Lars P Hansen - Collected Quotes

"Part of a meaningful quantitative analysis is to look at models and try to figure out their deficiencies and the ways in which they can be improved. A more subtle challenge for statistical methods is to explore systematically potential modeling errors in order to assess the quality of the model predictions. This kind of uncertainty about the adequacy of a model or model family is not only relevant for econometricians outside the model but potentially also for agents inside the models." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"Uncertainty, generally conceived, is not often embraced in public discussions of economic policy. When uncertainty includes incomplete knowledge of dynamic responses, we might well be led away from arguments that 'complicated problems require complicated solutions'. When complexity, even formulated probabilistically, is not fully understood by policy makers, perhaps it is the simpler policies that are more prudent. This could well apply to the design of monetary policy, environmental policy and financial market oversight. Enriching our toolkit to address formally such challenges will improve the guidance that economists give when applying models to policy analysis." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"Using random processes in our models allows economists to capture the variability of time series data, but it also poses challenges to model builders. As model builders, we must understand the uncertainty from two different perspectives. Consider first that of the econometrician, standing outside an economic model, who must assess its congruence with reality, inclusive of its random perturbations. An econometrician’s role is to choose among different parameters that together describe a family of possible models to best mimic measured real world time series and to test the implications of these models. I refer to this as outside uncertainty. Second, agents inside our model, be it consumers, entrepreneurs, or policy makers, must also confront uncertainty as they make decisions. I refer to this as inside uncertainty, as it pertains to the decision-makers within the model. What do these agents know? From what information can they learn? With how much confidence do they forecast the future? The modeler’s choice regarding insiders’ perspectives on an uncertain future can have significant consequences for each model’s equilibrium outcomes." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"When confronted with multiple models, I find it revealing to pose the resulting uncertainty as a two-stage lottery. For the purposes of my discussion, there is no reason to distinguish unknown models from unknown parameters of a given model. I will view each parameter configuration as a distinct model. Thus a model, inclusive of its parameter values, assigns probabilities to all events or outcomes within the model’s domain. The probabilities are often expressed by shocks with known distributions and outcomes are functions of these shocks. This assignment of probabilities is what I will call risk. By contrast there may be many such potential models. Consider a two-stage lottery where in stage one we select a model and in stage two we draw an outcome using the model probabilities. Call stage one model ambiguity and stage two risk that is internal to a model." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"When there is a reference to a decision problem, an analysis with multiple priors can deduce bounds on the expected utility consequences of alternative decisions, and more generally a mapping from alternative priors into alternative expected outcomes." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"Why is it fruitful to consider model misspecification? In economics and as in other disciplines, models are intended to be revealing simplifications, and thus deliberately are not exact characterizations of reality; it is therefore specious to criticize economic models merely for being wrong. The important criticisms are whether our models are wrong in having missed something essential to the questions under consideration." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"For us, a model is a stochastic process, that is, a probability distribution over a sequence of random variables, perhaps indexed by a vector of parameters. For us, model uncertainty includes a suspicion that a model is incorrect." (Lars P Hansen & Thomas J Sargent, "Uncertainty within Economic Models", 2015)

"I view the work I've done related to statistics and economics as roughly speaking, how to do something without having to do everything. So economic models - how any model by definition isn't right. When someone just says, 'Oh, your model is wrong.' That's not much of an insight. What you want to know is, is wrong in important ways or wrong in ways that are less relevant? And you want to know what does the data really say about the model?" (Lars P Hansen)