Learning the Structure of a Nonstationary Vector Autoregression

Daniel Malinsky, Peter Spirtes
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2986-2994, 2019.

Abstract

We adapt graphical causal structure learning methods to apply to nonstationary time series data, specifically to processes that exhibit stochastic trends. We modify the likelihood component of the BIC score used by score-based search algorithms, such that it remains a consistent selection criterion for integrated or cointegrated processes. We use this modified score in conjunction with the SVAR-GFCI algorithm, which allows us to recover qualitative structural information about the underlying data-generating process even in the presence of latent (unmeasured) factors. We demonstrate our approach on both simulated and real macroeconomic data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-malinsky19a, title = {Learning the Structure of a Nonstationary Vector Autoregression}, author = {Malinsky, Daniel and Spirtes, Peter}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {2986--2994}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/malinsky19a/malinsky19a.pdf}, url = {https://proceedings.mlr.press/v89/malinsky19a.html}, abstract = {We adapt graphical causal structure learning methods to apply to nonstationary time series data, specifically to processes that exhibit stochastic trends. We modify the likelihood component of the BIC score used by score-based search algorithms, such that it remains a consistent selection criterion for integrated or cointegrated processes. We use this modified score in conjunction with the SVAR-GFCI algorithm, which allows us to recover qualitative structural information about the underlying data-generating process even in the presence of latent (unmeasured) factors. We demonstrate our approach on both simulated and real macroeconomic data.} }
Endnote
%0 Conference Paper %T Learning the Structure of a Nonstationary Vector Autoregression %A Daniel Malinsky %A Peter Spirtes %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-malinsky19a %I PMLR %P 2986--2994 %U https://proceedings.mlr.press/v89/malinsky19a.html %V 89 %X We adapt graphical causal structure learning methods to apply to nonstationary time series data, specifically to processes that exhibit stochastic trends. We modify the likelihood component of the BIC score used by score-based search algorithms, such that it remains a consistent selection criterion for integrated or cointegrated processes. We use this modified score in conjunction with the SVAR-GFCI algorithm, which allows us to recover qualitative structural information about the underlying data-generating process even in the presence of latent (unmeasured) factors. We demonstrate our approach on both simulated and real macroeconomic data.
APA
Malinsky, D. & Spirtes, P.. (2019). Learning the Structure of a Nonstationary Vector Autoregression. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:2986-2994 Available from https://proceedings.mlr.press/v89/malinsky19a.html.

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