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Non-Parametric Data Dependent Bootstrap for Conditional Moment Model

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  • Bruce E. Hansen

    (University of Wisconsin)

Abstract

A new non-parametric bootstrap is introduced for dependent data. The bootstrap is based on a weighted empirical-likelihood estimate of the one-step-ahead conditional distribution, imposing the conditional moment restrictions implied by the model. This is the first dependent-data bootstrap procedure which imposes conditional moment restrictions on a bootstrap distribution. The method can be applied to form confidence intervals and p-values from hypothesis tests in Generalized Method of Moments estimation The bootstrap method is illustrated with an application to autoregressive models with martingale difference errors.

Suggested Citation

  • Bruce E. Hansen, 2000. "Non-Parametric Data Dependent Bootstrap for Conditional Moment Model," Econometric Society World Congress 2000 Contributed Papers 1556, Econometric Society.
    • Handle: RePEc:ecm:wc2000:1556
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    Cited by:

    1. Gebauer, Stefan & Setzer, Ralph & Westphal, Andreas, 2017. "Corporate debt and investment: a firm analysis for stressed euro area countries," Working Paper Series 2101, European Central Bank.
    2. de Guevara, Juan Fernández & Maudos, Joaquín & Salvador, Carlos, 2022. "Firms’ investment, indebtedness and financial constraints: Size does matter," Finance Research Letters, Elsevier, vol. 46(PA).

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