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Uncertainty, information acquisition and price swings in asset markets. Review of Economic Studies, forthcoming
, 2015
"... This article analyses costly information acquisition in asset markets with Knightian uncertainty about the asset fundamentals. In these markets, acquiring information not only reduces the expected variability of the fundamentals for a given distribution (i.e. risk). It also mitigates the uncertainty ..."
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This article analyses costly information acquisition in asset markets with Knightian uncertainty about the asset fundamentals. In these markets, acquiring information not only reduces the expected variability of the fundamentals for a given distribution (i.e. risk). It also mitigates the uncertainty about the true distribution of the fundamentals. Agents who lack knowledge of this distribution cannot correctly interpret the information other investors impound into the price. We show that, due to uncertainty aversion, the incentives to reduce uncertainty by acquiring information increase as more investors acquire information. When uncertainty is high enough, information acquisition decisions become strategic complements and lead to multiple equilibria. Swift changes in information demand can drive large price swings even after small changes in Knightian uncertainty.
Risksensitivity in bayesian sensorimotor integration
 PLoS Comput Biol
"... Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decisionmakers are, however, riskneutral in the sense that they weigh all possibilities based on prior expectation and sensory e ..."
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Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decisionmakers are, however, riskneutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risksensitive decisionmakers are sensitive to model uncertainty and bias their decisionmaking processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risksensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects ’ response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risksensitive decisionmaking processes that allow for deviations from Bayes optimal decisionmaking in the face of uncertainty. Our results suggest that both Bayesian integration and risksensitivity are important factors to understand sensorimotor integration in a quantitative fashion.
Central Bank Macroeconomic Forecasting during the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences
, 2014
"... This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New Y ..."
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This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
The Double Gaussian Approximation for High Frequency Data
, 2011
"... High frequency data have become an important feature of many areas of research. They permit the creation of estimators in highly nonparametric classes of continuoustime models. In the context of continuous semimartingale models, we here provide a locally parametric “double Gaussian ” approximatio ..."
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High frequency data have become an important feature of many areas of research. They permit the creation of estimators in highly nonparametric classes of continuoustime models. In the context of continuous semimartingale models, we here provide a locally parametric “double Gaussian ” approximation, to facilitate the analysis of estimators. As in Mykland and Zhang (2009), the error in the approximation can be offset with a postasymptotic likelihood correction. The current approximation is valid in large neighborhoods, permitting a sharp analysis of estimators that use local behavior over asymptotically increasing numbers of observations.
Denmark Statistical vs. Economic Significance in Economics and Econometrics: Further comments
"... I comment on the controversy between McCloskey & Ziliak and Hoover & Siegler on statistical versus economic significance, in the March 2008 issue of the Journal of Economic Methodology. I argue that while McCloskey & Ziliak are right in emphasizing ’real error’, i.e. nonsampling error t ..."
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I comment on the controversy between McCloskey & Ziliak and Hoover & Siegler on statistical versus economic significance, in the March 2008 issue of the Journal of Economic Methodology. I argue that while McCloskey & Ziliak are right in emphasizing ’real error’, i.e. nonsampling error that cannot be eliminated through specification testing, they fail to acknowledge those areas in economics, e.g. rational expectations macroeconomics and asset pricing, where researchers clearly distinguish between statistical and economic significance and where statistical testing plays a relatively minor role in model evaluation. In these areas models are treated as inherently misspecified and, consequently, are evaluated empirically by other methods than statistical tests. I also criticise McCloskey & Ziliak for their strong focus on the size of parameter estimates while neglecting the important question of how to obtain reliable estimates, and I argue that significance tests are useful tools in those cases where a statistical model serves as input in the quantification of an economic model. Finally, I provide a specific example from economics asset return predictability where the distinction between statistical and
RISK MANAGEMENT IN ACTION  ROBUST MONETARY POLICY RULES UNDER STRUCTURED UNCERTAINTY
, 2008
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Robust Control An Entry for the New Palgrave, 2nd Edition
"... Robust control considers the design of decision or control rules that fare well across a range of alternative models. Thus robust control is inherently about model uncertainty, particularly focusing on the implications of model uncertainty for decisions. Robust control originated in the 1980s in the ..."
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Robust control considers the design of decision or control rules that fare well across a range of alternative models. Thus robust control is inherently about model uncertainty, particularly focusing on the implications of model uncertainty for decisions. Robust control originated in the 1980s in the control theory branch of the engineering and applied mathematics literature, and it is now perhaps the dominant approach in control theory. Robust control gained a foothold in economics in the late 1990s and has seen increasing numbers of economic applications in the past few years. 1 The basic issues in robust control arise from adding more detail to the opening sentence above – that a decision rule performs well across alternative models. To begin, define a model as a specification of a probability distribution over outcomes of interest to the decision maker, which is influenced by a decision or control variable. Then model uncertainty simply means that the decision maker faces subjective uncertainty about the specification of this probability distribution. A first key issue in robust control then is to specify the class of alternative models which the decision maker entertains. As we discuss below, there are many approaches to doing so, with the most common cases taking
Financial Stability and Optimal InterestRate Policy∗
, 2015
"... We study optimal interestrate policy in a New Keynesian model in which the economy is at risk of experiencing a financial crisis and the probability of a crisis depends on credit conditions. The optimal adjustment to interest rates in response to credit conditions is (very) small when the model is ..."
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We study optimal interestrate policy in a New Keynesian model in which the economy is at risk of experiencing a financial crisis and the probability of a crisis depends on credit conditions. The optimal adjustment to interest rates in response to credit conditions is (very) small when the model is calibrated to match an estimated historical relationship between credit conditions, output, inflation and the likelihood of financial crises. Given the imprecise estimates of a number of key parameters, we also study optimal policy taking parameter uncertainty into account. We find that both Bayesian and robustcontrol central banks will respond more aggressively to financial stability risks when the probability and severity of financial crises are uncertain.
Incomplete Markets, Knightian Uncertainty, and Irreversible Investment ∗
, 2009
"... The problem of irreversible investment with idiosyncratic risk is studied by interpreting market incompleteness as a source of Knightian uncertainty over the appropriate discount factor. Maxmin utility over multiple priors is used to solve the irreversible investment problem. The notion of priors wi ..."
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The problem of irreversible investment with idiosyncratic risk is studied by interpreting market incompleteness as a source of Knightian uncertainty over the appropriate discount factor. Maxmin utility over multiple priors is used to solve the irreversible investment problem. The notion of priors with κignorance are used to analyse finitely lived options. For infinitely lived options the notion of constant κignorance is introduced. For these sets of density generators the corresponding optimal stopping problem is solved for general (in) finite horizon optimal stopping problems driven by geometric Brownian motions. It is argued that an increase in the level of ambiguity delays investment, whereas an increase in the degree of market completeness can have a nonmonotonic effect.
Do central banks ’ forecasts take into account public opinion and views?
, 2013
"... NOTE: International Finance Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. References to International Finance Discussion Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the auth ..."
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NOTE: International Finance Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. References to International Finance Discussion Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author. Recent IFDPs are available on the Web at www.federalreserve.gov/pubs/ifdp/.Do central banks ’ forecasts take into account public opinion and views?