國立高雄大學圖資館 |

Language: English

Back

Data uncertainty: Empirical evidenc...

Aruoba, S. Boragan.

Data uncertainty: Empirical evidence, general-equilibrium implications, and hedging strategies.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data uncertainty: Empirical evidence, general-equilibrium implications, and hedging strategies.
Author:	Aruoba, S. Boragan.
Description:	231 p.
Notes:	Adviser: Francis X. Diebold.
Notes:	Source: Dissertation Abstracts International, Volume: 65-06, Section: A, page: 2287.
Contained By:	Dissertation Abstracts International65-06A.
Subject:	Economics, General.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3137976
ISBN:	0496851462

Data uncertainty: Empirical evidence, general-equilibrium implications, and hedging strategies.
Aruoba, S. Boragan.

Data uncertainty: Empirical evidence, general-equilibrium implications, and hedging strategies. - 231 p.

Adviser: Francis X. Diebold.

Thesis (Ph.D.)--University of Pennsylvania, 2004.

In the first chapter we document the empirical properties of revisions to major macroeconomic variables in the U.S., over the period 1966--2000. We find that these revisions do not have a zero mean, which indicates that the initial announcements by statistical agencies are biased. We also find that the revisions are quite large compared to the original variables. They are predictable using the information set at the time of the initial announcement, which means that the initial announcements of statistical agencies are not rational forecasts. We also provide some evidence that professional forecasters ignore this predictability. Our findings suggest that data revisions in the U.S. do not satisfy simple desirable statistical properties.

ISBN: 0496851462Subjects--Topical Terms:

212429
Economics, General.

Data uncertainty: Empirical evidence, general-equilibrium implications, and hedging strategies.
LDR:02993nmm _2200277 _450 001 162738
005 20051017073518.5
008 090528s2004 eng d
020 $a 0496851462
035 $a 00149239
040 $a UnM $c UnM
100 0 $a Aruoba, S. Boragan. $3 227882
245 1 0 $a Data uncertainty: Empirical evidence, general-equilibrium implications, and hedging strategies.
300 $a 231 p.
500 $a Adviser: Francis X. Diebold.
500 $a Source: Dissertation Abstracts International, Volume: 65-06, Section: A, page: 2287.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2004.
520 # $a In the first chapter we document the empirical properties of revisions to major macroeconomic variables in the U.S., over the period 1966--2000. We find that these revisions do not have a zero mean, which indicates that the initial announcements by statistical agencies are biased. We also find that the revisions are quite large compared to the original variables. They are predictable using the information set at the time of the initial announcement, which means that the initial announcements of statistical agencies are not rational forecasts. We also provide some evidence that professional forecasters ignore this predictability. Our findings suggest that data revisions in the U.S. do not satisfy simple desirable statistical properties.
520 # $a In the third chapter we take the first step in analyzing ways of hedging the risk of data revisions. We show that we can construct portfolios of assets, which are maximally correlated with revisions to macroeconomic variables. The average correlation of the returns of these hedging portfolios and the underlying revision risk are in the order of 0.50, which is very promising in terms of hedging performance.
520 # $3 3 billion, $5 billion of which can be recovered by eliminating the predictability of revisions. Comparing these numbers with the budgets of the major statistical agencies in the US, we conclude that any money spent on the improvement of data collection would be well worth it. $a In the second chapter, using the empirical results from the previous chapter as the motivation, we study the effects of data revisions in a general equilibrium framework. We find that the presence of data revisions, or data uncertainty, creates a precautionary motive and causes significant changes in the decisions of agents. We also find that the model with revisions captures some aspects of the business cycle dynamics of the US data better than the benchmark model with no revisions. Using our model we measure the cost of having data revisions to be about
590 $a School code: 0175.
650 # 0 $a Economics, General. $3 212429
690 $a 0501
710 0 # $a University of Pennsylvania. $3 212781
773 0 # $g 65-06A. $t Dissertation Abstracts International
790 $a 0175
790 1 0 $a Diebold, Francis X., $e advisor
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://libsw.nuk.edu.tw:81/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3137976 $z http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3137976