This paper explores the potential of bootstrap methods in the empirical evaluation of dynamic stochastic general equilibrium (DSGE) models and, more generally, in linear rational expectations models featuring unobservable (latent) components. We consider two dimensions. First, we provide mild...

Kernel-based estimators are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals may not have correct coverage, even if the estimator is...

Research shows that the predictive ability of economic fundamentals for exchange rates is time-varying; it may be detected in some periods and disappear in others. This paper uses bootstrap-based methods to uncover the time-specific conditioning information for predicting exchange rates....

In this paper, we provide a novel way to estimate the out-of-sample predictive ability of a trading rule. Usually, this ability is estimated using a sample-splitting scheme, true out-of-sample data being rarely available. We argue that this method makes poor use of the available data and creates...

The purpose of this investigation is to research the causal link between economic policy uncertainty (EPU) and the housing returns (HR) in Germany. In the estimated vector autoregressive models, we test its stability and find the short-run relationship between HR and EPU is unstable. As a...

This paper explores a bootstrap modelling approach to estimating the value of mobile, spectrum and data demand. A mobile data forecast is not required, since data growth is modelled endogenously. The approach also dispenses with the notion of a “spectrum crunch” since data demand responds to...