Robust critical values for unit root tests for series with conditional heteroskedasticity errors using wild bootstrap

USING THE WILD BOOTSTRAP TO IMPLEMENT HETEROSKEDASTICITY-ROBUST TESTS FOR SERIAL CORRELATION IN DYNAMIC REGRESSION MODELS* by

Conditional heteroskedasticity is a common feature of financial and macroeconomic time series data. When such data are used to estimate dynamic regression models, standard checks for serial correlation are inappropriate. In such circumstances, it is obviously important to have valid tests that are reliable in finite samples. Generalizations of the standard Lagrange multiplier test and a Hausman...

Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation

Bootstrap Unit Root Tests

We consider the bootstrap unit root tests based on finite order autoregressive integrated models driven by iid innovations, with or without deterministic time trends. A general methodology is developed to approximate asymptotic distributions for the models driven by integrated time series, and used to obtain asymptotic expansions for the Dickey–Fuller unit root tests. The second-order terms in ...

Fractional Dickey-Fuller tests under heteroskedasticity

In a recent paper, Dolado, Gonzalo and Mayoral (2002) introduce a fractional Dickey-Fuller (FD-F) t-statistic for testing a unit root against the alternative of a mean reverting fractional unit root process. This t-statistic is based on the assumption that the errors are unconditionally homoskedastic. However, Busetti and Taylor (2003), McConnell and Perez-Quiros (2000), and van Dijk et al. (20...

A Nonparametric Goodness-of-fit-based Test for Conditional Heteroskedasticity∗

In this paper we propose a nonparametric test for conditional heteroskedasticity based on a new measure of nonparametric goodness-of-fit (R2). In analogy with the ANOVA tools for classical linear regression models, the nonparametric R2 is obtained for the local polynomial regression of the residuals from a parametric regression on some covariates. It is close to 0 under the null hypothesis of c...