Likelihood Based Testing for No Fractional Cointegration

Łasak, Katarzyna

doi:10.2139/ssrn.1266675

Cited by 7 publications

(11 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jeganathan (1999), Robinson and Hualde (2003), Lasak (2008Lasak ( , 2010, Avarucci and Velasco (2009), and Hualde and Robinson (2010). Speci…cally, in a vector autoregressive context, but in a model with d = 1 and a di¤erent lag structure from ours, Lasak (2010) analyzes a test for no cointegration and in Lasak (2008) she analyzes maximum likelihood estimation and inference; in both cases assuming "strong cointegration". In the same model as Lasak, but assuming "weak cointegration", Avarucci and Velasco (2009) extend the univariate test of Lobato and Velasco (2007) to analyze a Wald test for cointegration rank, see also Marmol and Velasco (2004).…”

Section: Introductionmentioning

confidence: 99%

“…Only recently, asymptotically optimal inference procedures have been developed for fractional processes, e.g. Jeganathan (1999), Robinson and Hualde (2003), Lasak (2008Lasak ( , 2010, Avarucci and Velasco (2009), and Hualde and Robinson (2010). Speci…cally, in a vector autoregressive context, but in a model with d = 1 and a di¤erent lag structure from ours, Lasak (2010) analyzes a test for no cointegration and in Lasak (2008) she analyzes maximum likelihood estimation and inference; in both cases assuming "strong cointegration".…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model

Johansen

Nielsen

Sommer³

2010

SSRN Journal

183

View full text Add to dashboard Cite

We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model based on the conditional Gaussian likelihood. The model allows the process X t to be fractional of order d and cofractional of order d b; that is, there exist vectors for which 0 X t is fractional of order d b:Our main technical contribution is the proof of consistency of the maximum likelihood estimators on the set 1=2 b d d 1 for any d 1 d 0 . To this end, we consider the conditional likelihood as a stochastic process in the parameters, and prove that it converges in distribution when errors are i.i.d. with suitable moment conditions and initial values are bounded. We then prove that the estimator of is asymptotically mixed Gaussian and estimators of the remaining parameters are asymptotically Gaussian. We also …nd the asymptotic distribution of the likelihood ratio test for cointegration rank, which is a functional of fractional Brownian motion of type II.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model

Johansen

Nielsen

Sommer³

2010

SSRN Journal

183

View full text Add to dashboard Cite

show abstract

“…We generate N = 1, 000 Monte Carlo replication from the following DGP For each run of the Monte Carlo, we fit the model To simplify the exposition, we introduce new notation for the elements of the matrices in the model (11). We call the elements of the matrices Instead the plot in Figure 2 shows that the distribution ofd when T = 10, 000 is uni-modal and symmetric as the distributions of all the other parameters.…”

Section: Simulation Experimentmentioning

confidence: 99%

On an Estimation Method for an Alternative Fractionally Cointegrated Model

Carlini

Łasak

2014

SSRN Journal

Self Cite

View full text Add to dashboard Cite

“…This is avoided in the algorithm of Robinson (2008), which consists of a sequence of Hausmantype tests using integration order estimates for observables, and is explicitly justified for both stationary and non-stationary observables. Lasak (2005) extended the parametric procedure of Johansen (1991) to fractional series, assuming the (common) integration order of the observables is known and equal to 1, but allowing that of the cointegrating errors (which is also assumed to be constant across all cointegrating relations) to be unknown.…”

Section: Final Commentsmentioning

confidence: 99%

Semiparametric inference in multivariate fractionally cointegrated systems

Hualde

Robinson

2010

Journal of Econometrics

View full text Add to dashboard Cite

a b s t r a c tA semiparametric multivariate fractionally cointegrated system is considered, integration orders possibly being unknown and I(0) unobservable inputs having nonparametric spectral density. Two estimates of the vector of cointegrating parameters ν are considered. One involves inverse spectral weighting and the other is unweighted but uses a spectral estimate at frequency zero. Both corresponding Wald statistics for testing linear restrictions on ν are shown to have a standard null χ 2 limit distribution under quite general conditions. Notably, this outcome is irrespective of whether cointegrating relations are ''strong'' (when the difference between integration orders of observables and cointegrating errors exceeds 1/2), or ''weak'' (when that difference is less than 1/2), or when both cases are involved. Finite-sample properties are examined in a Monte Carlo study and an empirical example is presented.

show abstract

Likelihood Based Testing for No Fractional Cointegration

Cited by 7 publications

References 48 publications

Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model

Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model

On an Estimation Method for an Alternative Fractionally Cointegrated Model

Semiparametric inference in multivariate fractionally cointegrated systems

Contact Info

Product

Resources

About