Heterogeneity and the Business Cycle Advances in Macroeconomic Modelling Vincent Sterk UCL , CfM , CEPR Tinbergen Today: Challenges for Macroeconomic Modelling DNB, November 2019
Heterogeneity & Business Cycle Models Since 1980s: strong emphasis on optimizing behavior and expectations ◮ Lucas critique,“conquest of inflation”, etc. development of (New-Keynesian) DSGE models Representative Agent assumption ◮ greatly simplifies computational complexity (distributions not a state) ⋆ estimation, forecasting, quantitative policy analysis, etc.
Heterogeneity & Business Cycle Models Since 1980s: strong emphasis on optimizing behavior and expectations ◮ Lucas critique,“conquest of inflation”, etc. development of (New-Keynesian) DSGE models Representative Agent assumption ◮ greatly simplifies computational complexity (distributions not a state) ⋆ estimation, forecasting, quantitative policy analysis, etc.
Heterogeneity & Business Cycle Models Since 1980s: strong emphasis on optimizing behavior and expectations ◮ Lucas critique,“conquest of inflation”, etc. development of (New-Keynesian) DSGE models Representative Agent assumption ◮ greatly simplifies computational complexity (distributions not a state) ⋆ estimation, forecasting, quantitative policy analysis, etc.
Heterogeneity & Business Cycle Models Since 2000s: growing unease about Representative Agent: vast (and growing) heterogeneity in the micro data ◮ inequality in household income, wealth and consumption ◮ differences in firm size, productivity and markups, measures of “misallocation” and “dynamism” policy makers faced with growing demand to consider distributional consequences growing evidence on non-linearities at the micro level ◮ households: interest rate sensitivities, marginal propensities to consume, labour supply elasticities, etc. ◮ firms: lumpy investment and hiring, etc. growing evidence of heterogeneous responses to aggregate shocks ◮ by age, ownership status, balance sheet characteristics, etc.
Heterogeneity & Business Cycle Models Since 2000s: growing unease about Representative Agent: vast (and growing) heterogeneity in the micro data ◮ inequality in household income, wealth and consumption ◮ differences in firm size, productivity and markups, measures of “misallocation” and “dynamism” policy makers faced with growing demand to consider distributional consequences growing evidence on non-linearities at the micro level ◮ households: interest rate sensitivities, marginal propensities to consume, labour supply elasticities, etc. ◮ firms: lumpy investment and hiring, etc. growing evidence of heterogeneous responses to aggregate shocks ◮ by age, ownership status, balance sheet characteristics, etc.
Heterogeneity & Business Cycle Models Since 2000s: growing unease about Representative Agent: vast (and growing) heterogeneity in the micro data ◮ inequality in household income, wealth and consumption ◮ differences in firm size, productivity and markups, measures of “misallocation” and “dynamism” policy makers faced with growing demand to consider distributional consequences growing evidence on non-linearities at the micro level ◮ households: interest rate sensitivities, marginal propensities to consume, labour supply elasticities, etc. ◮ firms: lumpy investment and hiring, etc. growing evidence of heterogeneous responses to aggregate shocks ◮ by age, ownership status, balance sheet characteristics, etc.
Heterogeneity & Business Cycle Models Since 2000s: growing unease about Representative Agent: vast (and growing) heterogeneity in the micro data ◮ inequality in household income, wealth and consumption ◮ differences in firm size, productivity and markups, measures of “misallocation” and “dynamism” policy makers faced with growing demand to consider distributional consequences growing evidence on non-linearities at the micro level ◮ households: interest rate sensitivities, marginal propensities to consume, labour supply elasticities, etc. ◮ firms: lumpy investment and hiring, etc. growing evidence of heterogeneous responses to aggregate shocks ◮ by age, ownership status, balance sheet characteristics, etc.
Heterogeneity & Business Cycle Models Since 2000s: growing unease about Representative Agent: vast (and growing) heterogeneity in the micro data ◮ inequality in household income, wealth and consumption ◮ differences in firm size, productivity and markups, measures of “misallocation” and “dynamism” policy makers faced with growing demand to consider distributional consequences growing evidence on non-linearities at the micro level ◮ households: interest rate sensitivities, marginal propensities to consume, labour supply elasticities, etc. ◮ firms: lumpy investment and hiring, etc. growing evidence of heterogeneous responses to aggregate shocks ◮ by age, ownership status, balance sheet characteristics, etc.
Heterogeneity & Business Cycle Models Recent years: New generation of Heterogeneous-Agents DSGE models ◮ typically calibrated towards cross-sectional distributions Challenging to solve: need to keep track of time-varying distributions Some popular computational approaches: ◮ Approximate aggregation: assume agents keep track only of certain moments (Krusell and Smith,1998) ◮ Reiter (2009) method: solve model using perturbation, approximate distribution with a histogram ◮ “MIT” shocks: one-time unanticipated shock, solve by computing perfect foresight transition
Heterogeneity & Business Cycle Models Recent years: New generation of Heterogeneous-Agents DSGE models ◮ typically calibrated towards cross-sectional distributions Challenging to solve: need to keep track of time-varying distributions Some popular computational approaches: ◮ Approximate aggregation: assume agents keep track only of certain moments (Krusell and Smith,1998) ◮ Reiter (2009) method: solve model using perturbation, approximate distribution with a histogram ◮ “MIT” shocks: one-time unanticipated shock, solve by computing perfect foresight transition
Heterogeneity & Business Cycle Models Recent years: New generation of Heterogeneous-Agents DSGE models ◮ typically calibrated towards cross-sectional distributions Challenging to solve: need to keep track of time-varying distributions Some popular computational approaches: ◮ Approximate aggregation: assume agents keep track only of certain moments (Krusell and Smith,1998) ◮ Reiter (2009) method: solve model using perturbation, approximate distribution with a histogram ◮ “MIT” shocks: one-time unanticipated shock, solve by computing perfect foresight transition
Some challenges Cannot possibly include all forms of heterogeneity. How to choose? ◮ Which cross-sectional patterns to match? Large-scale heterogeneous-agents model often quite difficult to understand ◮ potentially complex equilibrium feedbacks Monetary and fiscal policy intertwined ◮ breakdown of Ricardian equivalence ◮ seemingly innocuous assumptions on the distribution of factor payments may be very important
Some challenges Cannot possibly include all forms of heterogeneity. How to choose? ◮ Which cross-sectional patterns to match? Large-scale heterogeneous-agents model often quite difficult to understand ◮ potentially complex equilibrium feedbacks Monetary and fiscal policy intertwined ◮ breakdown of Ricardian equivalence ◮ seemingly innocuous assumptions on the distribution of factor payments may be very important
Some challenges Cannot possibly include all forms of heterogeneity. How to choose? ◮ Which cross-sectional patterns to match? Large-scale heterogeneous-agents model often quite difficult to understand ◮ potentially complex equilibrium feedbacks Monetary and fiscal policy intertwined ◮ breakdown of Ricardian equivalence ◮ seemingly innocuous assumptions on the distribution of factor payments may be very important
Road map Goal: highlight some lessons that have been learned on heterogeneity may affect the aggregate business cycle. Set up basic HANK (Heterogeneous Agents New Keynesian) model ◮ idiosyncratic income risk + incomplete insurance ⇒ heterogeneity Compare two extreme, but tractable special cases :
Road map Goal: highlight some lessons that have been learned on heterogeneity may affect the aggregate business cycle. Set up basic HANK (Heterogeneous Agents New Keynesian) model ◮ idiosyncratic income risk + incomplete insurance ⇒ heterogeneity Compare two extreme, but tractable special cases :
Road map Goal: highlight some lessons that have been learned on heterogeneity may affect the aggregate business cycle. Set up basic HANK (Heterogeneous Agents New Keynesian) model ◮ idiosyncratic income risk + incomplete insurance ⇒ heterogeneity Compare two extreme, but tractable special cases :
Model overview Households ◮ face idiosyncratic unemployment risk ⋆ employed: choose labour supply, lose their job with probability p eu ⋆ unemployed: receive benefit ϑ , find job with probability p ue ◮ save in nominal bonds subject to no-borrowing limit: B t ( i ) ≥ 0, Firms ◮ produce, set prices subject to adjustment cost Monetary authority ◮ set nominal interest rate according to rule Fiscal authority
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