North Carolina Unemployment rate

7/23/2019 North Carolina Unemployment rate

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North Carolina UnemploymentRate

Presenters:

x



Outline• Introduction

• Data Analysis• Forecasting Models

• Summary



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



What is Unemployment?

• eople able"a#ailable and willingto $nd wor! andacti#ely see!ingwor!" but notemployed

• The unemployed areincluded in the laborforce



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&



"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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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$%&)*')#



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



Data +nalysis

%*&)

%;&,

%;&+

;&;

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*&)

Autocorrelation Function for Monthly 8nemployment 1ate

ag

Autocorrelation

OutsideUC,

Trendpattern



-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



Deomposition "ethod.,inear

;

)

+

6

,

*;

*)f/01 2 . %'%30 4 &&'35R6 2 %'37

8nemployment 1ate?=@

Time

8nemployment rate



Deomposition "ethod.80ponential

; *; ); -; +; <; 6; .; ,;;)

+

6

,

*;

*)f?x@ *)&++ exp? %;&;* x @1G ;&,+


Time

8nemployment rate



Deomposition "ethodPolynomial

;

)

+

6

,

*;

*)

f?x@ % ;xH) ;&;-x *;&<(

1G ;&(,


Time

8nemployment 1ate



Deomposition method-oreastin! 8rrors

8rrors ,inear80ponenti

alPolynomia

l

"+D ;&;6* ;&6;( ;&);-

"98 ;&-.) ;&<6* ;&;.)

"+P8 # ;&;6* ;&;.* ;&;)-

"P8 # %;&;;. %;&;*- %;&;**

,owest8rrors

Polynomial -ittin! ,ine8uation is

y %;&;;*+x) ;&;).(x *;&<(*



-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



"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



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;



alidation of "odel

SpreadAround not

random andseems tofollow a

pattern whichis not good

1) ;&(,

5; ;5* *

Nhich is good

- ti i " d l



-oreastin! usin! "odel.&

Forecasting eOuation

Model redicts unemployment rate will go down



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



Data +nalysis

* ) - + < 6 . , ( *; ** *) *- *+ *< *6 *. *, *( ); )* )) )-

%*&;

%;&,

%;&6

%;&+

%;&)

;&;

;&)

;&+

;&6

;&,

*&;Autocorrelation Function for Annually 8nemployment 1ate

ag

Autocorrelation

Cylialpattern

/une>endistane

peaks andlows1



"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

&



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



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



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



-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);*< .&*<=

•



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



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&



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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North Carolina Unemployment rate