Template-Type: ReDIF-Paper 1.0
Author-Name: Franses, Ph.H.B.F.
Author-Name-Last: Franses
Author-Name-First: Philip Hans
Author-Person: pfr226
Author-Name: Janssens, E.
Author-Name-Last: Janssens
Author-Name-First: Eva
Title: Spurious Principal Components
Abstract: The Principal Component Regression is often used to forecast macroeconomic variables
 when there are many predictors. In this letter, we argue that it makes sense to pre-whiten the
 predictors before including these in a PCR. With simulation experiments, we show that
 without such pre-whitening, spurious principal components can appear, and that these can
 become spuriously significant in a PCR. With an illustration to annual inflation rates for five
 African countries, we show that non-spurious principal components can be genuinely relevant
 in empirical forecasting models.
Length: 9
Creation-Date: 2017-11-01
File-URL: https://repub.eur.nl/pub/102704/EI2017-31.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI2017-31
Classification-JEL: C52
Keywords: Principal Component Regression, Pre-whitening, Spurious Regressions
Handle: RePEc:ems:eureir:102704