Testing for non-causality by using the Autoregressive Metric
AbstractA new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in nite samples. The empirical relevance of the test is illustrated via two applications.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 29637.
Date of creation: 2011
Date of revision:
AR metric; Bootstrap test; Granger non-causality; VAR;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-03-26 (All new papers)
- NEP-ECM-2011-03-26 (Econometrics)
- NEP-ETS-2011-03-26 (Econometric Time Series)
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