Data Revisions in General Equilibrium
In this paper we document the properties of revisions to macroeconomic data in the US and analyze the implications of these properties in the context of a general equilibrium model. We find that the revisions to major macroeconomic variables such as output and productivity growth are large and predictable. We also provide some evidence that professional forecasters ignore this predictability. Using our empirical results as the motivation, we study the effects of revisions in a general equilibrium framework. We find that the presence of data revisions 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 $43 billion, $12 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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|Date of creation:||2004|
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