Carbon Prices and Forest Preservation Over Space and Time in the Brazilian Amazon
Abstract:
Some portions of land in the Brazilian Amazon are forested, and other portions are used in agricultural activities, principally cattle-ranching. Deforestation emits carbon, and reforestation captures it. Both are consequential for the global climate. The social and private productivities for the alternative land uses vary across locations within the Amazon region. In this research, we build and analyze a spatial/dynamic model of socially efficient land allocation to establish a benchmark for ad-hoc policies. We incorporate the stochastic evolution of cattle prices into our analysis, and we explore the consequences of ambiguity in the location-specific productivities on the socially efficient policy. Finally, we assess the consequences of imposing alternative social costs of carbon emissions on the spatial/dynamic allocation of land use. Our results indicate that even modest transfers per ton of net CO2 would incentivize Brazil to choose policies that produce substantial capture of greenhouse gases in the next 30 years. Our analysis points to the management of tropical forests as an important contributor to climate change mitigation in the near future.
We thank Pengyu Chen, Bin Cheng, Patricio Hernandez, João Pedro Vieira, Daniel (Samuel) Zhao for their expert research assistance and to Joanna Harris and Diana Petrova for their helpful comments.
@article{assunccao2023carbon,
title={Carbon Prices and Forest Preservation Over Space and Time in the Brazilian Amazon},
author={Assun{\c{c}}{\~a}o, Juliano J and Hansen, Lars Peter and Munson, Todd and Scheinkman, Jos{\’e} A},
journal={Available at SSRN 4414217},
year={2023}
}
✕Newly published in the Journal of Econometrics: “Robust Inference for Moment Condition Models without Rational Expectations”
Abstract
Applied researchers using structural models under rational expectations (RE) often confront empirical evidence of misspecification. In this paper we consider a generic dynamic model that is posed as a vector of unconditional moment restrictions. We suppose that the model is globally misspecified under RE, and thus empirically flawed in a way that is not econometrically subtle. We relax the RE restriction by allowing subjective beliefs to differ from the data-generating probability (DGP) model while still maintaining that the moment conditions are satisfied under the subjective beliefs of economic agents. We use statistical measures of divergence relative to RE to bound the set of subjective probabilities. This form of misspecification alters econometric identification and inferences in a substantial way, leading us to construct robust confidence sets for various set identified functionals.
JEL Classification: C14, C15, C31, C33, G40
Keywords: Subjective beliefs, bounded rationality, misspecification sets, nonlinear expectation, divergence, Lagrange multipliers, stochastic dual programming, confidence sets
@article{chen2021robust,
title={Robust Inference for Moment Condition Models Without Rational Expectations},
author={Chen, Xiaohong and Hansen, Lars Peter and Hansen, Peter G.},
journal={Journal of Econometrics, forthcoming},
year={2021}
}
New Working Paper: “A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty”
Abstract:
We study the implications of model uncertainty in a climate-economics framework with three types of capital: “dirty” capital that produces carbon emissions when used for production, “clean” capital that generates no emissions but is initially less productive than dirty capital, and knowledge capital that increases with R&D investment and leads to technological innovation in green sector productivity. To solve our high-dimensional, non-linear model framework we implement a neural-network-based global solution method. We show there are first-order impacts of model uncertainty on optimal decisions and social valuations in our integrated climate-economic-innovation framework. Accounting for interconnected uncertainty over climate dynamics, economic damages from climate change, and the arrival of a green technological change leads to substantial adjustments to investment in the different capital types in anticipation of technological change and the revelation of climate damage severity.
@article{barnett2023deep,
title={A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty},
author={Barnett, Michael and Brock, William and Hansen, Lars Peter and Hu, Ruimeng and Huang, Joseph},
journal={arXiv preprint arXiv:2310.13200},
year={2023}
}
New Working Paper: “How Should Climate Change Uncertainty Impact Social Valuation and Policy?”
Abstract:
We study the uncertain transition to a carbon-neutral economy. The requisite technological innovation is made more probable through research and development (R&D). We explore multiple channels of economic and geoscientific uncertainties that impact this transition, and we show how to assess the relative importance of their varied contributions. We represent the social benefit of R&D and cost of global warming as expected discounted values of social payoffs using a probability measure adjusted for concerns about model misspecification and prior ambiguity. Our quantitative results show the value of R&D investment even when the timing of its technological success is highly uncertain.
@article{barnett2023should,
title={How Should Climate Change Uncertainty Impact Social Valuation and Policy?},
author={Barnett, Michael and Brock, William A. and Hansen, Lars Peter and Zhang, Hong},
journal={University of Chicago, Becker Friedman Institute for Economics Working Paper},
number={2023-140},
year={2023}
}
Newly published in the Journal of Applied Econometrics: “Risk, Ambiguity, and Misspecification: Decision Theory, Robust Control, and Statistics”
Abstract
What are “deep uncertainties,” and how should their presence influence prudent decisions? To address these questions, we bring ideas from robust control theory into statistical decision theory. Decision theory has its origins in axiomatic formulations by von Neumann and Morgenstern, Wald, and Savage. After Savage, decision theorists constructed axioms that formalize a notion of ambiguity aversion. Meanwhile, control theorists constructed decision rules that are robust to some model misspecifications. We reinterpret axiomatic foundations of decision theories to express ambiguity about a prior over a family of models along with concerns about misspecifications of the corresponding likelihood functions.
Keywords— deep uncertainty, ambiguity, misspecification, variational preferences, statistical divergence, relative entropy, prior, likelihood
JEL Codes— C10, C14, C18
@article{hansen2022risk,
title={Risk, Ambiguity, and Misspecification: Decision Theory, Robust Control, and Statistics},
author={Hansen, Lars Peter and Sargent, Thomas J.},
journal={University of Chicago, Becker Friedman Institute for Economics Working Paper},
number={2022-157},
year={2022}
}
Working Paper: “Making Decisions Under Model Misspecification”
We use decision theory to confront uncertainty that is sufficiently broad to incorporate “models as approximations.” We presume the existence of a featured collection of what we call “structured models” that have explicit substantive motivations. The decision maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max-min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision-making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns.
This research received the Best Paper Award at the 1st MUSEES Conference, as presented by my co-author, Fabio Maccheroni.
@article{cerreia2020making,
title={Making decisions under model misspecification},
author={Cerreia-Vioglio, Simone and Hansen, Lars Peter and Maccheroni, Fabio and Marinacci, Massimo},
journal={University of Chicago, Becker Friedman Institute for Economics Working Paper},
number={2020-103},
year={2020}
}
✕ Uncertainty in Economic Analysis and the Economic Analysis of Uncertainty
This essay was written for the inaugural issue of a journal Called KNOW, published in conjunction with the Stevanovich Institute for the Formation of Knowledge. I explore why addressing uncertainty in our knowledge is especially important in economic analyses when we seek a better understanding of markets, economic outcomes, and the impact of alternative policies. I also provide some historical context to the formalization of the alternative components to uncertainty and their impact in economic analyses. It has been important in economic scholarship to take inventory, not only of what we know, but also of the gaps in this knowledge. Thus, part of economic research assesses what we know about what we do not know and how we confront what we do not know. Not only does uncertainty matter for how economic researchers interpret and use evidence, but also for how consumers and enterprises we incorporate in models confront the future.
@article{hansen:2017uncertainty,
title={Uncertainty in Economic Analysis and the Economic Analysis of Uncertainty},
author={Hansen, L. P.},
journal={KNOW: A Journal on the Formation of Knowledge},
volume={1},
number={1},
pages={171–197},
year={2017},
publisher={University of Chicago Press}
}
Modeling and Measuring Systemic Risk
An important challenge worthy of NSF support is to quantify systemic financial risk. There are at least three major components to this challenge: modeling, measurement, and data accessibility. Progress on this challenge will require extending existing research in many directions and will require collaboration between economists, statisticians, decision theorists, sociologists, psychologists, and neuroscientists.
@article{bhkkl:2010,
title={Modeling and Measuring Systemic Risk},
author={Brunnermeier, Markus K. and Hansen, Lars Peter and Kashyap, Anil K. and Krishnamurthy, Arvind and Lo, Andrew W},
year={2010}
}
✕ Nonlinear Principal Components and Long-Run Implications of Multivariate Diffusions
We investigate a method for extracting nonlinear principal components. These principal components maximize variation subject to smoothness and orthogonality constraints; but we allow for a general class of constraints and densities, including densities without compact support and even densities with algebraic tails. We provide primitive sufficient conditions for the existence of these principal components. We also characterize the limiting behavior of the associated eigenvalues, the objects used to quantify the incremental importance of the principal components. By exploiting the theory of continuous-time, reversible Markov processes, we give a different interpretation of the principal components and the smoothness constraints. When the diffusion matrix is used to enforce smoothness, the principal components maximize long-run variation relative to the overall variation subject to orthogonality constraints. Moreover, the principal components behave as scalar autoregressions with heteroskedastic innovations. Finally, we explore implications for a more general class of stationary, multivariate diffusion processes.
@article{hansen2000principal,
title={Principal Components and the Long Run},
author={Xiaohong Chen, Lars Peter Hansen, and José A. Scheinkman},
year={2000},
publisher={Citeseer}
}
✕ Risk and Robustness in Equilibrium
@article{anderson1998risk,
title={Risk and Robustness in General Equilibrium},
author={Anderson, Evan W and Hansen, Lars Peter and Sargent, Thomas J},
journal={Preprint University of Chicago},
year={1998}
}
✕