When a model is nonlinear, boostrap testing can be expensive because of the need to perform at least one nonlinear estimation for every bootstrap sample. We show that it may be possible to reduce computational costs by performing only a fixed, small number of Newton steps or artificial...

In this paper we are interested in inference based on heteroskedasticity consistent covariance matrix estimators, for which the appropriate bootstrap is a version of the wild bootstrap. Simulation results, obtained by a new very efficient methos, show that all wild bootstraps tests exhibit...

Bootstrap tests are tests for which the significance level is calculated by some sort of bootstrap procedure, which may be parametric or nonparametric. We provide a theoretical framework in which to study the size distorsions of bootstrap P values. We show that, in many circumstances, the size...

Most confidence intervals, whether based on asymptotic theory or the bootstrap, are implicitly based on inverting a Wald test. Since Wald test statistics are not invariant under nonlinear reparametrizations of the restrictions they test, confidence intervals based on them are not invariant...

In this paper we develop a general strategy for studying the effect on unbiased, nearest-neighbor walks of opening up or blocking a trap or neural site on a d-dimensional lattice.

In this paper we propose a form of semi-nonparametric regression based on wavelet analysis, and demonstrate its empirical utility in the context of commodity price behavior.

We show that, in many circumstances, the size distortion of a bootstrap P value for a test will be one whole order of magnitude smaller than that of the corresponding asymptotic P value. We also show that, at least in the parametric case, the magnitude of the distorsion will depend on the shape...

This paper proposes a new kind of asymmetric GARCh where the conditional variance obeys two different regimes with a smooth transition function. In one formulation variance reacts differently to negative and positive shocks while a second formulation, small and big shocks have separate effects.

We propose a test for spatial correlation in Probit models that is a joint test for exclusion of spatially lagged-dependent variables and absence of spatial-error correlation. We give a maximum-likelihood justification for the test but use a simulations approach rather than relying on its...

This paper considers the Bayesian analysis of threshold regression models. It shows that this analysis can be conducted with simple deterministic numerical integration in one or two dimensions. The shape of the posterior density is greatly determined by the type of threshold and of transition...