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Ajuste recursivo con transformaciones invariantes y bootstrapping: El caso de una caminata aleatoria con intercepto


  • Eddy Lizarazu Alanez

    () (UAM-Iztapalapa)

  • Jose A. Villasenor Alva

    (Colegio de Postgraduados)


Usamos simulaciones de Monte Carlo para estudiar el desempeno de la prueba de raiz unitaria de Shin-So (DFSS) bajo los enfoques de transformaciones invariantes y el bootstrapping. Si la hipotesis alternativa es un proceso estacionario alrededor de una tendencia lineal, entonces la prueba bootstrap parametrica es la mejor en terminos de la potencia estadistica. Sin embargo, si transformamos las observaciones para construir una prueba invariante, entonces la prueba DFSS es la mejor. Por consiguiente, la recomendacion es usar transformaciones invariantes de la prueba de raiz unitaria de Shin-So debido a que su ejecucion es directa y de menor coste.

Suggested Citation

  • Eddy Lizarazu Alanez & Jose A. Villasenor Alva, 2010. "Ajuste recursivo con transformaciones invariantes y bootstrapping: El caso de una caminata aleatoria con intercepto," EconoQuantum, Revista de Economia y Negocios, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., vol. 7(1), pages 97-119, Julio - D.
  • Handle: RePEc:qua:journl:v:7:y:2010:i:1:p:97-119

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    References listed on IDEAS

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    More about this item


    Ajuste de tendencia recursivo; estadistico DF; metodo bootstrap parametrico.;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models


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