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Learning by Doing as a Propagation Mechanism

Author

Listed:
  • Yongsung Chang

    (University of Pennsylvania)

  • Joao Gomes

    (University of Pennsylvania)

  • Frank Schorfheide

    (University of Pennsylvania)

Abstract

This paper suggests that skill accumulation through past work experience, or ``learning-by-doing'' (LBD), can provide an important propagation mechanism in a dynamic stochastic general equilibrium model, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the model's ability to fit the dynamics of aggregate output and hours.

Suggested Citation

  • Yongsung Chang & Joao Gomes & Frank Schorfheide, 2002. "Learning by Doing as a Propagation Mechanism," Macroeconomics 0204002, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpma:0204002
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    References listed on IDEAS

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    More about this item

    Keywords

    Learning by Doing; Propagation Mechanism; Business Cycles; Bayesian Analysis;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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