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Reaction to Technology Shocks in Markov-Switchings Structural VARs: Identification via heteroskedasticity

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  • Aleksei NETSUNAJEV

Abstract

The paper reconsiders the conflicting results in the debate connected to the effects of technology shocks on hours worked in the bivariate system. Given major dissatisfaction with the just-identifying long-run restrictions, I analyze whether the restrictions used in the literature are consistent with the data. Modeling volatility of shocks using Markov switching structure allows to obtain additional identifying information and perform tests of the restrictions that were just-identifying in classical structural vector autoregression analysis. Using four datasets where hours worked are modeled differently, I find that the standard restriction, identifying the technology shocks as the only sources of variation in labor productivity, has major support by the data. Taking into account important low frequency movements in the hours worked series yields a result consistent with the recent findings: hours decline in response to a positive technology shock. I also show that the use of a standard Hodrick-Prescott filter may be problematic in the context.

Suggested Citation

  • Aleksei NETSUNAJEV, 2012. "Reaction to Technology Shocks in Markov-Switchings Structural VARs: Identification via heteroskedasticity," Economics Working Papers ECO2012/13, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2012/13
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    Cited by:

    1. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    2. repec:eee:dyncon:v:84:y:2017:i:c:p:43-57 is not listed on IDEAS
    3. Dmitry Kulikov & Aleksei Netsunajev, 2016. "Identifying Shocks in Structural VAR models via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2015-8, Bank of Estonia, revised 19 Feb 2016.
    4. Juan Carlos Cuestas & Bo Tang, 2015. "Exchange Rate Changes and Stock Returns in China: A Markov Switching SVAR Approach," Working Papers 2015024, The University of Sheffield, Department of Economics.
    5. Noel Gaston & Gulasekaran Rajaguru, 2015. "A Markov-switching structural vector autoregressive model of boom and bust in the Australian labour market," Empirical Economics, Springer, vol. 49(4), pages 1271-1299, December.

    More about this item

    Keywords

    Technology shocks; Markov switching model; heteroskedasticity;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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