Now showing items 1-3 of 3

  • The Bootstrap of Mean for Dependent Heterogeneous Arrays 

    Gonçalves, Sílvia; WHITE, Halbert (Université de Montréal. Département de sciences économiques., 2001)
    Presently, conditions ensuring the validity of bootstrap methods for the sample mean of (possibly heterogeneous) near epoch dependent (NED) functions of mixing processes are unknown. Here we establish the validity of the bootstrap in this context, ...
  • Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form 

    Gonçalves, Sílvia; KILIAN, Lutz (Université de Montréal. Département de sciences économiques., 2003)
    Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the ...
  • Bootstrapping factor models with cross sectional dependence 

    Gonçalves, Sílvia; Perron, Benoit (Université de Montréal. Département de sciences économiques., 2018-07)
    We consider bootstrap methods for factor-augmented regressions with cross sectional dependence among idiosyncratic errors. This is important to capture the bias of the OLS estimator derived recently by Gonçalves and Perron (2014). We first show that a ...