We analyze tidal data from Port Mansseld, TX using Kunsch's (1989) blockwise bootstrap in the regression setting. In particular, we estimate the variability of parameter estimates in a harmonic analysis via block subsampling of residuals from a least squares t. We see that naive least squares variance estimates can be either too large or too small depending on the strength of correlation and th...

In an earlier paper Rakonczai et al. (2014), we have emphasized the effective sample size for autocorrelated data. The simulations were based on the block bootstrap methodology. However, the discreteness of the usual block size did not allow for exact calculations. In this paper we propose a generalisation of the block bootstrap methodology, relate it to the existing optimisation procedures and...

Value at Risk (VaR) has become one of the most commonly used measures of risk for …nancial risk management. Econometrically, a suitable conditional quantile model can provide accurate estimation for this purpose. However due to the special dependence features of …nancial time series, a classical econometric methodology does not lend itself for this purpose . In this paper, the main objective is...

In this paper, we show the first order validity of the block bootstrap in the context of Kolmogorov type conditional distribution tests when there is dynamic misspecification and parameter estimation error. Our approach differs from the literature to date because we construct a bootstrap statistic that allows for dynamic misspecification under both hypotheses. We consider two test statistics; o...

The parametric model introduced by Lee and Carter in 1992 for modeling mortality rates the USA was a seminal development forecasting life expectancies has been widely used since then. Different extensions of this model, using different hypotheses about data, constraints on parameters, appropriate methods have led to improvements model’s fit historical data future. This paper’s main objective is...

A nonparametric, residual-based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series. The resampling procedure is based on weak assumptions on the dependence structure of the stationary process driving the random walk and successfully generates unit root integrated pseudo-series retaining the important characteristics of the data. It is more gen...

Subsampling and block-based bootstrap methods have been used in a wide range of inference problems for time series. To accommodate the dependence, these resampling methods involve a bandwidth parameter, such as the subsampling window width and block size in the block-based bootstrap. In empirical work, using different bandwidth parameters could lead to different inference results, but tradition...

Abstract Subsampling and block-based bootstrap methods have been used in a wide range of inference problems for time series. To accommodate the dependence, these resampling methods involve a bandwidth parameter, such as subsampling window width and block size in the block-based bootstrap. In empirical work, using different bandwidth parameters could lead to different inference results, but the ...

Our objectives in this paper are twofold. First, we introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out-of-sample nonlinear Granger causality, and in the other we ou...

In this paper, we propose a model-free bootstrap method for the empirical process under absolute regularity. More precisely, consistency of an adapted version of the so-called dependent wild bootstrap, that was introduced by Shao (2010) and is very easy to implement, is proved under minimal conditions on the tuning parameter of the procedure. We apply our results to construct confidence interva...