Abstract
In advance of focusing in subsequent chapters on the main theme of the measures of interdependency, Chap. 1 provides a brief overview of the literature on empirical causal analysis and places the theme in a broader perspective, comparing a variety of conflicting views on how certain statistical associations can be viewed as causal. Among others, alluded to is the field experiment model of detecting causal effects by Neyman (1923) and its reliance on a counterfactual assumption. Controlled random experiments are compared with observational studies in econometrics. The concepts of causality and exogeneity in the framework of the simultaneous equation are discussed. Specifically, ancillarity and conditioning in statistical inferences are explained and their relation to exogeneity is expounded. A preliminary concept of Granger causality is introduced, and the role of prediction improvement in empirical analyses is emphasized.
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References
Athanasopoulos, G., & Vahid, F. (2008). VARMA versus VAR for macroeconomic forecasting. Journal of Business and Economic Statistics, 26, 237–252.
Barndorff-Nielsen, O. (1978). Information and Exponential Families: In Statistical Theory. Chichester: Wiley.
Basu, D. (1964). Recovery of ancillary information. Sankhya, 21, 247–256.
Birnbaum, A. (1962). On the foundations of statistical inference (with discussion). Journal of American Statistical Association, 57, 269–326.
Cox, D. R. (1958). Some problems connected with statistical inference. Annals of Mathematical Statistics, 29, 357–372.
Cox, D. R., & Wermuth, N. (2001). Some statistical aspects of causality. European Sociological Review, 17, 65–74.
Engle, R. F., Hendry, D. F., & Richard, J.-F. (1983). Exogeneity. Econometrica, 51, 277–304.
Fisher, R. A. (1925). Theory of statistical estimation. Proceedings of Cambridge Philosophical Society, 22, 700–725.
Fisher, R.A. (1934). Two new properties of mathematical likelihood, Proceedings of Royal Society (London), A 144, 285-307.
Fisher, R. A. (1956). Statistical methods and scientific inference. Edinburgh: Oliver and Boyd.
Freedman, D. A. (2010). Statistical Models and Causal Inference. Cambridge: Cambridge University Press.
Granger, C. W. J. (1963). Economic process involving feedback. Information and Control, 6, 28–48.
Granger, C. W. J., & Hatanaka, M. (1964). Spectral Analysis of Economic Time Series. Princeton: Princeton University Press.
Haavelmo, T. (1944). The probability approach in econometrics, Econometrica, 12, supplement.
Hamilton, J. D. (1994). Time Series Analysis. Princeton: Princeton University Press.
Heckman, J. J. (2000). Causal parameters and policy analysis in economics: A twentieth century retrospective. The Quarterly Journal of Economics, 115, 45–97.
Hicks, J. (1979). Causality in Economics. Oxford: Basil Blackwell.
Hill, A. B. (1965). The environment and disease: association or causation. Proceedings of the Royal Society of Medicine, 58, 295–300.
Holland, P. (1986). Statistics and causal inference (with discussion). Journal of the American Statistical Association, 81, 945–970.
Hosoya, Y. (1988). The second-order Fisher information. Biometrika, 75, 265–274.
Hosoya, Y., Tsukuda, Y., & Terui, N. (1989). Ancillarity and the limited information maximum-likelihood estimation of a structural equation in a simultaneous equation system. Econometric Theory, 5, 384–404.
Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59, 1551–1580.
Keynes, J. M. (1939). Professor Tinbergen’s method. The Economic Journal, 49, 558–570.
Keynes, J. M. (1940). Comment on Tinbergen’ s response. Economic Journal, 50, 154–156.
Klein, L. R. (1981). Econometric models as guides for decision-making. London: The Free Press.
Lucas, R. (1976). Econometric policy evaluation: A critique, the phillips curve and labor markets. Carnegie Rochester Conference Series on Public Policy, I, 19–46.
Marschak, J. (1953). Economic measurements for policy and prediction. In William Hood & Tialling Koopmans (Eds.), Studies in Econometric Method (pp. 1–26). New York: Wiley.
Marshall, A. (1920). Principles of Economics (8th ed.). Londonn: Macmillan and Co. (Reprinted 1930).
Neyman, J. (1923). Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes, Roczniki Nauk Rolniczki, 10, 1–51, in Polish. English translation by D. Dabrowska & T. Speed (1990), Statistical Science, 5, 463–480 (with discussion).
Simon, H. A. (1953). Causal ordering and identifiability. In C. Hood & T. C. Koopmans (Eds.), Studies in Econometric Method (pp. 49–74). New York: Wiley.
Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48, 1–47.
Sutton, J. (2000). Marshall’s Tendencies. Cambridge: The MIT Press.
Tinbergen, J. (1939). Statistical Testing of Business Cycle Theories, vols. 1 and 2, League of Nations, Geneva.
Tinbergen, J. (1940). Reply to Keynes. The Economic Journal, 50, 141–154.
Wold, H. (1956). Causal inference from observation data. Journal of the Royal Statistical Society, 119, 28–60.
Yule, G. U. (1899). An investigation into the causes of changes in pauperism in England, chiefly during the last two intercensal decades. Journal of the Royal Statistical Society, 62, 249–295.
Zeisel, H., & Kaye, D. (1997). Prove It with Figures: Empirical Methods in Law and Litigation. New York: Springer.
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Hosoya, Y., Oya, K., Takimoto, T., Kinoshita, R. (2017). Introduction. In: Characterizing Interdependencies of Multiple Time Series. SpringerBriefs in Statistics(). Springer, Singapore. https://doi.org/10.1007/978-981-10-6436-4_1
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