often not provided. If confidence intervals are given they are often based on bootstrap methods with poor theoretical …. Special emphasis is placed on systems with cointegrated variables. -- monetary policy ; bootstrap ; impulse response ; money …

use quantities derived from the parameters to disentangle the relationships between the variables. Bootstrap methods are … often used for inference on the derived quantities. Alternative bootstrap methods for this purpose are discussed, some … problems are presented. -- Impulse response ; bootstrap ; vector autoregression ; confidence interval …

conditional heteroskedasticity. We show that the wild bootstrap provides convenient critical values for the considered OLS …-based statistics under both homoskedastic and conditionally heteroskedastic model errors. The wild bootstrap is easy to implement and … approximations. We prove further that the wild bootstrap retains its validity for inference within a system of pooled equations …

problem of online estimation of current values of w = w(T) and a = a(T) from the observations SI , ... ,ST. We propose an … adaptive method of estimation which does not use any information about time homogeneity of the obscured process. We apply this …

alternative for modelling financial data exhibiting skewness and fat tails. In this paper we explore the Bayesian estimation of …

factors representing "shift" and "curvature" of the term structure of "at the money" DAX options. We present a risk management …

bootstrap resampling technique. The method is illustrated on S&P 500 index data. -- Identification, Bootstrap, Diffusion …

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very … restricted by parametric assumptions, require more data and are less efficient. A third possible estimation approach is based on … ; limited-dependent-variable models ; nonlinear regression ; robust estimation …