Title

A Paradox of Inconsistent Parametric and Consistent Nonparametric Regression

Publication Type

Working Paper

Publication Date

2009

Abstract

This paper explores a paradox discovered in recent work by Phillips and Su (2009). That paper gave an example in which nonparametric regression is consistent whereas parametric regression is inconsistent even when the true regression functional form is known and used in regression. This appears to be a paradox, as knowing the true functional form should not in general be detrimental in regression. In the present case, local regression methods turn out to have a distinct advantage because of endogeneity in the regressor. The paradox arises because additional correct information is not necessarily advantageous when information is incomplete. In the present case, endogeneity in the regressor introduces bias when the true functional form is known, but interestingly does not do so in local nonparametric regression. We examine this example in detail and propose two new consistent estimators for the parametric regression, which address the endogeneity in the regressor by means of spatial bounding and bias correction using nonparametric estimation. Some simulations are reported illustrating the paradox and the new procedures.

Keywords

bias-correction, endogeneity, Kernel regression, L_{2} regression, location shift, nonparametric IV, nonstationarity, paradox, spatial regression, structural estimation

Discipline

Econometrics

Research Areas

Econometrics

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1414345

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