Data Uncertainty in General Equilibrium
In this paper, using recent empirical results regarding the statistical properties of macroeconomic data revisions, we study the effects of data revisions in a general equilibrium framework. We find that the presence of data revisions, or data uncertainty, creates a precautionary motive and causes significant changes in the decisions of agents. We also find that the model with revisions captures some aspects of the business cycle dynamics of the US data better than the benchmark model with no revisions. Using our model we measure the cost of having data revisions to be about $33 billion, $5 billion of which can be recovered by eliminating the predictability of revisions. Comparing these numbers with the budgets of the major statistical agencies in the US, we conclude that any money spent on the improvement of data collection would be well worth it
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