Spurious principal components
The principal component regression (PCR) 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.
|Keywords||pre-whitening, Principal component regression, spurious regressions|
|JEL||Model Evaluation and Testing (jel C52)|
|Persistent URL||dx.doi.org/10.1080/13504851.2018.1433292, hdl.handle.net/1765/104559|
|Series||Econometric Institute Reprint Series|
|Journal||Applied Economics Letters|
Franses, Ph.H.B.F, & Janssens, E. (2018). Spurious principal components. Applied Economics Letters, 26(1), 37–39. doi:10.1080/13504851.2018.1433292