arXiv 28 Dec 2018 · Econometrics · 2 citations (OpenAlex)
arXiv:1812.11246 · PDF · DOI · OpenAlex · Extracted main text
This paper studies identification and estimation of a class of dynamic models in which the decision maker (DM) is uncertain about the data-generating process. The DM surrounds a benchmark model that he or she fears is misspecified by a set of models. Decisions are evaluated under a worst-case model delivering the lowest utility among all models in this set. The DM's benchmark model and preference parameters are jointly underidentified. With the benchmark model held fixed, primitive conditions are established for identification of the DM's worst-case model and preference parameters. The key step in the identification analysis is to establish existence and uniqueness of the DM's continuation value function allowing for unbounded statespace and unbounded utilities. To do so, fixed-point results are derived for monotone, convex operators that act on a Banach space of thin-tailed functions arising naturally from the structure of the continuation value recursion. The fixed-point results are quite general; applications to models with learning and Rust-type dynamic discrete choice models are also discussed. For estimation, a perturbation result is derived which provides a necessary and sufficient condition for consistent estimation of continuation values and the worst-case model. The result also allows convergence rates of estimators to be characterized. An empirical application studies an endowment economy where the DM's benchmark model may be interpreted as an aggregate of experts' forecasting models. The application reveals time-variation in the way the DM pessimistically distorts benchmark probabilities. Consequences for asset pricing are explored and connections are drawn with the literature on macroeconomic uncertainty.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Barillas, F., L. P. Hansen, and T. J. Sargent (2009) Doubts or variability? | 1.000 | 6 | 3 | 100% |
| 2 | Bidder, R. and M. Smith (2018) Doubts and variability: A robust perspective on exotic consumption series | 1.000 | 5 | 4 | 100% |
| 3 | Hansen, L. P., J. C. Heaton, and N. Li (2008) Consumption strikes back? Measuring long-run risk | 1.000 | 5 | 4 | 100% |
| 4 | Hansen, L. P. and T. J. Sargent (2008) Robustness | 0.928 | 4 | 3 | 100% |
| 5 | Hansen, L. P., T. J. Sargent, and T. D. Tallarini (1999) Robust permanent income and pricing | 0.928 | 4 | 3 | 100% |
| 6 | Ju, N. and J. Miao (2012) Ambiguity, learning, and asset returns | 0.874 | 6 | 2 | 100% |
| 7 | Klibanoff, P., M. Marinacci, and S. Mukerji (2009) Recursive smooth ambiguity preferences | 0.874 | 5 | 2 | 100% |
| 8 | Hansen, L. P. and T. J. Sargent (2007) Recursive robust estimation and control without commitment | 0.811 | 4 | 2 | 100% |
| 9 | Hansen, L. P. and T. J. Sargent (2010) Fragile beliefs and the price of uncertainty | 0.811 | 4 | 2 | 100% |
| 10 | Bhandari, A., J. Borovicka, and P. Ho (2017) Identifying ambiguity shocks in business cycle models using survey data | 0.737 | 3 | 2 | 100% |
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