A FLEXIBLE FINITE-HORIZON ALTERNATIVE TO LONG-RUN RESTRICTIONS WITH AN APPLICATION TO TECHNOLOGY SHOCKS

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. I...

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Bibliographic Details
Published in:The review of economics and statistics 2014-10, Vol.96 (4), p.638-647
Main Authors: Francis, Neville, Owyang, Michael T., Roush, Jennifer E., DiCecio, Riccardo
Format: Article
Language:English
Subjects:
Business cycles
Economic fluctuations
Economic models
Economic theory
Estimating techniques
Estimation bias
Forecasting models
Identification
Labor economics
Labor productivity
Monte Carlo methods
Monte Carlo simulation
Productivity
Restriction
Restrictions
Statistical variance
Studies
Technology
Technology forecasting
U.S.A
Vector autoregression
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Summary: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. 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.
ISSN:0034-6535
1530-9142
DOI:10.1162/REST_a_00406