Building confidence intervals for band-pass and Hodrick–Prescott filters: an application using bootstrapping

Building Confidence Intervals for the Band-pass and Hodrick-prescott Filters: an Application Using Bootstrapping

This article generates innovative confidence intervals for two of the most popular de-trending methods: the Hodrick-Prescott and Band-Pass filters. The confidence intervals are obtained using block-bootstrapping techniques for dependent data. As an example, we present GDP trend growth and output gap intervals for the G7 economies. This new concept can improve the utility and applications of the...

Higher-Order Confidence Intervals for Stochastic Programming using Bootstrapping

We study the problem of constructing confidence intervals for the optimal value of a stochastic programming problem by using bootstrapping. Bootstrapping is a resampling method used in the statistical inference of unknown parameters for which only a small number of samples can be obtained. One such parameter is the optimal value of a stochastic optimization problem involving complex spatio-temp...

Confidence intervals in QTL mapping by bootstrapping.

The determination of empirical confidence intervals for the location of quantitative trait loci (QTLs) was investigated using simulation. Empirical confidence intervals were calculated using a bootstrap resampling method for a backcross population derived from inbred lines. Sample sizes were either 200 or 500 individuals, and the QTL explained 1, 5, or 10% of the phenotypic variance. The method...

Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation

In many reinforcement learning applications executing a poor policy may be costly or even dangerous. Thus, it is desirable to determine confidence interval lower bounds on the performance of any given policy without executing said policy. Current methods for high confidence off-policy evaluation require a substantial amount of data to achieve a tight lower bound, while existing model-based meth...

Bootstrapping Confidence Intervals For Robust Measures Of Association