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Xavier Gabaix:Behavioral Macro Via
Sparse Dynamic Programming
Discussion by Thomas Chaney
Toulouse School of Economics
Banque de France, June, 2016
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 1 / 11
Some perspective
Lucas JET 1972:
1
confronted large unexplained puzzles (Philips curve)
2
confronted lack of micro-foundations for dynamic macro
3
offered novel concept (rational expectation equilibrium)
4
offered tractable tools (dynamic programming)
Gabaix 2014/16:
1
confronts a series of (smaller?) puzzles
2
confronts unease with macro models (full rationality)
3
offers novel concept (sparse bounded rationality)
4
offers tractable tools (sparse dynamic programming)
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 2 / 11
Some perspective
Lucas JET 1972:
1
confronted large unexplained puzzles (Philips curve)
2
confronted lack of micro-foundations for dynamic macro
3
offered novel concept (rational expectation equilibrium)
4
offered tractable tools (dynamic programming)
Gabaix 2014/16:
1
confronts a series of (smaller?) puzzles
2
confronts unease with macro models (full rationality)
3
offers novel concept (sparse bounded rationality)
4
offers tractable tools (sparse dynamic programming)
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 2 / 11
Roadmap
1
Representative agent/heterogeneous agents.
2
Utility accounting.
3
Setting the “default” model.
4
Small comments.
5
A network application.
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 3 / 11
1- Representative agent?
Unlikely a “representative agent” will be sparse BR.
What about the aggregation of sparse BR agents?
You can make progress:
1
Your model: which agent drops which state variable.
2 ,! extensive margin of attention.
3
Composition effect from this extensive margin.
4
Tractability should allow to deal with heterogeneity?
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 4 / 11
1- Representative agent?
Unlikely a “representative agent” will be sparse BR.
What about the aggregation of sparse BR agents?
You can make progress:
1
Your model: which agent drops which state variable.
2 ,! extensive margin of attention.
3
Composition effect from this extensive margin.
4
Tractability should allow to deal with heterogeneity?
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 4 / 11
1- Representative agent? (cont’d)
De-coupling of dimensions?
1
Narrow framing or not? (footnote 51)
2
If I buy a house, do I become aware of the interest rate for other
decisions (e.g. consumption-saving)?
3
Ultimately, an empirical question.
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 5 / 11
1- Representative agent? (cont’d)
Lucas critique:
1
Lucas: what looks like money illusion (Philips curve).
2
Gabaix: sort of the same.
3
Smooth version of Lucas: big/small shocks, aware/unaware agents.
4
Gabaix is empirically relevant version of Lucas.
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 6 / 11
2- Utility accounting
Importance of mental cost for welfare/policy recommendations.
Cost, kg (m), does not appear in preferences (only through A).
Does accounting for mental cost affect time [in]consistency?
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 7 / 11
3- Setting the default model
Very upfront about the arbitrariness of choosing a default model.
But you can say more: how changing the default model affects
behavior!
Example:
1
Tax code tutorials make it the default model (Chetty et al.)
2
Changes in the tax code (with tutorials) change the default model.
3
Gabaix tells us how other shocks interact with the tax code.
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 8 / 11
3- Setting the default model
Very upfront about the arbitrariness of choosing a default model.
But you can say more: how changing the default model affects
behavior!
Example:
1
Tax code tutorials make it the default model (Chetty et al.)
2
Changes in the tax code (with tutorials) change the default model.
3
Gabaix tells us how other shocks interact with the tax code.
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 8 / 11
Smaller comment I: cross partials
Agent weighs utility gain against cognitive cost one state variable at a
time.
What about two variables at once? any combination of them?
Question: Could it be the agent misses out on cross-partial terms?
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 9 / 11
Smaller comment II: outsourcing complexity
Well defined utility cost of contemplating one state variable (kg (mi )).
Well defined benefit as well (could be expressed in income-equivalent).
Question: Could complexity be outsourced?
Example:
1
e.g. g (m) = mawith a = 0 (fixed cost).
2
Economies of scale for a service provider (e.g. if similar clients).
3
Model makes predictions re: when outsourcing is more likely.
4
Refinement: An intermediary can easily cheat a sparse BR agent.
5
Should it be regulated?
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 10 / 11
Smaller comment II: outsourcing complexity
Well defined utility cost of contemplating one state variable (kg (mi )).
Well defined benefit as well (could be expressed in income-equivalent).
Question: Could complexity be outsourced?
Example:
1
e.g. g (m) = mawith a = 0 (fixed cost).
2
Economies of scale for a service provider (e.g. if similar clients).
3
Model makes predictions re: when outsourcing is more likely.
4
Refinement: An intermediary can easily cheat a sparse BR agent.
5
Should it be regulated?
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 10 / 11
Application: Oligopoly in an input-output network
Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 11 / 11