Search Results - subject:"Bootstrap approach"

Data-Mining Bootstrap Procedure with Potential Predictors in Forecasting Models : Evidence from Eight Countries in the Asia-Pacific Stock Markets

Lee, Hojin - 2020

We use a data-mining bootstrap procedure to investigate the predictability test in the eight Asia-Pacific regional stock markets using in-sample and out-of-sample forecasting models. We address ourselves to the data-mining bias issues by using the data-mining bootstrap procedure proposed by...

Persistent link: https://www.econbiz.de/10012844506

Bootstrapping the Early Exercise Boundary in the Least-Squares Monte Carlo Method

Stentoft, Lars - 2020

This paper proposes an innovative algorithm that significantly improves on the approximation of the optimal early exercise boundary obtained with simulation based methods for American option pricing. The method works by exploiting and leveraging the information in multiple cross sectional...

Persistent link: https://www.econbiz.de/10012846097

Arbitrary Spearman’s Rank Correlations in Maximum Entropy Bootstrap and Improved Monte Carlo Simulations

Vinod, Hrishikesh D. - 2020

The R package for maximum entropy bootstrap (meboot) is widely used for numerous applications involving statistical inference for time series data without having to do differencing or de-trending. We report some simulations confirming its effectiveness. It has been used for simulating time...

Persistent link: https://www.econbiz.de/10012831864

Pull Your Small Area Estimates Up by the Bootstraps

Corral Rodas, Paul Andres - 2020

(2010) by considering heteroscedasticity and includes survey weights, but uses a different bootstrap approach, here referred … (2010) provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased point … as in Van der Weide (2014); and 2) to adapt the parametric bootstrap approach for mean squared error (MSE) estimation …

Persistent link: https://www.econbiz.de/10012833369

An Introduction to Bootstrap Theory in Time Series Econometrics

Cavaliere, Giuseppe - 2020

This article provides an introduction to methods and challenges underlying application of the bootstrap in econometric modelling of economic and financial time series. Validity, or asymptotic validity, of the bootstrap is discussed as this is a key element in deciding whether the bootstrap is...

Persistent link: https://www.econbiz.de/10012835479

The Numerical Bootstrap

Hong, Han - 2020

This paper proposes a numerical bootstrap method that is consistent in many cases where the standard bootstrap is known to fail and where the m-out-of-n bootstrap and subsampling have been the most commonly used inference approaches. We provide asymptotic analysis under both fixed and drifting...

Persistent link: https://www.econbiz.de/10012856218

Efficient Bootstrap Forecast Intervals for Return and Volatility Using the Linear Estimator of Arch Models

Iqbal, Farhat - 2020

In this paper, we construct efficient forecast intervals for autoregressive conditional heteroscedastic (ARCH) models using the bootstrap. Forecast intervals for returns and volatility are constructed using the linear estimator (LE) for ARCH model. An advantage of LE over the widely used quasi...

Persistent link: https://www.econbiz.de/10012856558

Empirical Tail Copulas for Functional Data

Einmahl, John H. J. - 2020

For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the dependence in the upper tail. The empirical versions of these functions are rank-based estimators whose inflated...

Persistent link: https://www.econbiz.de/10012842451

Testing for the appropriate level of clustering in linear regression models

MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, … - 2020

Reliable inference with clustered data has received a great deal of attention in recent years. The overwhelming majority of this research assumes that the cluster structure is known. This assumption is very strong, because there are often several possible ways in which a dataset could be...

Persistent link: https://www.econbiz.de/10012201366

Bootstrapping quasi likelihood ratio tests under misspecification

Bertail, Patrice; Lavergne, Pascal - 2020

Persistent link: https://www.econbiz.de/10012216207