Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/2936
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dc.contributor.authorManzan, Sebastianoen_GB
dc.date.accessioned2009-12-08T16:22:33Z-
dc.date.available2009-12-08T16:22:33Z-
dc.date.issued2004en_GB
dc.identifier.citationEmpirical Economics, 2004, 29 (4), pp.901-920en_GB
dc.identifier.urihttp://link.springer.com/article/10.1007%2Fs00181-004-0207-7en_GB
dc.identifier.urihttp://hdl.handle.net/2381/2936-
dc.description.abstractWe investigate the finite-sample performance of model selection criteria for local linear regression by simulation. Similarly to linear regression, the penalization term depends on the number of parameters of the model. In the context of nonparametric regression, we use a suitable quantity to account for the Equivalent Number of Parameters as previously suggested in the literature. We consider the following criteria: Rice T, FPE, AIC, Corrected AIC and GCV. To make results comparable with other data-driven selection criteria we consider also Leave-Out CV. We show that the properties of the penalization schemes are very different for some linear and nonlinear models. Finally, we set up a goodness-of-fit test for linearity based on bootstrap methods. The test has correct size and very high power against the alternatives investigated. Application of the methods proposed to macroeconomic and financial time series shows that there is evidence of nonlinearity.-
dc.formatMetadataen_GB
dc.language.isoenen_GB
dc.titleModel Selection for Nonlinear Time Seriesen_GB
dc.typeArticleen_GB
dc.identifier.doi10.1007/s00181-004-0207-7-
dc.relation.raeRAE 2007-
Appears in Collections:Published Articles, Dept. of Economics

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