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Bootstrap inference in semiparametric generalized additive models

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  • Hardle, Wolfgang
  • Huet, Sylvie
  • Mammen, Enno

Abstract

Semiparametric generalized additive models are a powerful tool in quantitative econometrics. The main focus is the application of bootstrap methods. It is shown that bootstrap can be used for bias correction, hypothesis testing (e.g. component-wise analysis) and the construction of uniform confidence bands. Various bootstrap tests for model specification and parametrization are given, in particular for testing additivity and link function specification. The practical performance of our methods is illustrated in simulations and in an application to East-West German migration.

Suggested Citation

  • Hardle, Wolfgang & Huet, Sylvie & Mammen, Enno, 2000. "Bootstrap inference in semiparametric generalized additive models," DES - Working Papers. Statistics and Econometrics. WS 10079, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:10079
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