Recent studies using long-run restrictions question the valid- ity of the technology-driven real business cycle hypothesis. We propose an alternative identification that maximizes the contribution of technology shockstotheforecast-errorvarianceoflaborproductivityatalongbutfinite horizon. In small-sample Monte Carlo experiments, our identification out- performs standard long-run restrictions by significantly reducing the bias in the short-run impulse responses and raising their estimation precision. Unlike its long-run restriction counterpart, when our Max Share identifica- tion technique is applied to U.S. data, it delivers the robust result that hours worked responds negatively to positive technology shocks.
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