A general framework for testing the Granger noncausality hypothesis
AbstractIn this paper, new noncausality tests relying on a general nonlinear framework are proposed and their performance studied by a Monte Carlo experiment and a variety of nonlinear artificial series. Two of the tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. Yet another test is based on artificial neural networks. The tests appear to be well-sized and have good power properties.
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Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 343.
Length: 22 pages
Date of creation: 09 Nov 1999
Date of revision:
Publication status: Published as Péguin-Feissolle, Anne, Timo Teräsvirta and Birgit Strikholm, 'Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. ' in Communications in Statistics – Simulation and Computation 42, 1063-1087, 2013, pages 1063-1087.
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Hypothesis testing; causality; artificial neural networks; nonlinearity;
Other versions of this item:
- Peguin-Feissolle, A. & Terasvirta, T., 1999. "A General Framework for Testing the Granger Noncausality Hypothesis," G.R.E.Q.A.M. 99a42, Universite Aix-Marseille III.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2000-01-24 (All new papers)
- NEP-ECM-2000-01-24 (Econometrics)
- NEP-ETS-2000-01-24 (Econometric Time Series)
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