layout(matrix(c(1, 2), 1, 2, byrow=TRUE), + widths=c(3/5, 2/5)) > plot(1900:2007, L, type="h", xlab="Year", + ylab="Number of Loss Events") > grid() > mtext("a", side=3, line=1, adj=0, cex=1.1) > hist(y, xlab="Loss Amount ($ log)", + ylab="Frequency", main="") > mtext("b", side=3, line=1, adj=0, cex=1.1) . . . " > Jump to ContentJump to Main Navigation
Hurricane ClimatologyA Modern Statistical Guide Using R$
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James B. Elsner and Thomas H. Jagger

Print publication date: 2013

Print ISBN-13: 9780199827633

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780199827633.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 28 November 2021

Impact Models

Impact Models

Chapter:
(p.340) 13 Impact Models
Source:
Hurricane Climatology
Author(s):

James B. Elsner

Thomas H. Jagger

Publisher:
Oxford University Press
DOI:10.1093/oso/9780199827633.003.0017

In this chapter, we show some broader applications of our models and methods. We focus on impact models. Hurricanes are capable of generating large financial losses. We begin with a model that estimates extreme losses conditional on climate covariates. We then describe a method for quantifying the relative change in potential losses over the decades. Financial losses from hurricanes are to some extent directly related to fluctuations in climate. Environmental factors influence the frequency and intensity of hurricanes at the coast as detailed throughout this book (see for example Chapters 7 and 8). So, it is not surprising that these same environmental signals appear in estimates of losses. Here loss is the economic damage associated with a hurricane’s direct impact. A normalization procedure adjusts the loss estimate from a past hurricane to what it would be if the same cyclone struck in a recent year by accounting for inflation and changes in wealth and population over the intervening time, plus a factor to account for changes in the number of housing units exceeding population growth. The method produces loss estimates that can be compared over time (Pielke et al. 2008). Here you focus on losses exceeding one billion ($ U.S.) that have been adjusted to 2005. The loss data are available in Losses.txt in JAGS format (see Chapter 9). Input the data by typing . . . > source("Losses.txt"). . . The log-transformed loss amounts are in the column labeled ‘y’. The annual number of loss events are in the column labeled ‘L’. The data cover the period 1900–2007. More details about these data are given in Jagger et al. (2011). You begin by plotting a time series of the number of losses and a histogram of total loss per event. . . . > layout(matrix(c(1, 2), 1, 2, byrow=TRUE), + widths=c(3/5, 2/5)) > plot(1900:2007, L, type="h", xlab="Year", + ylab="Number of Loss Events") > grid() > mtext("a", side=3, line=1, adj=0, cex=1.1) > hist(y, xlab="Loss Amount ($ log)", + ylab="Frequency", main="") > mtext("b", side=3, line=1, adj=0, cex=1.1) . . .

Keywords:   exploratory analysis, extreme losses, future wind damage, impact models

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