Size and Power of the Likelihood Ratio Test for Seasonal Cointegration in Small Samples: A Monte Carlo Study PDF Download
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Languages : en
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The Stockholm School of Economics presents an abstract for the paper entitled "Size and Power of the Likelihood Ratio Test for Seasonal Cointegration in Small Samples: A Monte Carlo Study," by Marten Lof. The article discusses the small sample size and power properties of the likelihood ratio test in the seasonal error correction model.
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Languages : en
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The Stockholm School of Economics presents an abstract for the paper entitled "Size and Power of the Likelihood Ratio Test for Seasonal Cointegration in Small Samples: A Monte Carlo Study," by Marten Lof. The article discusses the small sample size and power properties of the likelihood ratio test in the seasonal error correction model.
Author: Phoebus J. Dhrymes
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Languages : en
Pages : 0
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This paper reports on the results of a Monte Carlo study. The latter investigates the performance of various versions of the Conformity test CCT$ for the existence and rank of cointegration, as given in Dhrymes (1996b), the likelihood ratio test LRT as given in Johansen (J) (1988), (1991), and the stochastic trends test (SW), as given in Stock and Watson (1988). The design of the experiments allows for small, medium and large stationary roots, and one, two, and three unit roots. The largest system investigated is a quadrivariate VAR(4). Results based on the underlying normal theory indicate that the performance of the CCT is extremely good when the null hypothesis involves the sum of, or individual, (characteristic) roots, some of which are not zero; it does not perform reliably when the sum of the roots under the null involves, in truth,all zero roots. Results based on non-standard asymptotic theory for estimators of zero roots indicate that the CCT has very good power characteristics in detecting the rank of cointegration, but it exhibits some size distortions that can potentially lead to overestimation of the true cointegrating rank. On the other hand, both versions are robust to non normal and dependent error structures. Such results generally hold for sample sizes 100 and 500. For samples of size 100, the LR test performs quite well, in terms of size, when the error process is Gaussian and when small and medium stationary roots are employed in the experimental design, but performs rather poorly in terms of power. The problem is magnified with large stationary roots, and/or non-normal errors. The results improve, as expected, for sample size 500. The SW test performs rather poorly overall, and cannot be recommended for use in empirical applications.
Author: Phoebus James Dhrymes
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ISBN:
Category :
Languages : en
Pages : 54
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Author: Hector O. Zapata
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ISBN:
Category : Agriculture
Languages : en
Pages : 26
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"The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error- correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples."--Page 3.
Author: Johan Baras
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Languages : en
Pages : 169
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Author: Johan Baras
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Category :
Languages : en
Pages : 169
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Author: John R. Boulet
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Languages : en
Pages : 0
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The present paper demonstrates how Structural Equation Modelling (SEM) can be used to formulate a test of the difference in means between groups on a number of dependent variables. A Monte Carlo study compared the Type I error rates of the Likelihood Ratio (LR) Chi-square ($\chi\sp2$) statistic (SEM test criterion) and Hotelling's two-sample T$\sp2$ statistic (MANOVA test criterion) in detecting differences in means between two independent samples. Seventy-two conditions pertaining to average sample size ((n$\sb1$ + n$\sb2$)/2), extent of inequality of sample sizes (n$\sb1$:n$\sb2$), number of variables (p), and degree of inequality of variance-covariance matrices ($\Sigma\sb1$:$\Sigma\sb2$) were modelled. Empirical sampling distributions of the LR $\chi\sp2$ statistic and Hotelling's T$\sp2$ statistic consisted fo 2000 samples drawn from multivariate normal parent populations. The actual proportion of values that exceeded the nominal levels are presented. The results indicated that, in terms of maintaining Type I error rates that were close to the nominal levels, the LR $\chi\sp2$ statistic and Hotelling's T$\sp2$ statistic were comparable when $\Sigma\sb1$ = $\Sigma\sb2$ and (n$\sb1$ + n$\sb2$)/2:p was relatively large (i.e., 30:1). However, when $\Sigma\sb1$ = $\Sigma\sb2$ and (n$\sb1$ + n$\sb2$)/2:p was small (i.e., 10:1) Hotelling's T$\sp2$ statistic was preferred. When $\Sigma\sb{1} \not=\Sigma\sb2$ the LR $\chi\sp2$ statistic provided more appropriate Type I error rates under all of the simulated conditions. The results are related to earlier findings, and implications for the appropriate use of the SEM method of testing for group mean differences are noted.
Author: G. S. Maddala
Publisher: Cambridge University Press
ISBN: 9780521587822
Category : Business & Economics
Languages : en
Pages : 528
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A comprehensive review of unit roots, cointegration and structural change from a best-selling author.
Author: Alfred A. Haug
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Category :
Languages : en
Pages : 0
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The effect of time-aggregation on the power of commonly used tests for cointegration is studied with the Monte Carlo method. The results suggest that, for a given span, a higher frequency of observation can add substantially to test power. Also, Engle and Granger's (1987) ADF test leads overall to the highest and most stable powers for typical finite sample sizes and likely data generating processes encountered by practitioners.
Author: Mika Karjalainen
Publisher:
ISBN: 9789516506596
Category :
Languages : en
Pages : 39
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