Working Paper: CEPR ID: DP7474
Authors: Cristina Fuentes-Albero; Maxym Kryshko; Josvctor Rosrull; Ral Santaeullia Llopis; Frank Schorfheide
Abstract: In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.
Keywords: business cycle; fluctuations; calibration; DSGE model; estimation; technology shocks
JEL Codes: C1; C8; E3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| labor supply elasticity (J20) | impact of technology shocks on hours worked (J29) |
| technology shocks (D89) | labor productivity (J24) |
| labor productivity (J24) | hours worked (J22) |
| neutral and investment-specific technology shocks (E22) | hours worked (J22) |
| Bayesian estimation techniques (C11) | analysis of technology shocks and hours worked (J29) |