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Books on the topic 'Granger causality'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 May 2024

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1

Yi, Wen. Granger causality and equilibrium business cycle theory. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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2

Reuveny, Rafael. International trade, political conflict/cooperation, and Granger causality. Bloomington, Indiana: Indiana University, Graduate School of Business, 1994.

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3

Bianchi, Marco. Granger causality in the presence of structural changes. London: Bank of England, 1995.

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4

Reuveny, Rafael. Disaggregated bilateral trade political conflict/cooperation and Granger causality. Bloomington, Indiana: Indiana University, Graduate School of Business, 1996.

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5

Halbert, White, Engle R. F, and Granger, C. W. J. 1934-, eds. Cointegration, causality, and forecasting: A festschrift in honour of Clive W.J. Granger. Oxford: Oxford University Press, 1999.

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6

Lyons, Susanna E. Cointegration and Granger-causality testing of hypotheses on supply-leading and demand-following finance. Cardiff: Cardiff Business School, Financial and Banking Economics Research Group, 1993.

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7

Spanos, Aris. On the statistical implications of modelling the error term: Common factors, Granger-causality and unit roots. London: Birkbeck College, Dept. of Economics, 1987.

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8

Abhyankar, Abhay. Linear and nonlinear Granger causality: Evidence from the FT-SE 100 stock index futures and cash markets. Stirling: University of Stirling, Dept. of Accountancy and Finance, 1994.

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9

Zhongguo jin rong fa zhan yu jing ji zeng chang de yin guo fang xiang xing shi zheng yan jiu: 1995-2004 = An empirical study on the direction of granger causality between financial development and economic growth in China. Beijing Shi: Jing ji ke xue chu ban she, 2008.

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10

Zhongguo jin rong fa zhan yu jing ji zeng chang de yin guo fang xiang xing shi zheng yan jiu: 1995-2004 = An empirical study on the direction of granger causality between financial development and economic growth in China. Beijing Shi: Jing ji ke xue chu ban she, 2008.

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11

Philonenko, Alexis. Kant et l'île de la vérité: Néant, chose en soi, sujet transcendantal, causalité, grandeur intensive, temps, substance. Paris: Librarie philosophique J. Vrin, 2011.

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12

Dobson, S. M. Performance and revenue in professional league football: Evidence from Granger causality tests. University of Hull, Department of Economics, 1997.

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13

(Editor), Robert F. Engle, and Halbert White (Editor), eds. Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive W.J. Granger. Oxford University Press, USA, 1999.

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14

Watson, Mark W., Clive W. J. Granger, Norman R. Swanson, and Eric Ghysels. Essays in Econometrics : Volume 2, Causality, Integration and Cointegration, and Long Memory: Collected Papers of Clive W. J. Granger. Cambridge University Press, 2010.

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15

Watson, Mark W., Clive W. J. Granger, Norman R. Swanson, and Eric Ghysels. Essays in Econometrics : Volume 2, Causality, Integration and Cointegration, and Long Memory: Collected Papers of Clive W. J. Granger. Cambridge University Press, 2001.

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16

Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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Abstract:
This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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