2019
DOI: 10.2139/ssrn.3494876
|View full text |Cite
|
Sign up to set email alerts
|

Self-Justified Equilibria: Existence and Computation

Abstract: In this paper we introduce "self-justified" equilibrium as a solution concept in stochastic general equilibrium models with a large number of heterogeneous agents. In each period agents trade in assets to maximize the sum of current utility and forecasted future utility. Current prices ensure that markets clear and agents forecast the probability distribution of future prices and consumption on the basis of current endogenous variables and the current exogenous shock. The forecasts are self-justfied in the sen… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
10
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
7
2

Relationship

4
5

Authors

Journals

citations
Cited by 12 publications
(10 citation statements)
references
References 54 publications
0
10
0
Order By: Relevance
“…Off-equilibrium, the PLM and the actual law of motion may diverge, but households never discover it, as this region is never visited. For details about self-justified equilibria, see Kubler and Scheidegger (2018).…”
Section: Maximizing the Likelihoodmentioning
confidence: 99%
“…Off-equilibrium, the PLM and the actual law of motion may diverge, but households never discover it, as this region is never visited. For details about self-justified equilibria, see Kubler and Scheidegger (2018).…”
Section: Maximizing the Likelihoodmentioning
confidence: 99%
“…Our implementation follows Krusell and Smith (1998), in general, and Kubler and Scheidegger (2019), in particular, by condensing the distribution of assets across agents into one state variable.…”
Section: Recursive Formulation Of the Olg Modelmentioning
confidence: 99%
“…An obvious shortcoming of our approach lies in the fact that the sets of admissible forecasts are exogenous. It would be useful to consider a loss function that chooses forecasts to trade off accuracy and the complexity of the forecast (see, e.g., Kübler and Scheidegger (2019)). It is worth noting that forecasting errors for P 3 ca are generally very small, and it seems difficult to argue in favor of the need for more accuracy.…”
Section: Computation In An Olg Economy: Numerical Examplesmentioning
confidence: 99%