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Heterogeneity and Uemployment Dynamics.

Ahn, Hie Joo.

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Heterogeneity and Uemployment Dynamics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Heterogeneity and Uemployment Dynamics./
作者:	Ahn, Hie Joo.
面頁冊數:	208 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-11(E), Section: A.
Contained By:	Dissertation Abstracts International76-11A(E).
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3709234
ISBN:	9781321848137

Heterogeneity and Uemployment Dynamics.
Ahn, Hie Joo.

Heterogeneity and Uemployment Dynamics. - 208 p.

Source: Dissertation Abstracts International, Volume: 76-11(E), Section: A.

Thesis (Ph.D.)--University of California, San Diego, 2015.

This dissertation consists of three papers about unemployment dynamics. The first chapter is "Heterogeneity and unemployment dynamics", the second chapter is "The role of observed and unobserved heterogeneity in the duration of unemployment spells" and the last chapter is "Forecasting unemployment using Dynamic Model Adaptation".

ISBN: 9781321848137Subjects--Topical Terms:

517137
Economics.

Heterogeneity and Uemployment Dynamics.
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300 $a 208 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-11(E), Section: A.
500 $a Adviser: James D. Hamilton.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2015.
520 $a This dissertation consists of three papers about unemployment dynamics. The first chapter is "Heterogeneity and unemployment dynamics", the second chapter is "The role of observed and unobserved heterogeneity in the duration of unemployment spells" and the last chapter is "Forecasting unemployment using Dynamic Model Adaptation".
520 $a The first chapter develops new estimates of flows into and out of unemployment that allow for unobserved heterogeneity across workers as well as direct effects of unemployment duration on unemployment-exit probabilities. Unlike any previous paper in this literature, we develop a complete dynamic statistical model that allows us to measure the contribution of different shocks to the short-run, medium-run, and long-run variance of unemployment as well as to specific historical episodes. We find that changes in the inflows of newly unemployed are the key driver of economic recessions and identify an increase in permanent job loss as the most important factor.
520 $a Then the second chapter explores the role of observed and unobserved heterogeneity in explaining both cross-sectional differences across individuals in the duration of unemployment as well as changes in the average duration of unemployment over time. Using CPS micro data I construct for each month the number of individuals who have been looking for work for 1 month, the number looking for work for 2-3 months, the number looking for 4-6 months, and so on, for people grouped according to a variety of observable characteristics. I use a dynamic accounting identity to infer from these vector-valued time series changes in inflows and outflows of different unobserved types of workers within a given observed category. I propose new strategies to explicitly quantify the contribution of unobserved heterogeneity to unemployment duration in the aggregate as well as across individuals. Unobserved heterogeneity explains about one third of the aggregate dispersion in ongoing duration spells of unemployment and 40$\%$ of the cross-sectional dispersion in completed duration spells over the 1980-2013 period. The compositional shift of unobserved types is a crucial factor raising the mean duration in progress during the Great Recession. By contrast, observed heterogeneity makes only a minor contribution to either cross-sectional or time-series variation.
520 $a The last chapter proposes a new method of combining forecasts based on the recent performance of out-of-sample forecasts for forecasting the U.S. unemployment rate. At every period, a forecaster chooses a single model of which the recent out-of-sample forecasts yields the smallest squared error among a given set of forecasting models to make multiple-period ahead forecasts. The proposed combination method produces more accurate forecasts than existing model averaging methods and the Greenbook forecasts.
590 $a School code: 0033.
650 4 $a Economics. $3 517137
650 4 $a Labor economics. $3 642730
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710 2 $a University of California, San Diego. $b Economics. $3 1022410
773 0 $t Dissertation Abstracts International $g 76-11A(E).
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791 $a Ph.D.
792 $a 2015
793 $a English
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