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Learning by Doing as a Propagation Mechanism Author info | Abstract | Publisher info | Download info | Related research | Statistics Yongsung Chang (University of Pennsylvania)
Joao Gomes (University of Pennsylvania)
Frank Schorfheide (University of Pennsylvania)
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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.
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Paper provided by EconWPA in its series Macroeconomics with number
0204002.
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Date of creation: 01 May 2002Date of revision:
Handle: RePEc:wpa:wuwpma:0204002Note: Type of Document - pdf; prepared on IBM-PC; to print on HP;Contact details of provider: Web page: http://129.3.20.41
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Keywords: Learning by Doing ; Propagation Mechanism ; Business Cycles ; Bayesian Analysis ; Other versions of this item:
Find related papers by JEL classification: C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing 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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