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What Happens After A Technology Shock? A Bayesian Perspective

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  • Ossama Mikhail

    (University of Central Florida)

Abstract

This paper investigates the effect of a positive technology shock on per capita hours worked within the class of Bayesian Vector Auto-Regressive [BVAR] models. Such a framework avoids the current debate regarding the specification issue of per capita hours [level versus first-difference stationary]. Six priors are considered and for each, we examine the impulse responses of per capita hours following a positive technology shock. The marginal posteriors of the VAR parameters are generated using the Markov Chain Monte Carlo (MCMC) Gibbs sampler. We find that the estimation of the VAR yields significantly different estimates under competing priors. Using the Francis and Ramey (2004, UCSD working paper) new measure for per capita hours, and after imposing the identifying restrictions (i.e., Blanchard-Quah and sign restrictions), the results show that per capita hours worked rise following a positive technology shock - if one [objectively] assumes a non-informative prior.

Suggested Citation

  • Ossama Mikhail, 2005. "What Happens After A Technology Shock? A Bayesian Perspective," Macroeconomics 0510016, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpma:0510016
    Note: Type of Document - pdf; pages: 34
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/mac/papers/0510/0510016.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian Vector Auto-Regression (BVAR); Blanchard-Quah Identification; Markov Chain Monte Carlo (MCMC) Gibbs Sampler; Technology Shock; Real Business Cycle (RBC);
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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