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Confidence Intervals in Regressions with Estimated Factors and Idiosyncratic Components

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)71-74
Early online date3 Jun 2017
Publication statusPublished - Aug 2017


King's Authors


This paper shows that HAC standard errors must be adjusted when constructing confidence intervals in regressions involving both the factors and idiosyncratic components estimated from a big dataset. This result is in contrast to the seminal result of Bai and Ng (2006) where the assumption that √T/N is sufficient to eliminate the effect of estimation error, where T and N are the time-series and cross-sectional dimensions. Simulations show vast improvements in the coverage rates of the adjusted confidence intervals over the unadjusted ones.

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