Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-25T06:08:11.483Z Has data issue: false hasContentIssue false

Noncausality and Marginalization of Markov Processes

Published online by Cambridge University Press:  11 February 2009

J.P. Florens
Affiliation:
Université des Sciences Sociales
M. Mouchart
Affiliation:
Université Catholique de Louvain
J.M. Rolin
Affiliation:
Université Catholique de Louvain

Abstract

In this paper it is shown that a subprocess of a Markov process is markovian if a suitable condition of noncausality is satisfied. Furthermore, a markovian condition is shown to be a natural condition when analyzing the role of the horizon (finite or infinite) in the property of noncausality. We also give further conditions implying that a process is both jointly and marginally markovian only if there is both finite and infinite noncausality and that a process verifies both finite and infinite noncausality only if it is markovian. Counterexamples are also given to illustrate the cases where these further conditions are not satisfied.

Type
Articles
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Basu, D.On statistics independent of a complete sufficient statistic. Sankhya 15 (1955): 377380.Google Scholar
2.Basu, D.On statistics independent of a sufficient statistic. Sankhya 20 (1958): 223226.Google Scholar
3.Boudjellaba, H., Dufour, J.M. & Roy, R.. Testing causality between two vectors in multivariate ARMA models. Paper presented at the VIth World Meeting of the Econometric Society, Barcelona (1990).Google Scholar
4.Bouissou, M.B., Laffont, J.J. & Vuong, Q.H.. Tests of non-causality under markov assumptions for qualitative panel data. Econometrica 54 (1986): 395414.Google Scholar
5.Breiman, L., Lecam, L. & Schwartz, L.. Consistent estimates and zero-one sets. Annals of Mathematical Statistics 35 (1964): 157161.Google Scholar
6.Bremaud, P. & Yor, M.. Changes of filtrations and of probability measures. Zeitschrift für Wahrscheinlichheits theorie und Verwandte Gebiete 45 (1978): 269296.Google Scholar
7.Dellacherie, C. & Meyer, P.A.. Probabilité et Potentiel. Paris, Hermann 1975.Google Scholar
8.Engle, R.F., Hendry, D.F. & Richard, J.F.. Exogeneity. Econometrica 51 (2) (1983): 277304.Google Scholar
9.Florens, J.P. & Fougère, D.. Non-causality in continuous time: Application to counting processes. Cahier du GREMAQ 91.b, Université des Sciences Sociales de Toulouse (1991).Google Scholar
10.Florens, J.P. & Mouchart, M.. A note on non-causality. Econometrica 50 (1982): 583606.Google Scholar
11.Florens, J.P. & Mouchart, M.. A linear theory for non-causality. Econometrica 53 (1985a): 157175.Google Scholar
12.Florens, J.P. & Mouchart, M.. Conditioning in dynamic models. Journal of Time Series 6 (1) (1985b): 1535.Google Scholar
13.Florens, J.P., Mouchart, M. & Rolin, J.M.. Reduction dans les expériences bayesiennes séquentielles. Cahiers du Centre d'Etudes de Recherche Opérationnelle 22 (3–4) (1980): 353362.Google Scholar
14.Florens, J.P., Mouchart, M. & Rolin, J.M.. Elements of Bayesian Statistics. New York: Marcel Dekker, 1990.Google Scholar
15.Geweke, J.Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77 (1982): 304313.Google Scholar
16.Geweke, J., Meese, R. & Dent, W.. Comparing alternative tests of causality in temporal system. Journal of Econometrics 21 (1983): 161194.Google Scholar
17.Gourieroux, C., Monfort, A. & Renault, E.. Kullback causality measures. Annales d'Economie et de Statistique 6/7 (1987): 369410.Google Scholar
18.Granger, C.W.J.Investigating causal relations by econometric models and cross spectral methods. Econometrica 37 (1969): 424438.Google Scholar
19.Granger, C.W.J.Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control 2 (1980): 329352.Google Scholar
20.Granger, C.W.J. & Newbold, P.. Forecasting Economic Time Series. New York: Academic Press, 1977.Google Scholar
21.Koehn, U. & Thomas, D.L.. On the statistical independence of a sufficient statistics: Basu's lemma. The American Statistician 39 (1975): 4042.Google Scholar
22.Lehmann, E.L. & Scheffé, H.. Completeness, similar regions and unbiased tests. Part II. Sankhya 15 (1955): 219236.Google Scholar
23.Mouchart, M. & Rolin, J.M.. A note on conditional independence (with Statistical Applications). Statistica 44(4) (1984): 557584.Google Scholar
24.Mouchart, M. & Rolin, J.M.. On maximal ancillarity. Statistica 49(1) (1989): 2137.Google Scholar
25.Pierce, D. & Haugh, L.. Causality in temporal systems. Journal of Econometrics 51 (1977): 269294.Google Scholar
26.Sims, C.A.Money. Income and causality. American Economic Review 62 (1972): 540552.Google Scholar
27.Sims, C.A.Macroeconomics and reality. Econometrica 48 (1980): 148.Google Scholar
28.Teicher, H.Identifiability of mixtures of product measures. Annals of Mathematical Statistics 38 (1967): 13001302.Google Scholar