We describe and examine a consistent test for the correct specification of a regression function with dependent data. The test is based on the supremum of the difference between the parametric and nonparametric estimates of the regression model. Rather surprisingly, the behaviour of the test depends on whether the regressors are deterministic or stochastic. In the former situation, the normalization constants necessary to obtain the limiting Gumbel distribution are data dependent and difficult to estimate, so to obtain valid critical values may be difficult, whereas in the latter, the asymptotic distribution may not be even known. Because of that, under very mild regularity conditions we describe a bootstrap analogue for the test, showing its asymptotic validity and finite sample behaviour in a small Monte Carlo experiment.