4 edition of Optimal policy with low-probability extreme events found in the catalog.
Optimal policy with low-probability extreme events
Lars E. O. Svensson
Published
2003
by National Bureau of Economic Research in Cambridge, MA
.
Written in
Edition Notes
| Statement | Lars E.O. Svensson. |
| Series | NBER working paper series ;, working paper 10196, Working paper series (National Bureau of Economic Research : Online) ;, working paper no. 10196. |
| Contributions | National Bureau of Economic Research. |
| Classifications | |
|---|---|
| LC Classifications | HB1 |
| The Physical Object | |
| Format | Electronic resource |
| ID Numbers | |
| Open Library | OL3476822M |
| LC Control Number | 2005616366 |
Surprises and low- or unknown-probability, high impact events are excluded from the modeling effort (which is the most common approach), but then it has to be clearly stated that the analysis has neglected one of the key concerns about climate change. The policy conclusions that can be drawn from such analysis would then be very "Optimal Choice of Monetary Policy Instruments in a Simple Stochastic Macro Model," Quarterly Journal of Economics, vol. 84 (May), pp. _____ (). "Rules-of-Thumb for Guiding Monetary Policy," in Open Market Policies and Operating Procedures--Staff Studies. Washington: Board of Governors of the Federal Reserve System, pp.
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Downloadable (with restrictions). The optimal policy response to a low-probability extreme event is examined. A simple policy problem is solved for a sequence of different loss functions: quadratic, combined quadratic/absolute-deviation, absolute-deviation, combined quadratic/constant, and perfectionist.
The Paper shows that, under some simplifying assumptions, each of these loss functions Optimal policy with low-probability extreme events. Cambridge, Mass.: National Bureau of Economic Research, © (OCoLC) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: Lars E O Svensson; National Bureau of Economic :// The optimal policy response to a low-probability extreme event is examined.
A simple policy problem is solved for a sequence of different loss functions: quadratic, combined quadratic/absolute Get this from a library. Optimal policy with low-probability extreme events.
[Lars E O Svensson; National Bureau of Economic Research.] -- "The optimal policy response to a low-probability extreme event is examined. A simple policy problem is solved for a sequence of different loss CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A standard result in optimal-control theory is certainty equivalence, which results under the assumption of a linear model with additive uncertainty and a quadratic loss function.
Certainty equivalence implies that only the mean values (that is, the probability-weighted average outcomes) of ?doi= Downloadable. The optimal policy response to a low-probability extreme event is examined. A simple policy problem is solved for a sequence of different loss functions: quadratic, combined quadratic/absolute-deviation, absolute-deviation, combined quadratic/constant, and perfectionist.
The paper shows that, under some simplifying assumptions, each of these loss functions puts less weight Optimal Policy with Low-Probability Extreme Events Lars Optimal policy with low-probability extreme events book.
Svensson. NBER Working Paper No. Issued in December NBER Program(s):International Finance and Macroeconomics, Monetary Economics The optimal policy response to a low-probability extreme Optimal Policy with Low-Probability Extreme Eve where is a positive constant.
Assume that the instrument has to be set before the future shock is known but after the current state of the economy is observed. Assume that the future shock is the sum of two independently distributed random variables, and.
BibTeX @MISC{Svensson03optimalpolicy, author = {Lars E. Svensson}, title = {Optimal Policy with Low-Probability Extreme Events”, in Macroeconomics, Monetary Policy, and Financial Stability A Festschrift for Charles Freedman, Proceedings of a conference held by ?doi= Low probability, high impact: Policy making and extreme events.
(EWS) models to anticipate extreme events, such as currency crises in emerging markets. We show how the design of an “optimal” model for policy makers focuses on the choice of three parameters: the degree of risk aversion of failing to anticipate an event, the forecast Optimal Policy with Low-Probability Extreme Events.
By Lars E.O. Svensson. Get PDF ( KB) Abstract. The optimal policy response to a low-probability extreme event is examined. A simple policy problem is solved for a sequence of different loss functions: quadratic, combined quadratic/absolute-deviation, absolute-deviation, combined quadratic Optimal Policy with Low-Probability Extreme Events Lars E.O.
Svensson NBER Working Paper No. December JEL No. E52, E58, E61 ABSTRACT The optimal policy response to a low-probability extreme event is examined. A simple policy problem is solved for a sequence of different lo ss functions: quadratic, combined quadratic/ famous text An Introduction to Probability Theory and Its Applications (New York: Wiley, ).
In the preface, Feller wrote about his treatment of fluctuation in coin tossing: “The results are so amazing and so at variance with common intuition that even sophisticated colleagues doubted that coins actually misbehave as theory ~chance/teaching_aids/books_articles/probability_book/ Abstract: The electricity infrastructure is a critical lifeline system and of utmost importance to our daily lives.
Power system resilience characterizes the ability to resist, adapt to, and timely recover from disruptions. The resilient power system is intended to cope with low probability, high risk extreme events including extreme natural disasters and man-made :// Monetary Policy, and Financial Stability – A Festschrift in Honor of Charles (Chuck) Freedman, Proceedings of a conference held by the Bank of Canada, Ottawa, JuneAbstract.
The optimal policy response to a low-probability extreme event is :// Abstract. The optimal policy response to a low-probability extreme event is examined.
A simple policy problem is solved for a sequence of different loss functions: quadratic, combined quadratic/absolute-deviation, absolute-deviation, combined quadratic/constant, and perfec- :// Request PDF | Low probability, high impact: Policy making and extreme events | The objective of this paper is to derive key determinants for the optimal design of early warning system (EWS) models A surprising extreme event relative to the expected occurrence rate (extreme event in the sense that the consequences are large/severe, this understanding also applies to the interpretations 2 and 3 below).
An extreme event with a very low probability. A surprising, extreme event in situations with large uncertainties. An unknown :// Twitter Facebook LinkedIn Email Print Article. Howard Kunreuther and Michael Useem, professors at the Wharton Risk Management and Decision Processes Center, recently released a book called Mastering Catastrophic Risk: How Companies Are Coping with Disruption, for which they interviewed senior executives from multinational spoke to Professor Kunreuther about the biases However, it's not really useful for dealing with high-cost, low-probability events.
If you take the very low odds of an extinction-level asteroid impact, multiply it by the population of the Earth, and factor in a person's life expectancy at birth, you get the mathematically-correct prediction that you've got around a 3% chance of being killed /how-to-deal-with-low-probability-high-impact-risks.
opt(x) f(x)p(x)= is a probability density, and it has ˙2 q opt = 0. It is optimal, but not really usable because ^ becomes an average of f(x i)p(x i)=q(x i) = meaning that we could compute directly from f, p, and qwithout any sampling.
Likewise for f(x) 6 0 with ~owen/mc/In summary, probability deals with patterns and trends that occur in random events. Probability helps us to determine what the likelihood of something happening will be. Statistics and simulations help us to determine the probability with greater accuracy.
Simply put, one could say the probability is the study of chance. It affects so many even when fundamentals vary continuously, optimal policy calls for a discontinuous in-terest rate path. Turning to fiscal policy, I show that, there is a role for government spending dur-ing a liquidity trap.
Spending should be front-loaded. At the start of the liquidity trap, government spending should be higher than its natural
