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Generalization of Information, Granger Causality, and Forecasting

This paper analyzes forecasting problems from the theoretical perspective of information extraction. We study circumstances under which a generalization of information across economic domains (industries, countries) is warranted even if there are no causal links connecting these domains. A domain-specific variable (like an industry’s output level) is potentially useful for forecasting variables outside this domain because it reveals information concerning common unobserved driving variables. This is a form of rational information contagion. In multivariate time series-regressions such effects show up in (quantitatively and statistically) significant coefficients of lagged variables from other domains. Two examples are presented to illustrate the empirical relevance of information contagion.