@TechReport{dp-575,
  title         = {The Influence of Additive Outliers on the Performance of
                  Information Criteria to Detect Nonlinearity},
  author        = {Rinke, Saskia},
  astring       = {Saskia Rinke},
  year          = {2016},
  month         = {April},
  number        = {575},
  size          = {202},  
  language      = {en},
  pages         = {16},
  jelclass      = {C15, C22},
  abstract      = {In this paper the performance of information criteria and
                  a test against SETAR nonlinearity for outlier contaminated
                  time series are investigated. Additive outliers can
                  seriously influence the properties of the underlying time
                  series and hence of linearity tests, resulting in spurious
                  test decisions of nonlinearity. Using simulation studies,
                  the performance of the information criteria SIC and WIC as
                  an alternative to linearity tests are assessed in time
                  series with different degrees of persistence and different
                  outlier magnitudes. For uncontaminated series and a small
                  sample size the performance of SIC and WIC is similar to
                  the performance of the linearity test at the $5\%$ and
                  $10\%$ significance level, respectively. For an increasing
                  number of observations the size of SIC and WIC tends to
                  zero. In contaminated series the size of the test and of
                  the information criteria increases with the outlier
                  magnitude and the degree of persistence. SIC and WIC
                  clearly outperform the test in larger samples and larger
                  outlier magnitudes. The power of the test and of the
                  information criteria depends on the sample size and on the
                  difference between the regimes. The more distinct the
                  regimes and the larger the sample, the higher is the power.
                  Additive outliers decrease the power in distinct regimes in
                  small samples and in intermediate regimes in large samples,
                  but increase the power in similar regimes. Due to their
                  higher robustness in terms of size, information criteria
                  are a valuable alternative to linearity tests in outlier
                  contaminated time series.},
  keywords      = {Additive Outliers, Nonlinear Time Series, Information
                  Criteria, Linearity Test, Monte Carlo}
}