Published research in the Journal of Economic Theory: “Structured Ambiguity and Model Misspecification”
A decision maker is averse to not knowing a prior over a set of restricted structured models (ambiguity) and suspects that each structured model is misspecified. The decision maker evaluates intertemporal plans under all of the structured models and, to recognize possible misspecifications, under unstructured alternatives that are statistically close to them. Likelihood ratio processes are used to represent unstructured alternative models, while relative entropy restricts a set of unstructured models. A set of structured models might be finite or indexed by a finite-dimensional vector of unknown parameters that could vary in unknown ways over time. We model such a decision maker with a dynamic version of variational preferences and revisit topics including dynamic consistency and admissibility.
@article{hansen2020structured,
title={Structured ambiguity and model misspecification},
author={Hansen, Lars Peter and Sargent, Thomas J},
journal={Journal of Economic Theory},
pages={105165},
year={2020},
publisher={Elsevier}
}
✕ Published paper in the Annual Review of Economics: “Uncertainty Spillovers for Markets and Policy”
Abstract:
We live in a world surrounded by uncertainty. In this essay, I show that featuring this phenomenon more in economic analyses adds to our understanding of how financial markets work and how best to design prudent economic policy. This essay explores methods that allow for a broader conceptualization of uncertainty than is typical in economic investigations. These methods draw on insights from decision theory to engage in uncertainty quantification and sensitivity analysis. Uncertainty quantification in economics differs from most sciences because there is uncertainty both from the perspective of an external observer and from people and enterprises within the model. I illustrate these methods in two example economies in which the understanding of long-term growth is limited. One example looks at uncertainty ramifications for fluctuations in financial markets, and the other considers the prudent design of policy when the quantitative magnitude of climate change and its impact on economic opportunities is unknown.
View Paper in the Annual Review of Economics
@article{hansen2021uncertainty,
title={Uncertainty Spillovers for Markets and Policy},
author={Hansen, Lars Peter},
journal={Annual Review of Economics},
volume={13},
pages={371--396},
year={2021},
publisher={Annual Reviews}
}
✕ Published paper and Jupyter Notebook Available: “Macroeconomic Uncertainty Prices When Beliefs are Tenuous”
Investors face uncertainty over models when they do not know which member of a set of well-defined “structured models” is best. They face uncertainty about mod-els when they suspect that all of the structured models might be misspecified. We refer to worries about the first type of ignorance as ambiguity concerns and worries about the second type as misspecification concerns. These two types of ignorance about probability distributions of risks add what we call uncertainty components to equilibrium prices of those risks. A quantitative example highlights a representa-tive investor’s uncertainties about the size and persistence of macroeconomic growth rates. Our model of preferences under concerns about model ambiguity and misspec-ification puts nonlinearities into marginal valuations that induce time variations in market prices of uncertainty. These reflect the representative investor’s fears of high persistence of low growth rate states and low persistence of high growth rate states.
For the Non-Expert:
@article{hansen2021macroeconomic,
title={Macroeconomic uncertainty prices when beliefs are tenuous},
author={Hansen, Lars Peter and Sargent, Thomas J},
journal={Journal of Econometrics},
volume={223},
number={1},
pages={222--250},
year={2021},
publisher={Elsevier}
}
✕ Published paper in Proceedings of the National Academy of Sciences (PNAS) – “Rational Policymaking During a Pandemic”
Abstract:
Policymaking during a pandemic can be extremely challenging. As COVID-19 is a new disease and its global impacts are unprecedented, decisions need to be made in a highly uncertain, complex and rapidly changing environment. In such a context, in which human lives and the economy are at stake, we argue that using ideas and constructs from modern decision theory, even informally, will make policymaking more a responsible and transparent process.
@article{berger2021rational,
title={Rational policymaking during a pandemic},
author={Berger, Lo{\"\i}c and Berger, Nicolas and Bosetti, Valentina and Gilboa, Itzhak and Hansen, Lars Peter and Jarvis, Christopher and Marinacci, Massimo and Smith, Richard D},
journal={Proceedings of the National Academy of Sciences},
volume={118},
number={4},
year={2021},
publisher={National Acad Sciences}
}
✕ Published paper in Proceedings of the National Academy of Sciences (PNAS) – “Robust Identification of Investor Beliefs”
This paper develops a new method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a novel nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. We illustrate our method with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates.
@article{chen2020robust,
title={Robust identification of investor beliefs},
author={Chen, Xiaohong and Hansen, Lars Peter and Hansen, Peter G},
journal={Proceedings of the National Academy of Sciences},
volume={117},
number={52},
pages={33130--33140},
year={2020},
publisher={National Acad Sciences}
}
✕ Repercussions of Pandemics on Markets and Policy
Abstract:
The COVID-19 pandemic that we are experiencing is both tragic and shocking. There is no question that, except in some Asian countries trained by prior infectious outbreaks, most policy makers around the world have been ill-prepared to respond to the crisis. The effects of the coronavirus on our mental and physical health has been indeed calamitous, and the economic and financial impacts for many have been truly unfortunate. Furthermore, the extreme nature of the event is challenging researchers to compile and interpret new evidence that is arriving at a rapid pace. The editors Hui Chen, Thierry Foucault, Jeffrey Pontiff, and Nikolai Roussanov and contributing authors are to be commended for assembling and collating a thought-provoking collection of papers. More time and study will be needed to fully sift through the evidence and to glean the lessons to be learned from this pandemic for policy makers and investors. But the evidence and insights in this volume are a very good start.
All papers of this volume are posted online on the Oxford University Press website.
@article{peter2020repercussions,
title={Repercussions of Pandemics on Markets and Policy},
author={Peter Hansen, Lars},
journal={The Review of Asset Pricing Studies},
volume={10},
number={4},
pages={569--573},
year={2020},
publisher={Oxford University Press}
}
✕ Using Quantitative Models to Guide Policy Amid COVID-19 Uncertainty
Abstract:
Policymakers look to forecasts or projections about the future evolution of contagion and subsequent fatalities to guide their policy choices. These can be best guesses or warnings about how bad things could become. These considerations factor into their decision making in at least informal ways. Epidemiologists no doubt have important insights that we all look to digest. Economists and other social scientists are quick to consider ways by which they can draw upon their current stock of knowledge to incorporate endogenous responses of individuals and businesses to various policy alternatives. Quantitative predictions of disease transmission under alternative policies and the resulting social behaviors, however, bring special challenges. The reason is that models require specific assumptions and ingredients that govern the dynamic evolution and consequences of alternative forms of social and economic interactions. Subjective judgements are unavoidable. There are unknown parameters to calibrate in the face of limited data. These challenges are pervasive in quantitative modeling that aims to support policy. The unique challenges of the COVID-19 global situation are what draws our attention as we witness and participate in this harrowing episode.
Originally published by the Becker Friedman Institute in April 2020
@article{hansen2020using,
title={Using quantitative models to guide policy amid COVID-19 uncertainty},
author={Hansen, Lars Peter},
journal={White Paper, Becker Friedman Institute, Chicago},
year={2020}
}
✕ Published paper and Python Code – “Pricing Uncertainty Induced by Climate Change”
Geophysicists examine and document the repercussions for the earth’s climate induced by alternative emission scenarios and model specifications. Using simplified approximations, they produce tractable characterizations of the associated uncertainty. Meanwhile, economists write simplified damage functions to assess uncertain feedbacks from climate change back to the economic opportunities for the macroeconomy. How can we assess both climate and emissions impacts, as well as uncertainty in the broadest sense, in social decision-making? We provide a framework for answering this question by embracing recent decision theory and tools from asset pricing, and we apply this structure with its interacting components in a revealing quantitative illustration.
Associated Results and Python Scripts – GitHub
@article{BarnettBrockHansen:2020,
Author = {Michael Barnett and William Brock and Lars Peter Hansen},
Date-Added = {2019-11-08 10:47:02 -0600},
Date-Modified = {2019-11-08 10:49:54 -0600},
Journal = {Review of Financial Studies},
Title = {Pricing Uncertainty Induced by Climate Change},
Year = {March 2020, The Review of Financial Studies}}
✕Published Paper: “Twisted Probabilities, Uncertainty and Prices”
A decision maker constructs a convex set of nonnegative martingales to use as likeli-hood ratios that represent alternatives that are statistically close to a decision maker’s baseline model. The set is twisted to include some specific models of interest. Max-min expected utility over that set gives rise to equilibrium prices of model uncertainty expressed as worst-case distortions to drifts in a representative investor’s baseline model. Three quantitative illustrations start with baseline models having exogenous long-run risks in technology shocks. These put endogenous long-run risks into con-sumption dynamics that differ in details that depend on how shocks affect returns to capital stocks. We describe sets of alternatives to a baseline model that generate countercyclical prices of uncertainty.
Keywords— Risk, uncertainty, relative entropy, robustness, asset prices, exponential quadratic stochastic discount factor
JEL Classification— C52, C58, D81, D84, G12
Associated Paper Results and Code
@article{hansen2020twisted,
title={Twisted probabilities, uncertainty, and prices},
author={Hansen, Lars Peter and Sz{\H{o}}ke, B{\'a}lint and Han, Lloyd S and Sargent, Thomas J},
journal={Journal of Econometrics},
volume={216},
number={1},
pages={151--174},
year={2020},
publisher={Elsevier}
}
✕ Aversion to Ambiguity and Model Misspecification in Dynamic Stochastic Environments
In many dynamic economic settings, a decision maker finds it challenging to quantify the uncertainty or to assess the potential for mistakes in models. We explore alternative ways of acknowledging these challenges by drawing on insights from decision theory as conceptualized and implemented in statistics, engineering, and economics. Building on prior research, we suggest tractable and revealing ways to incorporate behavioral responses to uncertainty, broadly conceived. Our analysis adopts recursive intertemporal preferences for decision makers that allow them to be ambiguity averse and concerned about the potential misspecification of subjective uncertainty. By design, these representations have revealing implications for continuous-time environments with Brownian information structures. Problems where uncertainty’s structure is obscure such as macroeconomics, finance and climate change are promising areas for application of these tools.
@article{hansen:2018aversion,
title={Aversion to Ambiguity and Model Misspecification in Dynamic Stochastic Environments},
author={Hansen, L. P. and Miao, J.},
journal={Proceedings of the National Academy of Sciences},
volume={115},
number={37},
pages={9163–9168},
year={2018},
publisher={National Academy of Sciences}
}