IUGJEBS Vol 24, No1, 2016, pp 142-156

ISSN 2410-5198

1*2

02220202

0222020220610

0616

Estimating the Hidden Economy Using Currency Demand Approach

A Case Study of Palestine

Abstract The study aims to estimate the Hidden Economy (HE) by using the currency demand approach in Palestine (2000-2010), through

studying the relationship between a group of variables that affect the percentage of cash money held by the public (CC) to the broad

money supply (M2). The model used Fully Modified Ordinary Least Squares (FMOLS). Results showed that the hidden economy in

Palestine is varying over time, the annual average during 2000-2010 reached about 816.2 Million dollar, which is about 16.6% from

the GDP, this ratio reflects a positive reality, especially when compared with the ratio of the H.E in neighboring and regional

countries.

Keywords: Hidden Economy, Financial System, Money Demand, Money Supply, Palestine.

* : knamroty@iugaza.edu.ps

341

0790

0200

0792

07070799

0799

Schneider & Enste,

2000

0910

Schneider &

Buehn, 2011

0222

00412

02220202

2222

2121

0202

344

0222

0229

.

0200

07920227

Tanzi

0609

0229

07660222

MIMIC

0766

00199412

0222

0202

0222

Schneider & Buehn, 2011)

MIMIC060

07770229

0910

0777022902192417

0012041007100714

02100212

94120222

02292417Schneider &

Buehn, 2011

341

(Gulzar et al, 2010)

07200202Tanzi

2022

Schneider, 2008

00

02200224

(Tanzi

4010

Anno, 2006

07990224

MIMIC

0716

079209160224

07220722

419610

Tanzi,

1980

Informal

SectorIrregular Economy

Black Economy

Marginal EconomyShadow

EconomyUnderground Economy

Subterranean Parallel

EconomySecond Economy

Unofficial Economy

Secret EconomyIllegal

EconomyUnregistered

Economy020097

Gutmann,1977, p:9

Tanzi, 1980, p:441

341

Mirus, Roger, & Smith, 1994

0220

".0200

:0.

mimic

GDP

Filipec, 2011, p20

Filipec,

2011, p21

341

MIMIC

Structural

Equation Model SEM

Multiple Indicators and Multiple Causes

Model (MIMIC)

MIMIC

Latent Variable

MIMIC

020024

ζγnXn….γ4X4 γ3X3 γ2X2γ1X1αη

η

x

γ

ζ

Yiη

Yi = δi + λiη + εi

η

Yi

δiλi

εi

Cagan0792

07070799Gutmann

0799

07290796

Schneider & Enste, 2000

Tanzi, 1980

GutmannCagan

Tanzi

07070722

(Tanzi,

1999:339

341

0

0

2

4 M2

M1

Tanzi

M2

Tanzi

Tanzi

0

Tanzi, 1980, p435

ln(C/M2)t = β0 + β1 ln(TW)t + β2 ln(Ws/Y)t + β3 lnRt

+ β4 ln(Y/N)t + εt (1)

ln

0

(C/M2)t

0

(TW)t

(Ws/Y)t

Rt

(Y/N)t

0222

0792022404

0200

091020770

0222Gulzar, 2010

07200202

2022

Kanao & Hamori, 2010

0790022909

0

0

2

341

0

ln(CC/M2)t = β0 + β1 ln(1+(T/Y))t + β2 ln(W/Y)t + β3

(R)t + β4 ln(SE)t + β4 ln(GNIPC)t + DD + εt (2)

(CC/M2)t

(T/Y)t

(W/Y)t

tR

(SE)t

(GNIPC)t

DDt 0

602292

2

(1+(T/Y))Ln

T

TT YLn ∞

Ln Ln

εt

T/Y W/Y R SE GNIPC DD

+

02220202EViews

44

CC

Nominal GDP

311

M2

020218

GDP

Unit Root Test

0Unit Root

PP

level

09

90

0

Integrated of order (2)

1PP

== -3.27

(0.0224*)

-1.54

(0.5025) Ln(CC/M2)

-8.24

(0.0000*)

-2.21

(0.2056)

-2.17

(0.2175) Ln(1+(T/Y))

-7.81

(0.0000*)

-2.52

(0.1158)

-1.96

(0.2988) Ln(W/Y)

-8.28

(0.0000*)

-2.31

(0.1726)

0.35

(0.9787) Ln(GNIPC)

-7.59

(0.0000*)

-2.91

(0.0516)

-1.69

(0.4283) Ln(SE)

== -3.72

(0.0072*)

-2.26

(0.1882) (R)

Cointegration Test

Johansen Technique

0

Trace

r=009

r≥1

9

r≤1

r>1

r≤2

09

r>2

2

Trace

r

Critical

value

Critical

value

Likelihood

Ratio

1 5

( r = 0) 127.708

(0.0003)

117.708

(0.0003) 146.114

(r ≤ 1) 77.8188

(0.0237)

88.8038

(0.0237) 93.0685

(r ≤ 2) 54.6815

(0.1132)

63.8761

(0.1132) 59.3566

Trace test indicates 1 cointegrating eqn(s) at the

0.01 level

Trace test indicates 0 cointegrating eqn(s) at the

0.05 level

Max-eigenvalue test indicates 1 cointegrating

eqn(s) at the 0.05 level

Max-eigenvalue test indicates 1 cointegrating

eqn(s) at the 0.01 level

313

p-valuesMacKinnon-

Haug-Michelis (1999)

:

Modified Ordinary Least Squares FullyFMOLS

OLS

FMOLS

FMOLS

09

(3)EViews0

3

FMOLS

Dependent Variable: LOG(CC/M2)

Variable Coefficient Std. Error t-

Statistic Prob.

LOG(1+(T/Y)) 34.62439 1.594029 21.72130 0.0000*

LOG(W/Y) 0.478023 0.096629 4.946978 0.0000*

(R) -0.074060 0.008076 -

9.170634 0.0000*

LOG(SE) 0.681931 0.127021 5.368644 0.0000*

LOG(GNIPC) -1.109928 0.130070 -

8.533313 0.0000*

C 1.766200 1.131508 1.560926 0.1271

R2=0.762 Adj R

2= 0.762

2129

0Adj

R2

0.963

96.3

219

Serial Correlation

(Heteroskedasticity)

FMOLS

Multicollinearity

VIF

10

VIF

4VIF

VIF

VIF = 31.25

311

4VIF

Variable Centered VIF

C NA

LOG(1+(T/Y)) 1.628302

LOG(W/Y) 1.121624

(R) 3.993812

LOG(SE) 7.220055

LOG(GNIPC) 14.00983

studemund,

2006,p86

t = -0.4578

Prob = 0.6494

Normality

Residual

Jarque-Bera

Residual

FMOLS

J=1.712P-value = 0.424

2

ln(CC/M2)=0196+34160*ln(1+(T/Y)t+0.49*ln(W/Y)t

–0.07*ln(R)t+ 0.68*ln(SE)t – 1.10*ln(GNIPC)t (3)

CC

T

(5)

502220202million us$

CC

CC1

Tax ≠ 0

CC2

Tax = 0 CC1-CC2

2222 700.7 730.8 107.5 623.2 1.22 4194.7 758.7 18.09

2221 788.6 819.9 168.1 651.9 1.07 3897.2 699.9 17.96

2222 694.9 668.1 165.8 502.2 0.99 3432.6 500.1 14.57

2223 976 987.7 226.1 761.6 0.89 3840.9 674.6 17.57

2224 1149.9 1159.9 248.5 911.3 0.90 4198.4 823.1 19.61

2225 1339.8 1356.8 251.8 1104.9 0.92 4634.4 1020.3 22.02

2226 1408.9 1333.1 263.4 1069.7 0.88 4619.1 943.5 20.43

2227 843 848.1 225.4 622.6 1.02 5182.4 633.3 12.22

2228 972.9 997.2 227.1 770.1 1.02 6247.3 785.5 12.57

2229 1101.5 1086.5 238.3 848.2 1.01 6719.6 850.4 12.66

2212 1329.9 1321.9 196.6 1125.3 1.15 8330.6 1288.5 15.47

311

:

Tanzi

Modified Ordinary Least Fully

SquaresFMOLS

7612

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314

07100714

02100212(Schneider &

Buehn,2011, p30

2

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0

0

2

4

9

6

9

2

7

0202

22222211

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19662222

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0200

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420220

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19722229

0200

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