A local linear estimator of generalized impulse response (GIR) functions for nonlinear conditional heteroskedastic autoregressive processes is derived and shown to be asymptotically normal. A plug-in bandwidth is obtained that minimizes the asymptotical mean squared error of the GIR estimator. A...

In this paper, linear errors-in-response models are considered in the presence of validation data on the responses. A semiparametric dimension reduction technique is employed to define an estimator of Ø with asymptotic normality, the estimated empirical loglikelihoods and the adjusted empirical...

Applied researchers often need to estimate confidence intervals for functions of parameters, such as the effects of counterfactual policy changes. If the function is continuously differentiable and has non-zero and bounded derivatives, then they can use the delta method. However, if the function...

This paper evaluates the precision of the parametric double lognormal (DLN) and the nonparametric smoothing spline method (SPLINE) for estimating risk-neutral distributions (RNDs) from observed option prices. By using a bootstrap technique confidence bands are estimated for the riskneutral...

This paper deals with the question how to model health effects after the cessation of a randomised controlled trial (RCT). Using clinical trial data on severe congestive heart failure patients we illustrate how survival beyond the cessation of a RCT can be predicted based on parametric survival...

D. N. McCloskey and Stephen Ziliak have criticized economists and others for confounding statistical and substantive significance, and for committing the logical error of the transposed conditional. In doing so they sometimes misinterpret the function of significance tests. Nonetheless,...

The delta method is commonly used to calculate confidence intervals of functions of estimated parameters that are differentiable with non-zero, bounded derivatives. When the delta method is inappropriate, researchers usually first use a bootstrap procedure where they i) repeatedly take a draw...

This paper extends Imbens and Manski's (2004) analysis of confidence intervals for interval identified parameters. For their final result, Imbens and Manski implicitly assume superefficient estimation of a nuisance parameter. This appears to have gone unnoticed before, and it limits the result's...

Bayesian decisions are observationally identical to decisions with judgment. Decisions with judgment test whether a judgmental decision is optimal and, in case of rejection, move to the closest boundary of the confidence interval, for a given confidence level. The resulting decisions condition...

We use past forecast errors to construct confidence intervals around Australian Government Budget forecasts of key economic and fiscal variables. These confidence intervals provide an indication of the extent of uncertainty around the point estimate forecasts presented in the Budget.