Tag Archives | Prediction

This article explores the paradox that forecasts may be most likely to fail during dramatic moments of historic change that social scientists are most eager to predict. It distinguishes among four types of shocks that can undermine the predictive power of time series analyses: effect shocks that change the size of the causal effect; input shocks that change the causal variables; duration shocks that change how long a causal effect lasts; and actor shocks that change the number of agents in the system. The significance of these shocks is illustrated in Israeli–Palestinian interactions, one of the contemporary world’s most intensely scrutinized episodes, using vector autogression analyses of more than 15,000 Reuters news stories over the past three decades. The intervention of these shocks raises the prospect that some historic episodes may be unpredictable, even retrospectively.

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