Convergence to Stochastic Integrals for Dependent Heterogeneous Processes

Hansen, Bruce E.

doi:10.1017/s0266466600013189

Cited by 257 publications

(204 citation statements)

References 21 publications

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“…It follows from (A:32), Lemma A.2 and Theorem 2.1 of Hansen (1992) Here the rst and second equalities follow from de nitions, the third one is obtained because These results can be established in exactly the same way as their counterparts in the proof of Theorem 2. Next consider the rst factor on the r.h.s.…”

mentioning

confidence: 64%

Testing for the Cointegrating Rank of a Var Process With An Intercept

2000

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Testing the cointegrating rank of a vector autoregressive process with an intercept is considered. In addition to the likelihood ratio (LR) tests developed by Johansen and Juselius and others we also consider an alternative class of tests which is based on estimating the trend parameters of the deterministic term in a di erent w ay. The asymptotic local power of these tests is derived and compared to that of the corresponding LR tests. The small sample properties are investigated by simulations. The new tests are seen to be substantially more powerful than conventional LR tests.We are grateful to the Deutsche Forschungsgemeinschaft, SFB 373, for nancial support and to Christian M uller and Kirstin Hubrich for performing the computations.

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confidence: 64%

Testing for the Cointegrating Rank of a Var Process With An Intercept

2000

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“…The proof makes use of the following convergence results, see Hansen (1992) and Hamilton (1994). We have…”

Section: Appendixmentioning

confidence: 99%

A new unit root test against ESTAR based on a class of modified statistics

2009

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“…The weak convergence of the first terms follows from the weak convergence to stochastic integrals for sample covariances of i.i.d. processes, convergence of the second term follows from Hansen (1992), while the weak convergence of the third and fourth terms follows from Caner (1997). Finally, rearranging terms gives the result in the text.…”

Section: Discussionmentioning

confidence: 99%

Asymptotic Null Distributions of Stationarity and Nonstationarity Tests Under Local-to-finite Variance Errors

Cappuccio

Lubian

2006

AISM

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The purpose of this paper is to investigate the asymptotic null distribution of stationarity and nonstationarity tests when the distribution of the error term belongs to the normal domain of attraction of a stable law in any finite sample but the error term is an i.i.d. process with finite variance as T ↑ ∞. This local-to-finite variance setup is helpful to highlight the behavior of test statistics under the null hypothesis in the borderline or near borderline cases between finite and infinite variance and to assess the robustness of these test statistics to small departures from the standard finite variance context. From an empirical point of view, our analysis can be useful in settings where the (non)-existence of the (second) moments is not clear-cut, such as, for example, in the analysis of financial time series. A Monte Carlo simulation study is performed to improve our understanding of the practical implications of the limi theory we develop. The main purpose of the simulation experiment is to assess the size distortion of the unit root and stationarity tests under investigation.

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Convergence to Stochastic Integrals for Dependent Heterogeneous Processes

Cited by 257 publications

References 21 publications

Testing for the Cointegrating Rank of a Var Process With An Intercept

Testing for the Cointegrating Rank of a Var Process With An Intercept

A new unit root test against ESTAR based on a class of modified statistics

Asymptotic Null Distributions of Stationarity and Nonstationarity Tests Under Local-to-finite Variance Errors

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