Abstract
Recent studies using long-run restrictions question the validity of the technology driven real business cycle hypothesis. We propose an alternative identification that maximizes the contribution of technology shocks to the forecast-error variance of labor productivity at a long, but finite, horizon. In small-sample Monte Carlo experiments, our identification outperforms standard long-run restrictions by significantly reducing the bias in the short-run impulse responses and raising their estimation precision. When applied to the data, the hours response is shown to be sensitive to the contribution of non-technology shocks to the variance of productivity at long horizons. We conclude that long-run restrictions aimed at isolating the effects of technology shocks on productivity beyond business cycle frequencies do not provide information sufficient to robustly predict short-run movements in labor hours.
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