Article

Causal Search in Structural Vector Autoregressive Models.

Journal of Machine Learning Research - Proceedings Track 01/2011; 12:95-114.
Source: DBLP
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Available from: Nadine Chlaß, Jan 17, 2014
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    • "We assume that the underlying causal structure can be modeled as a directed graphical model G without simultaneous influence. There has been substantial work on modeling the statistics of time series, but relatively less on learning causal structure, and almost all of that assumes that the measurement and causal timescales match [1] [2] [3] [4] [5]. The problem of causal learning from " undersampled " time series data was explicitly addressed by [6] [7], but they assumed that the degree of undersampling—i.e., the ratio of τ S to τ M —was both known and small. "
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    • "Accordingly, we need some extra information to provide Q. However, even if we introduce an orthonormality constraint QQ T = E t (U (t)U (t) T ) to make noises in W(t) mutually uncorrelated, the representation of the DVAR model given by this approach is not unique, because there are many choices for Q that satisfy that constraint, for example, QO satisfying QO(QO) T = QOO T Q T = QQ T , where O is any orthonormal matrix [Moneta et al. 2011]. There are many studies on this issue for identifying the SVAR model. "
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    • "Besides, the variables imports, dependency ratio and population density were dropped after tests of stationary proved them to be of different order of integration. Moreover, the statistical performance of the estimates from VAR and VEC models has been well studied and well established for models with a few number of variables (Moneta et al., 2011). "
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