×

What (really) accounts for the fall in hours after a technology shock? (English) Zbl 1402.91965

Summary: The paper asks how state of the art DSGE models that account for the conditional response of hours following a positive neutral technology shock compare in a marginal likelihood race. To that end we construct and estimate several competing small-scale DSGE models that extend the standard real business cycle model. In particular, we identify from the literature six different hypotheses that generate the empirically observed decline in hours worked after a positive technology shock. These models alternatively exhibit (i) sticky prices; (ii) firm entry and exit with time to build; (iii) habit in consumption and costly adjustment of investment; (iv) persistence in the permanent technology shocks; (v) labor market friction with procyclical hiring costs; and (vi) Leontief production function with labor-saving technology shocks. In terms of model posterior probabilities, impulse responses, and autocorrelations, the model favored is the one that exhibits habit formation in consumption and investment adjustment costs. A robustness test shows that the sticky price model becomes as competitive as the habit formation and costly adjustment of investment model when sticky wages are included.

MSC:

91G80 Financial applications of other theories
91B51 Dynamic stochastic general equilibrium theory

References:

[1] Altig, D.; Lawrence, J. C.; Eichenbaumc, M.; Lindé, J., Firm-specific capital, nominal rigidities and the business cycle, Rev. Econ. Dyn., 14, 225-247, (2011)
[2] Ambler, S.; Guay, A.; Phaneuf, L., Endogenous business cycle propagation and the persistence problemthe role of labor-market frictions, J. Econ. Dyn. Control, 36, 47-62, (2012) · Zbl 1241.91065
[3] Bils, M.; Klenow, P., Some evidence on the importance of sticky prices, J. Polit. Econ., 112, 947-985, (2004)
[4] Blanchard, O.; Quah, D., The dynamic effects of aggregate demand and supply disturbances, Am. Econ. Rev., 79, 655-673, (1989)
[5] Blanchard, O. J.; Galí, J., The dynamic effects of aggregate demand and supply disturbances, Am. Econ. J. Macroecon., 2, 1-30, (2010)
[6] Boldrin, M.; Christiano, L.; Fisher, J., Habit persistence, asset returns and the business cycle, Am. Econ. Rev., 91, 149-166, (2001)
[7] Carlstrom, C. T.; Fuerst, T. S., Agencycosts, networth, and business fluctuationsa computable generale equilibrium analysis, Am. Econ. Rev., 87, 893-910, (1997)
[8] Chang, Y.; Gomes, J.; Schorfheide, F., Learning-by-doing as a propagation mechanism, Am. Econ. Rev., 92, 1498-1520, (2002)
[9] Chetty, R., 2009. Bounds on elasticities with optimization frictions: a synthesis of micro and macro evidence on labor supply. NBER Working Paper No. 15616. · Zbl 1274.91338
[10] Christiano, L., Eichenbaum, M., Vigfusson, R., 2004. What happens after a technology shock? Mimeo, Northwestern University.
[11] Christiano, L. J.; Eichenbaum, M.; Evans, C., Nominal rigidities and the dynamic effects of a shock to monetary policy, J. Polit. Econ., 113, 1-45, (2005)
[12] Cogley, T.; Nason, J. M., Output dynamics in real-business-cycle models, Am. Econ. Rev., 85, 492-511, (1995)
[13] Fernald, J., Trend breaks, long-run restrictions, and contractionary technology improvements, J. Monet. Econ., 54, 2467-2485, (2007)
[14] Fernández-Villaverde, J., The econometrics of DSGE models, J. Span. Econ. Assoc., 1, 3-49, (2010)
[15] Fisher, J., The dynamic effects of neutral and investment-specific technology shocks, J. Polit. Econ., 114, 413-451, (2006)
[16] Francis, N.; Ramey, V., Is the technology-driven real business cycle hypothesis dead? shocks and aggregate fluctuations revisited, J. Monet. Econ., 50, 1379-1399, (2005)
[17] Fuhrer, J., Habit formation in consumption and its implications for monetary-policy models, Am. Econ. Rev., 90, 367-390, (2000)
[18] Galí, J., Technology employment and the business cycledo technology shocks explain aggregate fluctuations?, Am. Econ. Rev., 89, 249-271, (1999)
[19] Geweke, J. F., Using simulation methods for Bayesian econometric modelsinference, development and communication, Econ. Rev., 18, 1-126, (1999) · Zbl 0930.62105
[20] Ghironi, F.; Melitz, M., International trade and macroeconomic dynamics with heterogeneous firms, Q. J. Econ., 120, 865-915, (2005) · Zbl 1179.91155
[21] Giraitis, L.; Kapetanios, G.; Yates, T., Inference on stochastic time-varying coefficient models, J. Econom., 179, 46-65, (2014) · Zbl 1293.62184
[22] Greenwood, J.; Hercowitz, Z.; Krusell, P., The role of investment-specific technological change in the business cycle, Eur. Econ. Rev., 44, 91-115, (2000)
[23] Jeffereys, H., Theory of probability, (1961), Oxford University Press Oxford · Zbl 0116.34904
[24] Justiniano, A.; Primiceri, G.; Tambalotti, A., Investment shocks and the relative price of investment, Rev. Econ. Dyn., 14, 102-121, (2011)
[25] King, R. G.; Plosser, C. I.; Rebelo, S. T., Production, growth and business cyclesi. the basic neoclassical model, J. Monet. Econ., 88, 195-232, (1988)
[26] Lindé, J., The effects of permanent technology shocks on hourscan the RBC-model fit the var evidence?, J. Econ. Dyn. Control, 33, 597-613, (2009) · Zbl 1170.91343
[27] Liu, Z.; Phaneuf, L., Technology shocks and labor market dynamicssome evidence and theory, J. Monet. Econ., 54, 2534-2553, (2007)
[28] Mandelman, F.S., Zanetti, F., 2010. Technology shocks, employment and labour market frictions. Working Paper No. 390, Bank of England.
[29] Pistaferri, L., Anticipated and unanticipated wage changes, wage risk, and intertemporal labor supply, J. Labor Econ., 21, 729-754, (2003)
[30] Prescott, E. C., Theory ahead of business cycle measurement, Carnegie-Rochester Conference Series on Public Policy, 11, 11-44, (1986)
[31] Schorfheide, F., Function-based evaluation of DSGE models, J. Appl. Econom., 15, 645-670, (2000)
[32] Sims, E.R., 2011. Permanent and transitory technology shocks and the behavior of hours: a challenge for DSGE models. University of Notre Dame, manuscript.
[33] Smets, F.; Wouters, R., An estimated dynamic stochastic general equilibrium model of the euro area, J. Eur. Econ. Assoc., 1, 1123-1175, (2003)
[34] Wang, P.; Wen, Y., Understanding the effects of technology shocks, Rev. Econ. Dyn., 14, 705-724, (2011)
[35] Whelan, K. T., Technology shocks and hours workedchecking for robust conclusions, J. Macroecon., 31, 231-239, (2009)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.