Tests for Long-Run Granger Non-Causality in Cointegrated Systems
AbstractIn this paper, we propose a new approach to test the hypothesis of long-run Granger non-causality in cointegrated systems. We circumvent the problem of singularity of the variance-covariance matrix associated with the usual Wald type test by proposing a generalized inverse procedure, and an alternative simple procedure which can be approximated by a suitable chi-square distribution. A test for the ranks of submatrices of the cointegration matrix and its orthogonal matrix plays a vital role in the former. The relevant small sample experiments indicate that the proposed method performs reasonably well in finite samples. As empirical applications, we examine long-run causal relations among long-term interest rates of three and five nations.
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Bibliographic InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Hi-Stat Discussion Paper Series with number d03-01.
Date of creation: Dec 2003
Date of revision:
Vector autoregression; Cointegration; Long-run causality; Hypothesis testing;
Other versions of this item:
- Taku Yamamoto & Eiji Kurozumi, 2006. "Tests for Long-Run Granger Non-Causality in Cointegrated Systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 703-723, 09.
- Yamamoto, Taku & Kurozumi, Eiji, 2003. "Tests for Long-Run Granger Non-Causality in Cointegrated Systems," Discussion Papers 2003-12, Graduate School of Economics, Hitotsubashi University.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-12-14 (All new papers)
- NEP-ECM-2003-12-14 (Econometrics)
- NEP-ETS-2003-12-14 (Econometric Time Series)
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