Searching for the Causal Structure of a Vector Autoregression

Oxford Bulletin of Economics and Statistics

Volumn 65, Issue SUPPL., 2003, Pages 745-767

  Demiralp, Selva ^a   Hoover, Kevin D ^b  

^a Division of Monetary Affairs   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0347568442     PISSN: 03059049     EISSN: None     Source Type: Journal    
DOI: 10.1046/j.0305-9049.2003.00087.x     Document Type: Conference Paper

References (34)

1. Akleman, D. G., Bessler, D. A. and Burton, D. M. (1999). 'Modeling com exports and exchange rates with directed graphs and statistical loss functions', in Glymour, C., Cooper, G. F. (eds), Computation, Causation, and Discovery, American Association for Artificial Intelligence, Menlo Park, CA and MIT Press, Cambridge, MA, pp. 497-520.
2. Board of Governors of the Federal Reserve System (1976). Banking and Monetary Statistics: 1941-1970, Board of Governors of the Federal Reserve System, Washington, DC.
3. Bessler, D. A. and Lee, S. (2002). 'Money and prices: US data 1869-1914 (a study with directed graphs)', Empirical Economics, Vol. 27, pp. 427-446.
4. Breitung, J. and Swanson, N. R. (2002). 'Temporal aggregation and spurious instantaneous causality in multiple time series models', Journal of Time Series Analysis, Vol. 23, pp. 651-656.
5. Cooley, T. F. and LeRoy, S. F. (1985). 'Atheoretical macroeconometrics: a critique', Journal of Monetary Economics, Vol. 16, pp. 283-308.
6. Cooper, G. F. (1999). 'An overview of the representation and discovery of causal relationships using Bayesian networks', in Glymour C. and Cooper G. F. (eds), Computation, Causation, and Discovery, American Association for Artificial Intelligence, Menlo Park, CA and MIT Press, Cambridge, MA, pp. 3-64.
7. Demiralp, S. (2000). 'The structure of monetary policy and transmission mechanism', PhD dissertation, Department of Economics, University of California, Davis, CA.
8. Hamilton, J. (1994). Time Series Analysis. Princeton University Press, Princeton.
9. Hendry, D. F. and Krolzig, H.-M. (1999). 'Improving on "Data Mining Reconsidered" by K. D. Hoover and S. J. Perez', Econometrics Journal, Vol. 2, pp. 202-218.
10. Hoover, K. D. (1995). 'In defense of data mining: some preliminary thoughts', in Hoover K. D. and Sheffrin S. M. (eds), Monetarism and the Methodology of Economics: Essays in Honour of Thomas Mayer, Edward Elgar, Aldershot.
11. Hoover, K. D. (2001). Causality in Macroeconomics, Cambridge University Press, Cambridge.
12. Hoover, K. D. (2003). 'Nonstationary time series, cointegration, and the principle of the common cause'. British Journal for the Philosophy of Science, Vol. 54, pp. 527-551.
13. Hoover, K. D. and Perez, S. J. (1999). 'Data mining reconsidered: encompassing and the general-to-specific approach to specification search', Econometrics Journal, Vol. 2, pp. 167191.
14. Hoover, K. D. and Perez, S. J. (2000). 'Three attitudes towards data-mining', Journal of Economic Methodology, Vol. 7, pp. 195-210.
15. Hoover, K. D. and Perez, S. J. (2002). Truth and Robustness in Cross-country Growth Regressions, Unpublished working paper, University of California, Davis, CA.
16. Johnston, J. (1972). Econometric Methods, 2nd edn, McGraw-Hill, New York.
17. King, R. G., Plosser, C. I., Stock, J. H. and Watson, M. W. (1991), 'Stochastic trends and economic fluctuations', American Economic Review, Vol. 81, pp. 819-840.
18. Krolzig, H.-M. and Hendry, D. F. (2001). 'Computer automation of general-to-specific model selection procedures', Journal of Economic Dynamics and Control, Vol. 25, pp. 831-866.
19. Lauritzen, S. L. and Richardson, T. S. (2002). 'Chain graphs and their causal interpretations', Journal of the Royal Statistical Society, Series B (Methodological), Vol. 64, pp. 321-361.
20. Lauritzen, S. L. and Spiegelhalter, D. J. (1988). 'Local computations with probabilities on graphical structures and their applications to expert systems' [with discussion], Journal of the Royal Statistical Society, Series B (Methodological), Vol. 50, pp. 157-224.
21. Leamer, E. (1985). 'Vector autoregressions for causal inference?' in Brunner K. and Meltzer A. H. (eds), Understanding Monetary Regimes, Carnegie-Rochester Conference Series on Public Policy, Vol. 22, North Holland, Amsterdam, 225-304.
22. LeRoy, S. F. (2002). 'Causality: models, reasoning, and inference: a review of Judea Pearl's Causality', Journal of Economic Methodology, Vol. 9, pp. 100-103.
23. Pearl, J. (2000). Causality: Models, Reasoning, and Inference, Cambridge University Press, Cambridge.
24. Reale, M. and Tunnicliffe Wilson, G. (2000). Identification of Vector AR Models with Recursive Structural Errors Using Conditional Independence Graphs, Unpublished typescript, Department of Mathematics and Statistics, University of Canterbury (New Zealand) and Department of Mathematics and Statistics, Lancaster University (UK).
25. Reale, M. and Tunnicliffe Wilson, G. (2002). 'The sampling properties of conditional independence graphs for structural vector autoregressions', Biometrica, Vol. 89, pp. 457-461.
26. Reichenbach, H. (1956). The Direction of Time, University of California Press, Berkeley and Los Angeles.
27. Tunnicliffe Wilson, G., Reale, M. and Morton, A. S. (2001). Developments in Multivariate Time Series Modeling, Unpublished typescript, Department of Mathematics and Statistics, Lancaster University (UK) and Department of Mathematics and Statistics, University of Canterbury (New Zealand).
28. Tunnicliffe Wilson, G. and Reale, M. (2002). Causal Diagrams for I(1) Structural VAR Models, Unpublished typescript, Department of Mathematics and Statistics, University of Canterbury (New Zealand) and Department of Mathematics and Statistics, Lancaster University (UK).
29. Sheffrin, S. M. and Triest, R. K. (1998). A New Approach to Causality and Economic Growth, Unpublished typescript, University of California, Davis, CA.
30. Sims, C. A. (1980). 'Macroeconomics and reality', Econometrica, Vol. 48, pp. 1-48.
31. Sims, C. A. (1986). 'Are forecasting models usable for policy analysis?' Federal Reserve Bank of Minneapolis Quarterly Review, Vol. 10, pp. 2-15.
32. Spirtes, P., Scheines, R., Meek, C., Richardson, T., Glymour, C., Hoijtink, H. and Boomsma, A. (1996). TETRAD 3: Tools for Causal Modeling, program (beta version, October) and user's manual on the worldwide web at http://www.phil.cmu.edu/tetrad/tet3/master.htm.
33. Spirtes, P., Glymour, C. and Schemes, R. (2000). Causation, Prediction, and Search, 2nd edn, MIT Press, Cambridge, MA.
34. Swanson, N. R. and Granger, C. W. J. (1997). 'Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions', Journal of the American Statistical Association, Vol. 92, pp. 357-367.