, Volume 76, Issue 6, pp 733–764 | Cite as

Local block bootstrap inference for trending time series

  • Arif Dowla
  • Efstathios Paparoditis
  • Dimitris N. Politis


Resampling for stationary sequences has been well studied in the last couple of decades. In the paper at hand, we focus on nonstationary time series data where the nonstationarity is due to a slowly-changing deterministic trend. We show that the local block bootstrap methodology is appropriate for inference under this locally stationary setting without the need of detrending the data. We prove the asymptotic consistency of the local block bootstrap in the smooth trend model, and complement the theoretical results by a finite-sample simulation.


Bootstrap Dependent data Kernel smoothing Local stationarity Regression 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arif Dowla
    • 1
  • Efstathios Paparoditis
    • 2
  • Dimitris N. Politis
    • 3
  1. 1.Stochastic Logic Ltd.DhakaBangladesh
  2. 2.Department of Mathematics and StatisticsUniversity of Cyprus NicosiaCyprus
  3. 3.Department of MathematicsUniversity of CaliforniaSan Diego, La JollaUSA

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