north carolina unemployment rate
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7/23/2019 North Carolina Unemployment rate
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North Carolina UnemploymentRate
Presenters:
x
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Outline• Introduction
• Data Analysis• Forecasting Models
• Summary
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Why do people work?
• Meet your needs
• Jobs pay salary
•
The greater theresponsibility of jobhigher the salary
•
Socially meet newpeople
• eople enjoy wor!ing
•
urpose of life
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What is Unemployment?
• eople able"a#ailable and willingto $nd wor! andacti#ely see!ingwor!" but notemployed
• The unemployed areincluded in the laborforce
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Types of Unemployment
Who Is NOTCounted as
unemployed?
TheUnderemployed Those with part%timeor seasonal jobs who
would rather ha#efull%time jobs&
Disoura!edWorkers Those whoha#e o'cially gi#enup loo!ing for wor!
and are discouraged&
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"easurin!Unemployment
()&(
*+,&-
,&.
Totals in Thousands" #alues seasonally adjusted
http/00www&frbsf&org0education0teacher%resources0datapost F12SF 3conomic3ducation 4roup
To be counted in 5Semployment data" sur#eyed
persons must be
age *6 or older78nemployed9 includessur#eyed persons who are
jobless and acti#ely see!ing a jobSur#eyed persons who are
neither 7employed” nor7unemployed” are considered
“not in the labor force9
:ot inlaborforce
3mployed
8nemployed
As of February );*<" :5unemployment was <&-= of the
labor force
Source / http/00www&bls&go#0eag0eag&nc&htm
US Bureau of Labor Statistics measures
unemployment using Current popularsurvey(CPS) and Current employmentstatistics survey (CES)
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;&;
)&;
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6&;
,&;
*;&;
*)&;
Time
Unemployment rate #
4 r e a t 1 e c e
s s i o n
Unemployment RateSeasonally adjusted ?=@" Jan& );;<%Dec& );*+
Source / http/00data&bls&go#0timeseries0ASST-.;;;;;;;;;;;;-Bdata toolE4table
"ar$%&%&&'(#
De$%&)*')#
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Data +nalysis
• First datasetrepresents the
monthlyunemployment ratefrom January );;( toDecember );*+
• Data after the greatrecession
Jan%;( Jan%*; Jan%** Jan%*) Jan%*- Jan%*+ Jan%*<<
6
.
,
(
*;
**
*)
8nemployment 1ate ?=@
Time
1ate
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Data +nalysis
%*&)
%;&,
%;&+
;&;
;&+
;&,
*&)
Autocorrelation Function for Monthly 8nemployment 1ate
ag
Autocorrelation
OutsideUC,
Trendpattern
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-oreastin! "odelsonsidered
• Decomposition Method
• Multiple inear 1egression Method – Month" ear and abor Force
– 8nited State 8nemployment" :orth5arolina opulation 4rowth 1ate and
Time
– 8nited States 8nemployment and Time
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Deomposition "ethod.,inear
;
)
+
6
,
*;
*)f/01 2 . %'%30 4 &&'35R6 2 %'37
8nemployment 1ate?=@
Time
8nemployment rate
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Deomposition "ethod.80ponential
; *; ); -; +; <; 6; .; ,;;)
+
6
,
*;
*)f?x@ *)&++ exp? %;&;* x @1G ;&,+
8nemployment 1ate?=@
Time
8nemployment rate
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Deomposition "ethodPolynomial
;
)
+
6
,
*;
*)
f?x@ % ;xH) ;&;-x *;&<(
1G ;&(,
8nemployment 1ate?=@
Time
8nemployment 1ate
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Deomposition method-oreastin! 8rrors
8rrors ,inear80ponenti
alPolynomia
l
"+D ;&;6* ;&6;( ;&);-
"98 ;&-.) ;&<6* ;&;.)
"+P8 # ;&;6* ;&;.* ;&;)-
"P8 # %;&;;. %;&;*- %;&;**
,owest8rrors
Polynomial -ittin! ,ine8uation is
y %;&;;*+x) ;&;).(x *;&<(*
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-oreastin! Usin! PolynomialDeomposition "ethod
;
)
+
6
,
*;
*)
f?x@ % ;xH) ;&;-x *;&<(1G ;&(,
8nemployment 1ate?=@"onth UR # ;an.&* *'&7
-e<.&* )'55
"ar.&* )'3&
+pr.&* )'=(
"ay.&* )'))
;un.&* )'$*
;ul.&* )'%=
+u!.&* ('3=
9ep.&* ('=7
Ot.&* (')7No>.&* ('$=
De.&* ('%=
This model does not catchthe cyclic pattern and !eep
on decreasing
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"ultiple ,inear Re!ression"ethod "odel.&
• redictor ariables used
- ear ?E*@" Month?E)@ and aborForce?E-@
May ha#e multi%collinearity between ear K abor force
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Re!ression +nalysis Result"odel.&
+ outliers" 1emo#ingoutliers might mis%represent the data pattern
• Ligh 1) alue• Signi$cance
#alues not in thecritical range
• :o sign change
between 8 and
• 1eject L;
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alidation of "odel
SpreadAround not
random andseems tofollow a
pattern whichis not good
1) ;&(,
5; ;5* *
Nhich is good
- ti i " d l
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-oreastin! usin! "odel.&
Forecasting eOuation
Model redicts unemployment rate will go down
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Data +nalysis
• Second dataset represents the annuallyunemployment rate from *((; to );*-
• To ensure to see long term pattern
*((; *((- *((6 *((( );;) );;< );;, );** );*++
<
6.
,
(
*;
**
*)
f?x@ ;&*(x % -,-&+.
1G ;&-(
8nemployement rate ?=@
ear
1ate
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Data +nalysis
* ) - + < 6 . , ( *; ** *) *- *+ *< *6 *. *, *( ); )* )) )-
%*&;
%;&,
%;&6
%;&+
%;&)
;&;
;&)
;&+
;&6
;&,
*&;Autocorrelation Function for Annually 8nemployment 1ate
ag
Autocorrelation
Cylialpattern
/une>endistane
peaks andlows1
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"ultiple linear Re!ression"ethod "odel.$
• redictor ariables used- Time ?E*@" :5 opulation growth rate?E)@ and 8S
unemployment rate ?E-@
Collinearity "atri0 A B& B$ B(
NC Unemployment rate/A1
& %'=$( .%'=%= %'5(3
Time /B&1 & .%')(5 %'(7(NC Population !rowthrate /B$1
& .%'*%)
U9 Unemployment rate/B(1
&
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Re!ression +nalysis Result"odel.$
Ligh 1)
#alue shows highcorrelation between andEPs
Signi$cance #alue greaterthan <= " Sign changebetween upper and ower
limitsFails Durbin QNatson Testwhich means data could
ha#e auto%correlation
Dropping :5 population 4rowthrate and re%doing regression
analysis
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Re!ression +nalysis with Time U9 unemployment rate E "odel (
Ligh 1) #alue shows highcorrelation between and
EPs
• Signi$cance #alues for all
#ariables below <=• :o sign change between
upper and lower limits for#ariables
• asses Durbin%Natsontest
• Model loo!s good afterremo#ing certain outliers
C l l ti f U9
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Calulation of U9Unemployment rate
• Rnly problem with the pre#ious modelis 8S unemployment rate should be!nown
• 8S unemployment rate predicted using
a polynomial eOuation
• Forecast for 8S unemployment for
);*< using polynomial 3Ouation is<&<(=&
•
3rrors for thisdecomposition model
is reasonably low
3rrors for thisdecomposition model
is reasonably low
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-oreastin! usin! "odel.(
• Multiple linear 1egression eOuation formodel is
• Nhich can be used to forecast Future yearsusing forecast of 8S unemployment rate
• Forecasted :5 unemployment rate for);*< .&*<=
•
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Disussion of the Results
• Decomposition predicts an a#erageof +&*+= unemployment rate usingshort%term data
• Multiple regression predicts .&*<=unemployment rate using long%term
data
• Important dierence between thetwo forecasts
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Disussion of the Results
• 1easons of the dierences – Decomposition model does not include
old data and the cyclical pattern&
– The multiple regression use a forecast#alue as a #ariable in the model whichcreate repeating errors
–
5yclical pattern hard to predict becauseit is hard to !now when a pea! or a lowwill occur&
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Conlusions• Two models may be used for
forecasting
*& Decomposition olynomial $tting
)& Multiple inear 1egression ? Time K8S unemployment rate@
ros/ow 3rrors and may be useful forpredicting for near future
5ons/Does not capture cyclic factornot good for predicting far future
ros/•
Ligh correlation between :5unemployment rate and#ariables&
• redicted #alues fairly closeenough
5ons/
• Depend on 8S unemploymentprediction
• Does not capture cyclicpatterns
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