Results 221 to 230 of about 206,208 (255)
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Journal of Economic Dynamics and Control, 1980
Abstract A general definition of causality is introduced and then specialized to become operational. By considering simple examples a number of advantages, and also difficulties, with the definition are discussed. Tests based on the definitions are then considered and the use of post-sample data emphasized, rather than relying on the same data to fit
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Abstract A general definition of causality is introduced and then specialized to become operational. By considering simple examples a number of advantages, and also difficulties, with the definition are discussed. Tests based on the definitions are then considered and the use of post-sample data emphasized, rather than relying on the same data to fit
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Are causal relationships sensitive to causality tests?
Applied Economics, 1987(1987). Are causal relationships sensitive to causality tests? Applied Economics: Vol. 19, No. 4, pp. 459-465.
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Prefiltering and Causality Tests
1982If data series are not filtered properly prior to the construction of a test of causality, the resulting test statistics are invalid This article,describes a general approach to data filtering based on the estimation of autoregressive-moving-average models and on specific tests for the Identification of white noise processes For selected examples ...
Belongia, Michael T. +3 more
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The Impossibility of Causality Testing
1984Causality tests developed by Sims and Granger are fatally flawed for several reasons First, when two variables, X and Y, are uncorrelated, X has no linear predictive value for Y, but X,and Y may be nonlinearly related unless they are statistically Independent, In which case X and Y are not related at all The light-hand side variables In a regression ...
Conway, Roger K. +3 more
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Multivariate Linear and Non-Linear Causality Tests
SSRN Electronic Journal, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhidong Bai +2 more
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2019
The investigation of causal relationships is of central concern to science. The proof of causality requires a number of typical features and conditions. There are different types of causal relationships, in particular direct, indirect, and moderated causal relationships.
Martin Eisend, Alfred Kuss
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The investigation of causal relationships is of central concern to science. The proof of causality requires a number of typical features and conditions. There are different types of causal relationships, in particular direct, indirect, and moderated causal relationships.
Martin Eisend, Alfred Kuss
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Surrogate-based test for Granger causality
2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), 2003An approach for testing the presence of Granger causality between two time series is proposed. The residue of the destination signal after self-prediction is computed, after which a cross-prediction of the source signal over this residue is examined. In the absence of causality, there should be no cross-predictive power, due to which the performance of
Temujin Gautama, Marc M. Van Hulle
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OUT-OF-SAMPLE TESTS FOR GRANGER CAUSALITY
Macroeconomic Dynamics, 2001Clive W.J. Granger has summarized his personal viewpoint on testing for causality in numerous articles over the past 30 years and has outlined what he considers to be a useful operational version of his original definition of Granger causality, which he notes is partially alluded to in the Ph.D. dissertation of Norbert Wiener.
Chao, John +2 more
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Causality tests in econometrics
Journal of Economic Dynamics and Control, 1979The basic theorem characterizing Granger causality events and existing testing methods are surveyed. An alternative direct testing method based on Akaike's final prediction error criterion is suggested. The various methods are compared both empirically and theoretically by applying them to postwar U.S.
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Testing Probabilistic Causality
1994Probabilistic causality is related to the value of λ, the parameter of the predictive probability function elaborated by Gini, Johnson, and Carnap. Thus it is impossible to make estimates within Suppes’s theory of probabilistic causality. This causality can only be checked using tests of significance. Two examples of this are given. Some considerations
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