Journal ArticleDOI
Finite sample performance of likelihood ratio tests for cointegrating ranks in vector autoregressions
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In this paper, the authors investigate the finite sample properties of likelihood ratio tests for cointegrating ranks and conclude that 100 observations are not sufficient to ensure reasonably good performance uniformly over the values of the nuisance parameters that affect the distributions of the test statistics.Abstract:
This paper investigates through Monte Carlo simulation the finite sample properties of likelihood ratio tests for cointegrating ranks that were proposed by Johansen (1991, Econometrica 59, 1551-1580). We transform the model into a canonical form so that the experiment is well controlled without loss of generality and then conduct a comprehensive simulation study. As expected, the test performance is very sensitive to the value of the stationary root(s) of the process. We also find that the test performance depends crucially on the correlation between the innovations that drive the stationary and the nonstationary components of the process. We conclude that 100 ob.servations are not sufficient to ensure reasonably good performance uniformly over the values of the nuisance parameters that affect the distributions of the test statistics.read more
Citations
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A small sample correction for the test of cointegrating rank in the vector autoregressive model
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References
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Journal ArticleDOI
Statistical analysis of cointegration vectors
TL;DR: In this paper, the authors consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors, and derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions.
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Maximum likelihood estimation and inference on cointegration — with applications to the demand for money
Søren Johansen,Katarina Juselius +1 more
TL;DR: In this paper, the estimation and testing of long-run relations in economic modeling are addressed, starting with a vector autoregressive (VAR) model, the hypothesis of cointegration is formulated as a hypothesis of reduced rank of the long run impact matrix.
Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models / Søren Johansen
TL;DR: In this paper, the authors present the likelihood methods for the analysis of cointegration in VAR models with Gaussian errors, seasonal dummies, and constant terms, and show that the asymptotic distribution of the maximum likelihood estimator is mixed Gausssian.
Journal ArticleDOI
Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models
TL;DR: In this article, the authors derived the likelihood analysis of vector autoregressive models allowing for cointegration and showed that the asymptotic distribution of the maximum likelihood estimator of the cointegrating relations can be found by reduced rank regression and derives the likelihood ratio test of structural hypotheses about these relations.
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A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics1
TL;DR: In this paper, the authors present an examination of such models for variables integrated at most of order one, when tests for cointegration involve statistics with non-standard asymptotic distributions.