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Learning efficiency shocks, knowledge capital and the business cycle: A Bayesian evaluation
Author(s) -
Johri Alok,
Karimzada Muhebullah
Publication year - 2021
Publication title -
canadian journal of economics/revue canadienne d'économique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 69
eISSN - 1540-5982
pISSN - 0008-4085
DOI - 10.1111/caje.12526
Subject(s) - economics , business cycle , total factor productivity , productivity , consumption (sociology) , econometrics , learning by doing , shock (circulatory) , investment (military) , demand shock , aggregate (composite) , supply shock , dynamic stochastic general equilibrium , bayesian inference , capital (architecture) , bayesian probability , production (economics) , monetary economics , microeconomics , monetary policy , macroeconomics , computer science , history , political science , medicine , politics , social science , materials science , law , archaeology , sociology , composite material , artificial intelligence
We incorporate shocks to the efficiency with which firms learn from production activity and accumulate knowledge into an otherwise standard real DSGE model with imperfect competition. Using real aggregate data and Bayesian inference techniques, we find that learning efficiency shocks are an important source of observed variation in the growth rate of aggregate output, investment, consumption and especially hours worked in post‐war US data. The estimated shock processes suggest much less exogenous variation in preferences and total factor productivity are needed by our model to account for the joint dynamics of consumption and hours. This occurs because learning efficiency shocks induce shifts in labour demand uncorrelated with current total factor productivity (TFP), a role usually played by preference shocks that shift labour supply. At the same time, knowledge capital acts like an endogenous source of productivity variation in the model. Measures of model fit prefer the specification with learning efficiency shocks. The results are robust to the addition of many observables and shocks.