Cointegration analysis in the presence of outliers

Nielsen, Heino Bohn

doi:10.1111/j.1368-423x.2004.00130.x

Cited by 61 publications

(51 citation statements)

References 27 publications

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“…Because of this, the standard asymptotic trace test distributions (for example, provided by CATS for RATS) are no longer correct. The critical values given in brackets below the test values have been kindly provided by Heino Nielsen using a simulation program described in Nielsen (2004): see also Kurita (2007). The inclusion of a broken linear trend in the cointegration relations shifts the distributions to the right, implying that the test will be undersized if one ignores the e¤ect of the broken trend.…”

Section: Determining the Two Rank Indicesmentioning

confidence: 99%

The Long Swings Puzzle: What the Data Tell When Allowed to Speak Freely

Jusélius¹

2009

Palgrave Handbook of Econometrics

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The persistent movements away from long-run benchmark values in real exchange rates often observed in periods of currency ‡oat have been subject to much empirical research without resolving the underlying theoretical puzzle. This chapter demonstrates how the Cointegrated VAR approach of grouping together components of similar persistence can be used to uncover structures in the data that ultimately may help to explain theoretically the forces underlying such puzzling movements. The charaterization of the data into components which are empirically I(0), I(1) and I(2) is shown to be a powerful organizing principle, allowing us to structure the data into long-run, medium-run, and short-run behavior. Its main advantage is the ability to associate persistent movements away from fundamental benchmark values in one variable/relation with similar persistent movements somewhere else in the economy.JEL: C32, C50, F41

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Section: Determining the Two Rank Indicesmentioning

confidence: 99%

The Long Swings Puzzle: What the Data Tell When Allowed to Speak Freely

Jusélius¹

2009

Palgrave Handbook of Econometrics

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“…In this case the term ω 1 is entirely due to the inconsistent estimation of σ 2 ε . The difference from the general expressions (6) sheds light on why additive and innovational shifts have different effects on finite-sample testing (see, e.g., Nielsen, 2004 Remark 2.11. In Theorem 1 it is imposed that the possible level shifts all have the same size η.…”

Section: Model and Preliminary Resultsmentioning

confidence: 99%

Testing for Unit Roots in Autoregressions With Multiple Level Shifts

Cavaliere

Georgiev

2007

Econ. Theory

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The asymptotic distributions of Augmented-Dickey-Fuller (ADF) unit root test statistics for autoregressive processes with a unit or near-unit root are derived in the presence of multiple stochastic level shifts of large size. The distributions depend on a Brownian motion and a Poisson-type jump process. The latter is related to the occurrence of level shifts, and induces for tests based on standard critical values power losses increasing rapidly with the number and the magnitude of the shifts. A new approach to unit root testing is suggested which does not require to know either the location or the number of level shifts. It is proposed to remove possible shifts from a time series by weighting each of its increments according to how likely it is, with respect to an ad hoc postulated distribution, a shift to have occurred in the respective period. Hence, no explicit decision rule on the occurrence of shifts is required. It is proved that if the number of level shifts is bounded in probability, the proposed test statistics have pivotal limiting distributions coinciding with those of the corresponding ADF statistics under standard conditions. A Monte Carlo experiment shows that, despite their generality, the new tests perform well in small samples.

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“…The additive formulation has been analyzed by, e.g., Lütkepohl and Saikkonen (2000a,b,c), Nielsen (2004Nielsen ( , 2007, and Trenkler, Saikkonen, and Lütkepohl (2007); each for speci…c choices of deterministic terms.…”

Section: The Additive Formulationmentioning

confidence: 99%

The Cointegrated Vector Autoregressive Model with General Deterministic Terms

Johansen

Nielsen

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive uni…ed treatment of deterministic terms in the additive model X t = Z t + Y t , where Z t belongs to a large class of deterministic regressors and Y t is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are 2 -distributed.

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Cointegration analysis in the presence of outliers

Cited by 61 publications

References 27 publications

The Long Swings Puzzle: What the Data Tell When Allowed to Speak Freely

The Long Swings Puzzle: What the Data Tell When Allowed to Speak Freely

Testing for Unit Roots in Autoregressions With Multiple Level Shifts

The Cointegrated Vector Autoregressive Model with General Deterministic Terms

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