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Asymptotic normal and bootstrap inference in structural VAR analysis
Author(s) -
Fachin Stefano,
Bravetti Luca
Publication year - 1996
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199607)15:4<329::aid-for610>3.0.co;2-w
Subject(s) - inference , monte carlo method , parametric statistics , mathematics , asymptotic distribution , confidence interval , asymptotic analysis , statistics , statistical inference , variance (accounting) , econometrics , computer science , artificial intelligence , estimator , business , accounting
Abstract The aim of the paper is to examine the performance of bootstrap and asymptotic parametric inference methods in structural VAR analysis. The results obtained through a Monte Carlo experiment suggest that the two approaches are largely equivalent in most, but not all, cases. While the asymptotic method turns out to be surprisingly robust with respect to the distribution of the errors, the bootstrap does deliver results superior in terms of both length of the confidence interval and coverage when highly non‐linear statistics (such as the components of the variance of the forecast error) are considered.