Bias correction for time series factor models Articles uri icon

publication date

  • February 2018

start page

  • 1576

end page

  • 1602

issue

  • 8

volume

  • 88

international standard serial number (ISSN)

  • 0094-9655

electronic international standard serial number (EISSN)

  • 1563-5163

abstract

  • In this paper we work with multivariate time series that follow a Factor Model. In particular, we consider the setting where factors are dominated by highly persistent AutoRegressive (AR) processes and samples that are rather small. Therefore, the factors' AR models are estimated using small sample bias correction techniques. A Monte Carlo study reveals that bias-correcting the AR coefficients of the factors allows to obtain better results in terms of prediction interval coverage. As expected, the simulation shows that bias-correction is more successful for smaller samples. We present the results assuming the AR order and number of factors are known as well as unknown. We also study the advantages of this technique for a set of Industrial Production Indexes of several European countries.

keywords

  • dimensionality reduction; small sample bias correction; autoRegressive models; persistent process; factor model