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Goodness-of-fit tests in nonparametric regression

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Author Info
Einmahl, John H.J.
Van Keilegom, Ingrid (Tilburg University, Center for Economic Research)

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Abstract

Consider the location-scale regression model Y = m(X)+o(X)", where the error e is independent of the covariate X, and m and o are smooth but unknown functions. We construct tests for the validity of this model and show that the asymptotic limits of the proposed test statistics are distribution free. We also investigate the finite sample properties of the tests through a simulation study, and we apply the tests in the analysis of data on food expenditures.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 79.

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Date of creation: 2006
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Handle: RePEc:dgr:kubcen:200679

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Related research
Keywords: 62G08; 62G10; 62G20; 62G30; 60F17;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Einmahl, John H.J. & Van Keilegom, Ingrid, 2006. "Tests for independence in nonparametric regression," Discussion Paper 80, Tilburg University, Center for Economic Research. [Downloadable!]
  2. Adang, Pim & Melenberg, Bertrand, 1995. "Nonnegativity Constraints and Intratemporal Uncertainty in a Multi-good Life-Cycle Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(1), pages 1-15, Jan.-Marc. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Sokbae 'Simon' Lee & Oliver Linton & Yoon-Jae Whang, 2008. "Testing for stochastic monotonicity," CeMMAP working papers CWP21/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
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