Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles
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Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles. / Franchi, Massimo; Johansen, Søren.
In: Econometrics, Vol. 5, No. 2, 14.06.2017, p. 1-20.Research output: Contribution to journal › Journal article › Research › peer-review
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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