Some Finite Sample Results On Testing For Granger Noncausality
Judith Clarke () and
No 305, Econometrics Working Papers from Department of Economics, University of Victoria
We compare testing strategies for Granger noncausality in vector autoregressions (VARs)that may or may not have unit roots and cointegration. Sequential testing methods are examined; these test for cointegration and use either a differenced VAR or a vector error correction model(VECM), in which to undertake the main noncausality test. Basically, these strategies attempt to verify the validity of appropriate standard limit theory. We contrast such methods with an augmented lag approach that ensures the limiting Chi Square null distribution irrespective of the data’s nonstationarity characteristics. Our simulations involve bivariate and trivariate VARs in which we allow for the lag order to be selected by general to specific testing as well as by model selection criteria. We find that the current practice of pretesting for cointegration can result in severe over-rejections of the noncausal null while overfitting results in better control of the Type I error probability with often little loss in power.
Keywords: cointegration; error correction model; lag length selection; sequential testing; testing for Granger noncausality; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C32 C51 C52 (search for similar items in EconPapers)
Pages: 35 pages
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: ISSN 1485-6441
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0305
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