Calculating Standard Errors of Predicted Values Based on Nonlinear Functional Forms

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dc.contributor.author	King, Gary
dc.date.accessioned	2010-08-04T14:41:32Z
dc.date.issued	1991
dc.identifier.citation	King, Gary. 1991. Calculating standard errors of predicted values based on nonlinear functional forms. The Political Methodologist 4(2): 2-4.	en_US
dc.identifier.uri	http://nrs.harvard.edu/urn-3:HUL.InstRepos:4323918
dc.description.abstract	Whenever we report predicted values, we should also report some measure of the uncertainty of these estimates. In the linear case, this is relatively simple, and the answer well-known, but with nonlinear models the answer may not be apparent. This short article shows how to make these calculations. I first present this for the familiar linear case, also reviewing the two forms of uncertainty in these estimates, and then show how to calculate these for any arbitrary function. An example appears last.	en_US
dc.description.sponsorship	Government	en_US
dc.language.iso	en_US	en_US
dc.publisher	The Society for Political Methodology	en_US
dc.relation.isversionof	http://polmeth.wustl.edu/index.php	en_US
dc.relation.hasversion	http://polmeth.wustl.edu/methodologist/tpm_v4_n2.pdf	en_US
dash.license	LAA
dc.title	Calculating Standard Errors of Predicted Values Based on Nonlinear Functional Forms	en_US
dc.type	Journal Article	en_US
dc.description.version	Version of Record	en_US
dc.relation.journal	The Political Methodologist	en_US
dash.depositing.author	King, Gary
dc.date.available	2010-08-04T14:41:32Z

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