Non-parametric transformation regression with non-stationary data

  • Oliver Linton
  • Qiying Wang

Published on 23 April 2013

This Working Paper is an update of Nonparametric transformation regression with non-stationary data

The authors examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008).

We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.

Authors

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Oliver Linton

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Qiying Wang

Working Paper details

DOI
10.1920/wp.cem.2013.1613
Publisher
Institute for Fiscal Studies

Suggested citation

Linton, O and Wang, Q. (2013). Non-parametric transformation regression with non-stationary data. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/non-parametric-transformation-regression-non-stationary-data (accessed: 19 April 2024).

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