Abstract
Economic theories are often formulated as a system of structural equations. These structural equations imply genuinely causal relations, i.e., we regard the right-hand side variables as the causes and the left-hand side variables as the effects. In this paper we show that regression is not the proper way to infer causal relations. Based on the theory of inferred causation we propose a method to derive structural equations from multivariate time series. We apply this method to study the wage-price spiral for the Australia economy.
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References
Blanchard O. J., Katz L. (1999) Reconciling theory and evidence. American Economic Review 89(2): 69–74
Cartwright N. (2001) What is wrong with Bayes nets?. MONIST 84: 242–264
Chen, P., & Flaschel, P. (2006). Measuring the interaction of wage and price phillips curves for the U.S. economy. Studies in Nonlinear Dynamics and Econometrics, 10(4), Article 2.
Chen, P., & Hsiao, C. (2007). Learning causal relations in multivariate time series data. Economics eJournal 2007, 11.
Demiralp S., Hoover K. (2004) Searching for the causal structure of a vector autoregression. Oxford Bulletin of Economics and statistics 65: 745–767
Demiralp S., Hoover K. D., Perez S. J. (2008) A bootstrap method for identifying and evaluating a structural vector autoregression. Oxford Bulletin of Economics and statistics 70(4): 509–533
Flaschel, P., & Krolzig, H. (2003). Wage and price Phillips curves. An empirical analysis of destabilizing wage-price spirals. Center of Empirical Macroeconomics, Bielefeld University.
Freedman, D., & Humphreys, P. (1998). Are there algorithms that discover causal structure? Available at http://www.stat.berkeley.edu/~census/514.pdf.
Hausman D. M., Woodward J. (2004) Modularity and the causal markov condition: A restatement. The British Journal for the Philosophy of Science 55(1): 147–161
Heckerman (1995). A tutorial on learning with bayesian networks. Microsoft Research. MSR-TR-95-06: http://research.microsoft.com/research/pubs.
Heckerman D., Geiger D., Chickering D. (1995) Learning bayesian network: The combination of knowledge and statistical data. Machine Learning 20: 197–243
Hoover K. (2005) Automatic inference of the contemporaneous causal order of a system of equations. Econometric Theory 21: 69–77
Krolzig H. M. (2003) General-to-specific model selection procedures for structural vector autoregressions. Oxford Bulletin of Economics and Statistics 65(s1): 769–801
Pearl J. (2000) Causality. Cambridge University Press, Cambridge
Pearl J., Verma T. (1991) A theory of inferred causation. In: Allen J.A., Fikes R., Sandewall E. (eds) Principles of knowledge representation and reasoning: Procedings of the 2nd International Conference. Morgan Kaufmann, San Mateo, CA, pp 441–452
Spirtes P., Glymour C., Scheines R. (2001) Causation, prediction and search (2nd ed.). Springer, New York
Swanson N., Granger J. (1997) Impulse response functions based on causal approach to residual orthogonalization in vector autoregressions. Journal of the American Statistical Association 92: 357–367
Verma, T., & Pearl, J. (1990). Equivalence and synthesis of causal models. In Proceeding of the sixth annual conference on uncertainty in artificial intelligence (pp. 255–270). ISBN: 0-444-89264-8.
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Chen, P., Hsiao, CY. Causal Inference for Structural Equations: With an Application to Wage-Price Spiral. Comput Econ 36, 17–36 (2010). https://doi.org/10.1007/s10614-010-9202-6
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DOI: https://doi.org/10.1007/s10614-010-9202-6