Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles

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Standard

Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles. / Franchi, Massimo; Johansen, Søren.

I: Econometrics, Bind 5, Nr. 2, 14.06.2017, s. 1-20.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Franchi, M & Johansen, S 2017, 'Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles', Econometrics, bind 5, nr. 2, s. 1-20. https://doi.org/10.3390/econometrics5020025

APA

Franchi, M., & Johansen, S. (2017). Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles. Econometrics, 5(2), 1-20. https://doi.org/10.3390/econometrics5020025

Vancouver

Franchi M, Johansen S. Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles. Econometrics. 2017 jun. 14;5(2):1-20. https://doi.org/10.3390/econometrics5020025

Author

Franchi, Massimo ; Johansen, Søren. / Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles. I: Econometrics. 2017 ; Bind 5, Nr. 2. s. 1-20.

Bibtex

@article{4bd799249d6441e285905604890b7d4a,
title = "Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles",
abstract = "It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper formulates a CVAR model allowing for multiple near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then two critical value adjustments suggested by McCloskey (2017) for the test on the cointegrating relations are implemented for the model with a single near unit root, and it is found by simulation that they eliminate the serious size distortions, with a reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs.",
keywords = "Faculty of Social Sciences, long-run inference, test on cointegrating relations, likelihood inference, vector autoregressive model, near unit roots, Bonferroni type adjusted quantiles",
author = "Massimo Franchi and S{\o}ren Johansen",
year = "2017",
month = jun,
day = "14",
doi = "10.3390/econometrics5020025",
language = "English",
volume = "5",
pages = "1--20",
journal = "Econometrics",
issn = "2225-1146",
publisher = "MDPI AG",
number = "2",

}

RIS

TY - JOUR

T1 - Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles

AU - Franchi, Massimo

AU - Johansen, Søren

PY - 2017/6/14

Y1 - 2017/6/14

N2 - It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper formulates a CVAR model allowing for multiple near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then two critical value adjustments suggested by McCloskey (2017) for the test on the cointegrating relations are implemented for the model with a single near unit root, and it is found by simulation that they eliminate the serious size distortions, with a reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs.

AB - It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper formulates a CVAR model allowing for multiple near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then two critical value adjustments suggested by McCloskey (2017) for the test on the cointegrating relations are implemented for the model with a single near unit root, and it is found by simulation that they eliminate the serious size distortions, with a reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs.

KW - Faculty of Social Sciences

KW - long-run inference

KW - test on cointegrating relations

KW - likelihood inference

KW - vector autoregressive model

KW - near unit roots

KW - Bonferroni type adjusted quantiles

U2 - 10.3390/econometrics5020025

DO - 10.3390/econometrics5020025

M3 - Journal article

VL - 5

SP - 1

EP - 20

JO - Econometrics

JF - Econometrics

SN - 2225-1146

IS - 2

ER -

ID: 193398265