carlo simulation suggests that our statistics have superior power performance on detecting bilinear, neural network, and smooth transition autoregressive specifications. We also provide an application to the Industrial Production Index of sixteen OECD countries. Copyright (c) 2002 Elsevier Science B.V. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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