Author: Mehmet Caner
Publisher:
ISBN:
Category :
Languages : en
Pages : 41

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Author: Myunghwan Seo
Publisher:
ISBN:
Category :
Languages : en
Pages : 31

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There is a growing literature on unit root testing in threshold autoregressive models. This paper makes two contributions to the literature. First, an asymptotic theory is developed for unit root testing in a threshold autoregression, in which the errors are allowed to be dependent and heterogeneous, and the lagged level of the dependent variable is employed as the threshold variable. The asymptotic distribution of the proposed Wald test is non-standard and depends on nuisance parameters. Second, the consistency of the proposed residual-based block bootstrap is established based on a newly developed asymptotic theory for this bootstrap. It is demonstrated by a set of Monte Carlo simulations that the Wald test exhibits considerable power gains over the ADF test that neglects threshold effects. The law of one price hypothesis is investigated among used car markets in the US.

Author: Frederique Bec
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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This paper proposes SupWald tests from a threshold autoregressive model computed with an adaptive set of thresholds. Simple examples of adaptive threshold sets are given. A second contribution of the paper is a general asymptotic null limit theory when the threshold variable is a level variable. We obtain a pivotal null limiting distribution under some simple conditions for bounded or asymptotically unbounded thresholds. Our general approach is flexible enough to allow a choice of the auxiliary threshold model or of the threshold set involved in the test specifically designed for nonlinear stationary alternatives relevant for macroeconomic and financial topics involving arbitrage in presence of transaction costs. A Monte-Carlo study and an application to the interest rates spread for French, German, New-Zealander and US post-1980 monthly data illustrate the ability of the adaptive SupWald tests to reject unit-root when the ADF does not.

Author: Mehmet Caner
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: George Kapetanios
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ISBN:
Category :
Languages : en
Pages : 0

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This paper proposes a simple direct testing procedure to distinguish a linear unit root process from a globally stationary three-regime self-exciting threshold autoregressive process. We derive the asymptotic null distribution of the Wald statistic, and show that it does not depend on unknown fixed threshold values. Monte Carlo evidence clearly indicates that the exponential average of the Wald statistic is more powerful than the Dickey-Fuller test that ignores the threshold nature under the alternative.

Author: Penelope A. Smith
Publisher:
ISBN: 9780734032218
Category : Mathematical statistics
Languages : en
Pages : 37

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Author: Jean-Yves Pitarakis
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ISBN:
Category :
Languages : en
Pages :

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Author: Robert Sollis
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ISBN:
Category :
Languages : en
Pages : 0

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Conventional Dickey-Fuller unit root tests have been generalized to allow for nonlinearity under the alternative hypothesis by Enders and Granger [Journal of Business Economics and Statistics, 16 (1998) 304] (EG) and Leybourne, Newbold and Vougas [Journal of Time Series Analysis, 19 (1998) 83] (LNV). EG focus on the case of asymmetric adjustment modelled as threshold autoregression, while LNV extend the concept of trend stationarity to that of stationarity around a smooth transition between deterministic linear trends. In this study, the EG and LNV methodologies are combined to develop tests of the null hypothesis of a unit root, that under the alternative hypothesis allow for stationary asymmetric adjustment around a smooth transition between deterministic linear trends. The empirical power of the combined tests is briefly investigated and an empirical application to time series on aggregate industrial production in the UK and the US is considered.

Author:
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Category :
Languages : en
Pages :

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The asymptotic distributions of Augmented-Dickey-Fuller (ADF) unit root tests for autoregressive processes with a unit or near-unit root are discussed in the presence of multiple stochastic level shifts of large size occurring independently in time. The distributions depend on a Brownian motion and a Poisson-type jump process. Due to the latter, tests based on standard critical values experience power losses increasing rapidly with the number and the magnitude of the shifts. A new approach to unit root testing is suggested which requires no knowledge of either the location or the number of level shifts, and which dispenses with the assumption of independent shift occurrence. It is proposed to remove possible shifts from a time series by weighting its increments according to how likely it is, with respect to an ad hoc postulated distribution, a shift to have occurred in each period. If the number of level shifts is bounded in probability, the limiting distributions of the proposed test statistics coincide with those of ADF statistics under standard conditions. A Monte Carlo experiment shows that, despite their generality, the new tests perform well in finite samples.

Author: Myunghwan Seo
Publisher:
ISBN:
Category :
Languages : en
Pages : 92

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