consistently, the bootstrap sample size needs to be of smaller order than the original sample size. See Jun Shao and Dongsheng Tu … (1995), Ex. 3.9,p. 123. We show that the same is true if we use the bootstrap for estimating an intermediate quantile. …

a two step subsample bootstrap method. This method adaptively determines the sample fraction that minimizes the …

In this paper we introduce a bootstrap procedure to test parameter restrictions in vector autoregressive models which … is robust in cases of conditionally heteroskedastic error terms. The adopted wild bootstrap method does not require any … Monte Carlo investigation empirical size and power properties of the new method are illustrated. We compare the bootstrap …

institutional factors, one may expect convergence across business cycles. When these have similar characteristics as seasonal cycles …, one may perhaps also find convergence in seasonality. To this aim, we propose a new method, which is based on treating the … examine the hypothesis of convergence in deterministic seasonality while allowing for seasonal unit roots. We derive the …

also reveal a move to the common mid-point. Fractionally integrated time series models support the convergence thesis with … right-most and left-most party affiliates converging most rapidly. However, the convergence we find may be part of a … nonperiodic wave-like pattern were periods of convergence are alternated by periods of divergence. Future political conflicts may …

be used for modeling "average" or steady-state behavior of complex stochastic systems. Recently, simulation-based methods … programs with equilibrium constraints. The convergence analysis of sample-path methods rely heavily on stability conditions. We …-sure convergence. Alongside we provide a complementary sensitivity result for the corresponding deterministic problems. In addition, we …

are considered: controlled and uncontrolled. Simulation results show that uncontrolled arrivals of ships perform worst in …

We consider the Nelder and Mead Simplex Method for the optimization of stochastic simulation models. Existing and new …