Using the Bootstrap to Test for Symmetry Under Unknown Dependence

A-Tier
Journal: Journal of Business & Economic Statistics
Year: 2016
Volume: 34
Issue: 3
Pages: 406-415

Score contribution per author:

4.022 = (α=2.01 / 1 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

This article considers tests for symmetry of the one-dimensional marginal distribution of fractionally integrated processes. The tests are implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the relevant test statistics. The sieve bootstrap allows inference on symmetry to be carried out without knowledge of either the memory parameter of the data or of the appropriate norming factor for the test statistic and its asymptotic distribution. The small-sample properties of the proposed method are examined by means of Monte Carlo experiments, and applications to real-world data are also presented.

Technical Details

RePEc Handle
repec:taf:jnlbes:v:34:y:2016:i:3:p:406-415
Journal Field
Econometrics
Author Count
1
Added to Database
2026-01-29