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Bootstrap Inference in Regressions with Estimated Factors and Serial Correlation
Author(s) -
Djogbenou Antoine,
Gonçalves Sílvia,
Perron Benoit
Publication year - 2015
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12118
Subject(s) - mathematics , inference , statistics , confidence interval , residual , regression , context (archaeology) , bootstrap aggregating , econometrics , autocorrelation , regression analysis , standard error , algorithm , computer science , artificial intelligence , geography , archaeology
This article considers bootstrap inference in a factor‐augmented regression context where the errors could potentially be serially correlated. This generalizes results in Gonçalves & Perron (2014) and makes the bootstrap applicable to forecasting contexts where the forecast horizon is greater than one. We propose and justify two residual‐based approaches, a block wild bootstrap and a dependent wild bootstrap. Our simulations document improvement in coverage rates of confidence intervals for the coefficients when using block wild bootstrap or dependent wild bootstrap relative to both asymptotic theory and the wild bootstrap when serial correlation is present in the regression errors.