Semiparametric Tests for the Order of Integration in the Possible Presence of Level Breaks

Fabrizio Iacone, Morten Ørregaard Nielsen, A.M. Robert Taylor

Publikation: Working paper/Preprint Working paperForskning

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Abstract

Lobato and Robinson (1998) develop semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or I(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First we show that it can be generalised to allow for testing of the null hypothesis that a series is I(ẟ) for any ẟ lying in the usual stationary and invertible region of the parameter space. The second extension is the more substantive and addresses the well known issue in the literature that long memory and level breaks can be mistaken for one another, with unmodelled level breaks rendering fractional integration tests highly unreliable. To deal with this inference problem we extend the Lobato and Robinson (1998) approach to allow for the possibility of changes in level at unknown points in the series. We show that the resulting statistics have standard limiting null distributions, and that the tests based on these statistics attain the same asymptotic local power functions as infeasible tests based on the unobserved errors, and hence there is no loss in asymptotic local power from allowing for level breaks, even where none is present. We report results from a Monte Carlo study into the finite-sample behaviour of our proposed tests, as well as several empirical examples.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider64
StatusUdgivet - 17 feb. 2021
NavnCREATES Research Paper
Nummer2021-04

Emneord

  • Fractional integration
  • Level breaks
  • Lagrange multiplier testing principle
  • Spurious long memory
  • Local Whittle likelihood
  • Conditional heteroskedasticity

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