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-type check are examined, as is a new procedure. Monte Carlo results on significance levels and power are reported. Asymptotic critical values fail to give good control of finite sample significance levels. It is, however, found that, if a particularly simple form of the wild bootstrap is used, it is possible to obtain well-behaved tests that are asymptotically robust to conditional heteroskedasticity of unspecified form.