Range Unit‐Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers

By F. Aparicio, Alvaro Escribano, A. Sipols. 2006

59 citations recorded by the source corpus.

Abstract.  Since the seminal paper by Dickey and Fuller in 1979, unit‐root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long‐wave patterns observed not only in unit‐root time series but also in series following more complex data‐generating mechanisms. To this end, our testing device analyses the unit‐root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit‐root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit‐root tests on near‐unit‐root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward–backward range unit‐root (FB‐RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey–Fuller unit‐root test on exchange rate series.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/498ab83d4425dd6513078bb9fac62cf5860b8755

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In Economics and Econometrics

1. Francesco D’Amuri
2. Juri Marcucci
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Citations in the corpus

• The Predictive Power of Google Searches in Forecasting Unemployment (2012, methodology): “We have thus performed the Range Unit Root test (RUR) suggested by Aparicio et al. (2006) which is a fully non-parametric unit-root test constructed using the running ranges of the series.”