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IDENTIFYING TECHNOLOGY SHOCKS AT THE BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS

Published online by Cambridge University Press:  05 February 2020

Yuliya Lovcha  and
Alejandro Perez-Laborda
Yuliya Lovcha*
Affiliation:
Universitat Rovira-i-Virgili and CREIP
Alejandro Perez-Laborda
Affiliation:
Universitat Rovira-i-Virgili and CREIP
*
Address correspondence to: Yuliya Lovcha, Department of Economics, Universitat Rovira-i-Virgili. Av. Universitat 1, 43204Reus, Spain. e-mail: [email protected]. Phone: (+34) 977759847. Fax: (+34) 977758907.
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Abstract

In this paper, we identify the technology shock at business cycle frequencies to improve the performance of structural vector autoregression models in small samples. To this end, we propose a new identification method based on the spectral decomposition of the variance, which targets the contributions of the shock in theoretical models. Results from a Monte-Carlo assessment show that the proposed method can deliver a precise estimate of the response of hours in small samples. We illustrate the application of our methodology using US data and a standard Real Business Cycle model. We find a positive response of hours in the short run following a non-significant, near-zero impact. This result is robust to a large set of credible parameterizations of the theoretical model.

Keywords

SVARFrequency DomainRBC ModelHours WorkedTechnology Shock
Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 8 , December 2021 , pp. 1966 - 1992
Copyright
© Cambridge University Press 2020

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Footnotes

This paper has benefited from numerous comments and suggestions from two anonymous referees. Perez-Laborda acknowledges financial support from the Spanish Ministry of Economy and Competitiveness through AEI/FEDER-EU (ECO2016-75410-P) grant. The usual disclaimers apply.

References

Basu, S., Fernald, J. G. and Shapiro, M. (2001) Productivity growth in the 1990s: Technology, utilization, or adjustment? Carnegie-Rochester Conference Series on Public Policy 55, 117–165.CrossRefGoogle Scholar
Blanchard, O. and Perotti, R. (2002) An empirical characterization of the dynamic effects of changes in government spending and taxes on output. The Quarterly Journal of Economics 117, 1329–1368.CrossRefGoogle Scholar
Blanchard, O. and Quah, D. (1989) The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79, 655–673.Google Scholar
Canova, F. and Paustian, M. (2011) Business cycle measurement with some theory. Journal of Monetary Economics 58, 345–361.CrossRefGoogle Scholar
Chari, V. V., Kehoe, P. J. and McGrattan, E. R. (2008) Are structural VARs with long-run restrictions useful in developing business cycle theory? Journal of Monetary Economics 55, 1337–1352.CrossRefGoogle Scholar
Christiano, L. J., Eichenbaum, M. and Vigfusson, R. (2003) What happens after a technology shock? NBER Working Paper No. 9819.Google Scholar
Christiano, L. J., Eichenbaum, M. and Vigfusson, R. (2004) The response of hours to a technology shock: Evidence based on direct measures of technology. Journal of the European Economic Association 2, 381–395.CrossRefGoogle Scholar
Christiano, L. J., Eichenbaum, M. and Vigfusson, R. (2007) Assessing structural VARs. In: Acemoglu, D., Rogoff, K. and Woodford, M. (eds.), NBER Macroeconomics Annual 2006, Vol. 21, pp. 1–106. Cambridge, MA: MIT Press.Google Scholar
Dedola, L. and Neri, S. (2007) What does a technology shock do? A VAR analysis with model-based sign restrictions. Journal of Monetary Economics 54, 512–549.CrossRefGoogle Scholar
DiCecio, R. and Owyang, M. T. (2010) Identifying technology shocks in the frequency domain. WP 2010-025A, Federal Reserve Bank of St.Louis.CrossRefGoogle Scholar
Erceg, C. J., Guerrieri, L. and Gust, C. (2005) Can long-run restrictions identify technology shocks? Journal of the European Economic Association 3, 1237–1278.CrossRefGoogle Scholar
Evans, C. L. and Marshall, D. A. (2009) Fundamental economic shocks, and the macroeconomy. Journal of Money, Credit and Banking 41, 1515–1555.CrossRefGoogle Scholar
Faust, J. and Leeper, E. (1997) When do long-run identifying restrictions give reliable results? Journal of Business and Economic Statistics 15, 345–353.Google Scholar
Fout, H. B. and Francis, N. (2014) Imperfect transmission of technology shocks and the business cycle consequences. Macroeconomic Dynamics 18, 418–437.CrossRefGoogle Scholar
Francis, N., Owyang, M. T., Roush, J. E. and DiCecio, R. (2014) A flexible finite-horizon alternative to long-run restrictions with an application to technology shocks. The Review of Economics and Statistics 96, 38–647.CrossRefGoogle Scholar
Francis, N. and Ramey, V. A. (2009) Measures of per capita hours and their implications for the technology-hours debate. Journal of Money, Credit and Banking 41, 1071–1097.CrossRefGoogle Scholar
Gali, J. (1999) Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271.CrossRefGoogle Scholar
Geweke, J. (1977) The dynamic factor analysis of economic time series. In: Dennis, J. A. and Goldberger, A. (eds.), Latent Variables in Socio-Economic Models, pp. 365–383. Amsterdam: North-Holland.Google Scholar
Giuli, F. and Tancioni, M. (2017) Contractionary technology shocks. Macroeconomic Dynamics 21, 1752–1789.CrossRefGoogle Scholar
Gospodinov, N., Maynard, A. and Pesavento, E. (2011) Sensitivity of impulse-responses to small low-frequency co-movements: Reconciling the evidence on the effect of technology shocks. Journal of Business and Economic Statistics 28, 1–12.CrossRefGoogle Scholar
Hall, A. R., Inoue, A., Nason, J. M. and Rossi, B. (2012) Information criteria for impulse-response function matching estimation of DSGE models. Journal of Econometrics 170, 499–518.CrossRefGoogle Scholar
Herbst, E. P. and Schorfheide, F. (2016) Bayesian Estimation of DSGE Models. Princeton; Oxford: Princeton University Press.CrossRefGoogle Scholar
Ireland, P. N. (2004) Technology shocks in the new Keynesian model. The Review of Economics and Statistics 86, 923–936.CrossRefGoogle Scholar
Kilian, L. and Lee, T. K. (2014) Quantifying the speculative component in the real price of oil: The role of global oil inventories. Journal of International Money and Finance 42, 71–87.CrossRefGoogle Scholar
Lanne, Markus, Meitz, Mikka and Saikkonen, Pentti (2017) Identification and estimation of non-Gaussian structural vector autoregressions. Journal of Econometrics 196, 288–304.CrossRefGoogle Scholar
Lovcha, Y. and Perez-Laborda, A. (2015) Hours worked-productivity puzzle: Identification in fractional integration settings. Macroeconomic Dynamics 19, 1593–1621.CrossRefGoogle Scholar
Lovcha, Y. and Perez-Laborda, A. (2019) Trimmed Witthle estimation of the SVAR vs. filtering low-frequency fluctuations: Applications to technology shocks. Studies in Nonlinear Dynamics and Econometrics. Advance online publication. doi: 10.1515/snde-2018-0030.Google Scholar
Mertens, K. and Ravn, M. O. (2013) The dynamic effects of personal and corporate income tax changes in the United States. American Economic Review 103, 1212–47.CrossRefGoogle Scholar
Pappa, E. (2009) The effect of fiscal shocks on employment and the real wage. International Economic Review 50, 217–253.CrossRefGoogle Scholar
Pesavento, E. and Rossi, B. (2005) Do technology shocks drive hours up or down? A little evidence from an agnostic procedure. Macroeconomic Dynamics 9, 478–488.CrossRefGoogle Scholar
Prescott, E. C. (1986) Theory ahead of business cycle measurement. Federal Reserve Bank of Minneapolis Quarterly Review 10, 9–22.Google Scholar
Ramey, V. A. (2016) Macroeconomic shocks and their propagation. In: John, B. T. and Uhlig, H. (eds.), Handbook of Macroeconomics Vol. 2, 71–172. North-Holland, Amsterdam: Elsevier.Google Scholar
Rubio-Ramirez, J. F., Waggoner, D. F. and Zha, T. (2010) Structural vector autoregressions. Theory of identification and algorithms for inference. The Review of Economic Studies 77, 665–696.CrossRefGoogle Scholar
Sargent, T. J. (1987) Macroeconomic Theory, 2nd ed. Boston, MA: Academic Press.Google Scholar
Shapiro, M. and Watson, M. W. (1988) Sources of business cycles fluctuations. In Fisher, S. (ed.), NBER Macroeconomics Annual 1988, Vol. 3, pp. 11–156. Cambridge, MA: MIT Press.Google Scholar
Sims, C. (1989) Models, and their uses. American Journal of Agricultural Economics 71, 489–494.CrossRefGoogle Scholar
Sobol, I. (1977) Distribution of points in a cube and approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics 16, 236–242.CrossRefGoogle Scholar
Thomet, J. and Wegmueller, P. (2019) Technology shocks and hours worked: A cross country analysis. Macroeconomic Dynamics. Advance online publication. doi: 10.1017/S1365100519000658.Google Scholar
Uhlig, H. (2004) Do technology shocks lead to a fall in total hours worked? Journal of the European Economic Association 2, 361–371.CrossRefGoogle Scholar