A flexible finite-horizon alternative to long-run restrictions with an application to technology shock

Recent studies using long-run restrictions question the validity of the technology-driven real business cycle hypothesis. We propose an alternative identi cation 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. Unlike its long-run restriction counterpart, when our Max Share identification technique is applied to U.S. data it delivers the robust result that hours worked responds negatively to positive technology shocks. ; Earlier title: A flexible finite-horizon identification of technology shocks