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The effects of permanent technology shocks on hours: Can the RBC-model fit the VAR evidence?

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  • Lindé, Jesper

Abstract

I show that a standard RBC-model can be used to explain why hours per capita decrease in response to a permanent technology shock when hours enter a vector autoregressive (VAR) in first differences and why hours increase when hours enter in levels. There are two parts to my argument. First, empirical evidence suggests that a positive permanent technology shock goes together with a persistent increase in the expected growth rate and the RBC-model predicts this increase in the expected growth rate to have a downward effect on hours worked (and can even result in a sizeable negative response of hours). Second, first-differencing hours in VARs results in a considerable downward bias. Using the estimated parameters for the technology process, I find (i) that the true model response of hours is positive and (ii) that when the VAR methodology is used with finite samples of simulated data then the hours' response is negative (positive) when hours enter the VAR in first differences (levels).

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  • Lindé, Jesper, 2009. "The effects of permanent technology shocks on hours: Can the RBC-model fit the VAR evidence?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 597-613, March.
  • Handle: RePEc:eee:dyncon:v:33:y:2009:i:3:p:597-613
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    References listed on IDEAS

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    Cited by:

    1. Ko, Jun-Hyung & Kwon, Hyeog Ug, 2015. "Do technology shocks lower hours worked? – Evidence from Japanese industry level data," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 138-157.
    2. Tommaso Ferraresi & Andrea Roventini & Willi Semmler, 2016. "Macroeconomic regimes, technological shocks and employment dynamics," Documents de Travail de l'OFCE 2016-19, Observatoire Francais des Conjonctures Economiques (OFCE).
    3. Mandelman, Federico S. & Zanetti, Francesco, 2014. "Flexible prices, labor market frictions and the response of employment to technology shocks," Labour Economics, Elsevier, vol. 26(C), pages 94-102.
    4. Konstantinos Theodoridis & Francesco Zanetti, 2016. "News shocks and labour market dynamics in matching models," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 906-930, August.
    5. Tobias Broer & Niels-Jakob H. Hansen & Per Krusell & Erik Öberg, 2016. "The New Keynesian Transmission Mechanism: A Heterogenous-Agent Perspective," NBER Working Papers 22418, National Bureau of Economic Research, Inc.
    6. Rebei, Nooman, 2014. "What (really) accounts for the fall in hours after a technology shock?," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 330-352.
    7. Alexiadis, Stilianos & Eleftheriou, Konstantinos & Nijkamp, Peter, 2013. "Technology adoption within a search model: Evidence from OECD countries," Economic Modelling, Elsevier, vol. 33(C), pages 137-148.
    8. Giuli, Francesco & Tancioni, Massimiliano, 2012. "Real rigidities, productivity improvements and investment dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 100-118.

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