The Long and Short-term Determinants of Inflation in ... Donni F. Anugrah.pdf · Targeting Framework (ITF) policy to achieve their goal. The high and persistent inflation The high

The Long and Short-term Determinants of Inflation in Indonesia’s Regions

Donni Fajar Anugrah1

Abstract

Although inflation rate was down from 6.96% in 2010 to 3.79% in 2011, the

average inflation in Indonesia has been relatively high since 1998. The high and persistent

inflation recently becomes important issues for both central bank and Indonesia

government. Therefore, the controlling inflation is a key policy concern in Indonesia.

However, this is a difficult task due to differences across regions. Since each region has

specific characteristics, determinants of inflation may be different in each region.

This research aims to analyze the factors of inflation in the long-term and short-

term. The result will be important to understand the differences between long-term and

short-term factors of inflation among regions. This study develops the long-term and short-

term models of inflation by applying panel co-integration techniques (Smith and Pesaran,

1995; Im, Pesaran and Shin, 1997; Breitung 2001; Breitung and Pesaran 2005). It also

applies an extended Engle Granger method of error correction mechanism (ECM). This

study extends previous research approaches (Leheyda, 2005) which applied Johansen’s

procedure and based on purchasing power parity theory. The model will be classified in

five regions: Sumatra, Java, Kalimantan, Sulawesi and Papua. Cross section data are

Indonesia’s provinces in each region and time series is over the period 1989-2010. Data

are provided by Statistic Bureau of Indonesia and Bank Indonesia.

The empirical results report that inflation in each region has been determined by

similar factors which are foreign price and exchange rate in the long term. These findings

confirm previous studies found that both foreign price and exchange rate have long-term

effects on domestic prices (e.g. Listiani, 2006; Endri, 2008; Kandil and Morsy, 2009; Khan

and Gill, 2010). The effects of both factors are different in each region which the highest

effect of foreign price to inflation is in Sumatra and the highest effect of exchange rate to

inflation is in Sulawesi. In the short term, the factors of inflation are different in each

region. They are wage, total factor productivity (TFP), money supply, interest rate and the

price of kerosene.

Keywords : inflation rate, regional economics, panel co-integration JEL : E31, R11, C33

1 Donni Fajar Anugrah is currently a PhD student in Economics at The University of Western Australia. His

study has been sponsored by Bank Indonesia as his employer. He has been working as an economist at Bank Indonesia since 1999.

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The Long and Short-term Determinants of Inflation in Indonesia’s Regions

1. Introduction

Since the Act No.23 about Bank Indonesia independence was issued in 1999 and

renewed in 2003 by the Act No.4, the main goal of Bank Indonesia as central bank of

Indonesia is to achieve a low and stable inflation. Bank Indonesia has applied Inflation

Targeting Framework (ITF) policy to achieve their goal. The high and persistent inflation

recently becomes important issues for both central bank and Indonesia government.

Controlling inflation is a key policy concern in Indonesia. However, this is a difficult task

due to differences across regions. Since each region has specific characteristics, the

determinants of inflation may be different in each region.

There are limited studies that examined the determinants of regional inflation in

Indonesia (Wimanda 2006; Anugrah, Dewati & Chawwa 2007). Wimanda (2006) found

that the main factors of inflation in all provinces in Indonesia are expected inflation and

exchange rate. Similarly, another study (Anugrah, Dewati and Chawwa, 2007) found that

factors of inflation in East Java, Central Java, Yogyakarta and Bali are expected inflation,

exchange rate and output gap. However, their studies fail to address the issue of differential

factors contributing for inflation in province in the long and short-term. Moreover, none of

these studies analyzed regional differences across Indonesia applying panel data co-

integration techniques.

These issues raise an important and related question which will be addressed in this

chapter: What are the long and short-term determinants of inflation in Indonesia’s regions?

The main focus of this thesis is to analyze the determinants of inflation in the long and

short-term. The result will be important to understand the differences long and short-term

factor of inflation among regions. Therefore, policy makers can issue their policies by

considering those factors in each region.

This study will use different methods, to address the different objectives and

specific topics. Developing long and short-term models of inflation by applying panel co-

integration techniques (Smith & Pesaran 1995; Im, Pesaran & Shin 1997; Breitung 2001;

Breitung & Pesaran 2005) using an extended Engle Granger method of error correction

mechanism (ECM). This study will build on previous research approaches (Leheyda, 2005)

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which applied Johansen’s procedure with VAR and based on purchasing power parity

theory. This chapter will focus on Indonesia’s regions which have not been studied yet.

This chapter is structured as follows. Section 2 presents the study literature related

with determinants of inflation. Section 3 presents data, methodology and econometric

models. Section 4 reports and discusses empirical results. The last part presents our main

conclusion and policy recommendation.

2. Literature Review

Inflation which generally means an increase in price can be determined by the long

and short term factors. In the long run, price can be influenced by changes in aggregate

demand and/or aggregate supply. Romer (2001) mentioned that technology shock and labor

supply can be as sources of inflation in aggregate supply side. Meanwhile money stock and

money demand can influence aggregate demand. He also stated that money supply growth

can be the main factor of inflation in the long term. The formula of demand for real money

is below:

( , )M

L i YP

Noted: M is money stock, P is price level, i is nominal interest rate, and Y is real

income.

Since nominal interest rate can influence price level and policy rate directly

influence interest rate, policy rate can be the factor of inflation rate. When policy rate goes

up or monetary policy tightening, domestic prices can be going down. An increase in policy

rate is followed by reducing consumption or shifting aggregate demand to the left.

Therefore, inflation goes down as a response of monetary tightening.

The monetary explanation for inflation has dominated the literature since the late

1970s. This theory suggests that money growth determines inflation, and that there is a

strong link between money growth and inflation (Friedman & Schwartz 1982). A number

of studies have confirmed Friedman’s analysis to argue that money growth is positively

linked to inflation in some countries, see for example studies from Australia (Fahrer &

Myatt 1991), Nigeria (Moser 1995), Albania (Domac & Elbrit 1998), Indonesia (Listiani

2006), Paraguay (Monfort & Pena 2008) and Tunisia (Boujelbene & Boujelbene 2010).

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Another important factor of inflation is exchange rate which can influence prices in

the short and long term. Some studies (e.g. Nicolae, 2002; Listiani, 2006; Endri, 2008;

Gachet, Maldonado and Perez, 2008; Kandil and Morsy, 2009; Khan and Gill, 2010) found

that depreciation of exchange rate can increase inflation. Indeed, Indonesian study (Siregar

& Rajaguru 2002) found that the main factors of inflation in Indonesia are base money and

exchange rate during the post Asian financial crisis period (1997). The role of exchange

rate can be explained by the theory of purchasing power parity. This theory suggests that

domestic prices are influenced by both foreign prices and the exchange rate (Hallwood &

MacDonald 2000). The formula of purchasing power parity is below:

*P SP

Noted: P is the domestic price, S is exchange rate, and *P is foreign price.

As foreign prices are positively correlated with domestic prices, an increase in

foreign prices can lead to an increase in domestic prices. Leheyda’s (2005) study from

Ukraine confirms this since the foreign prices were found to have long-term effects on

domestic prices. Meanwhile, depreciation of exchange rate can increase domestic prices.

The foreign exchange rate is regarded as a particularly important factor influencing

inflation in the long and short-term. Recent evidence suggests that exchange rate

depreciation is found to generate inflation, due to an increase in imported product prices

(e.g. Domac and Elbirt, 1998; Dlamini, Dlamini and Nxumado, 2001; Nicolae, 2002;

Listiani, 2006; Endri, 2008; Gachet, Maldonado and Perez, 2008; Kandil and Morsy, 2009;

Khan and Gill, 2010).

In the short term, the sources of inflation can be more factors than in the long term.

The popular theory of the long term factors of inflation is Philips Curve theory. This theory

argued that the main factors influencing inflation are expected inflation, output gap and

supply shock. The assumption that individual’s expectations of future inflation are based on

recent inflation is known as adaptive expectations. Monfort and Pena (2008) showed that

inflation expectation was an important factor explaining the inflation trends in Paraguay.

An increase in output gap is positively related to inflation across European countries

(Boschi & Girardi 2007; Andersson, Masuch & Schiffbauer 2009). Other explanations

include supply shocks due to exogenous events, such as an increase in oil price (Mankiw,

2007). Another important factor of inflation is wage. An increase in wage can raise

domestic prices, because aggregate demand shifts to the right. On contrary, a decrease in

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wage can decrease domestic prices. Inflation study in Swaziland (Dlamini, Dlamini &

Nxumado 2001) found that wage is source of inflation in the long and short term.

3. Data and Methodology

3.1 Data

The data which are used by this study are from Statistic Bureau of Indonesia, Bank

Indonesia, and CEIC data. The data cover the 26 provinces of total 33 provinces in

Indonesia. The other seven provinces which have limited data are Bangka Belitung, Riau

Islands, Banten, Gorontalo, West Sulawesi, North Maluku and West Papua. Provinces in

Indonesia can be classified by five regions based on location. They are Sumatra, Java,

Kalimantan, Sulawesi and Papua. The data frequencies are annual data over the 1989-2010

period and monthly data over the 1993-2010 period.

Those data are consumer price index (CPI), exchange rate (Indonesia Rupiah agains

US dollar), the consumer price index (CPI) of US, money supply (broad money), wage,

TFP (Total Factor Productivity), oil price (kerosene price), expected inflation and policy

rate (Bank Indonesia rate). Data of money supply in each province are calculated by

proportion of province’s GDP to national GDP. This method is used by previous research

(Laksono 2005). Meanwhile, for expected inflation variable, we use the lag of inflation as

adaptive inflation (Mankiw 2007). Author obtains data of TFP by developing Solow

growth model and calculating TFP by growth accounting method (Tjahjono & Anugrah

2006).

Table 1. The List of Data

No Data Code Definition

1 CPI Consumer Price Index*

2 CPI US US Consumer Price Index***

3 ER Exchange Rate**

4 M2 Money Supply (broad money)

5 W Wage*

6 TFP Total Factor Productivity

7 Krs Price of Oil (Kerosene)***

8 CPI (lag) Expected Inflation

9 IP Policy Rate (Bank Indonesia rate)**

Sources: *Statistic Bureau of Indonesia, **Bank Indonesia and ***CEIC

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3.2 Methodology

This paper applied econometric approach which mainly applies error correction

mechanism (ECM). In the region cases, we applied co-integration techniques using panel

data (Breitung and Pesaran, 2005) to find the long-term factors of inflation. We develop 5

panel data sets with provinces in each region as cross-section data and over the period 1989

to 2010 as time series. Meanwhile, in the province cases, this study applied co-integration

techniques using time series (Granger & Engle 1987). We also develop 26 models of each

province in the long and short term with monthly data from 1993 to 2010.

The long run models must be tested whether they have co-integration in the long

term or not. If they pass the stationary test, it means the long term relationship exists.

Therefore, the residual of the long run model can be used as ECM. The function of ECM

on the short-term model is to keep the short run model in the equilibrium of the long run

model (Granger & Engle 1987; Katarina & Johansen 1994).

The economic models of inflation are classified by panel data for regional models

and time series data for provincial models. The regional models are in the long and short-

term. The long-term equation based on purchasing power parity theory (equation 1) and

modified by adding money supply (equation 3).

1 2 3log( ) log( ) log( *)ij ij ij ijP er P e (1)

where j is time index, ijP is the domestic price in province i , er is the exchange rate in

province i and *P is the foreign price in province i . Furthermore, we find ECM variable

by reformulating equation 1 to be equation 2:

1 2 3log( ) log( ) log( *)ij ij ij ijecm P er P (2)

The alternative model of the long-term equation is put money supply as long and

short-term factor based on quantity theory of money:

1 2 3 4log( ) log( ) log( *) log( *)ij ij ij ij ijP er P M e (3)

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where j is time index, ijP is the domestic price in province i , er is the exchange rate in

province i , *P is the foreign price in province i and M is the broad money in province

i . Furthermore, we find ECM variable by reformulating equation 3 to be equation 4:

1 2 3 4log( ) log( ) log( *) log( )ij ij ij ij ijecm P er P M (4)

For long-term model procedure, we do panel unit root test for ECM variable. If it

has no unit root, we put it in the short-term equation. The short-term equation:

, 1 , , ,

0 0 0

log( ) log( *) log( ) log( )k k k

ij i j t i j t t i j t t i j t

t t t

P ecm P er TFP

, ,

0 0

... log( ) log( )k k

t i j t t i j t ij

t t

Krs W

0,1,2....t k (5)

where j is time index, t is time lag, ,log( )i jP is price changes (inflation) in province i ,

,log( )i jer is exchange rate changes in province i , ,log( *)i jP is foreign price changes in

province i , ,log( )i jTFP is Total Factor Productivity in province i , ,log( )i jW is wage

changes in province i , ,log( )i jKrs is kerosene price changes in province i

Similarly, inflation models for provincial monthly data are in the long and short-

term. The long-term equation based on purchasing power parity theory (equation 6).

1 2 3log( ) log( ) log( *)t t t tP er P e (6)

where t is time index, tP is the domestic price, er is the exchange rate and *P is the

foreign price. Furthermore, we find ECM variable by reformulating equation 6 to be

equation 7:

1 2 3log( ) log( ) log( *)t t t tecm P er P (7)

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Furthermore, we do unit root test for ECM variable. If it has no unit root, it means

that the long-term model exists. Therefore, we put ECM variable in the short-term

equation. The formula of the short-term equation:

1

1 0 0

log( ) log( ) log( ) log( *)k k k

t t t t i t t i t t i

i i i

P ecm P er P

0 0 0 0

... log( ) log( ) ( ) log( )k k k k

t t i t t i t P t i t t i t

i i i i

M W i Krs

0,1,2....i k (8)

where t is time index, i is time lag, log( )tP is price changes (inflation), log( )t iP is

expected inflation changes, log( )ter is exchange rate changes, log( *)tP is foreign price

changes, log( )tM is money supply changes, log( )tW is wage changes, ( )P ti is policy

rate (Bank Indonesia rate) changes and log( )tKrs is kerosene price changes.

4. Empirical Results

In this part, we begin with the unit root test of the data in both regions and

provinces to find the order of integration in each data. We applied panel unit root test in the

regional cases and time series unit root test in the provincial cases. Furthermore, we

develop the long term model based on the variables which have the similar order of

integration. We get the ECM from the long term model and applied it in the short term

model.

4.1 The Order of Integration

Empirical results of panel unit root test indicate that CPI province in all regions is

unit root at level (Table 2). Different result is found by Breitung’s method that CPI

province is stationary at level in Kalimantan and Papua. However, other methods found

that the data are unit root at level in both regions. Table 2 also reports that CPI province is

not unit root at first difference in all regions. This results mean that order of integration of

CPI province in all provinces is one due to the data is stationary at first difference.

Page 8

Table 2. Panel Unit Root Test of CPI Province

Method Sumatra Java Kalimantan Sulawesi Papua

Level 1st Diff Level 1st Diff Level 1st Diff Level 1st Diff Level 1st Diff

Breitung

t-stat

-1.55 -8.59

***

-0.78 -6.49

***

-1.80

**

-5.66

***

-0.54 -4.24

***

-1.97

**

-5.89

***

Im, Pesaran

and Shin W-

stat

10.85 -8.38

***

10.85 -7.32

***

8.05 -5.63

***

7.36 -5.75

***

9.47 -5.62

***

ADF - Fisher

Chi-square

0.01 89.94

***

0.01 62.61

***

0.002 42.62

***

0.01 43.58

***

0.004 47.52

***

Observation 176 110 88 88 110

Notes: Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *

The data of CPI US as representative of foreign price are not stationary at level by

three methods which are Im, Pesaran and Shin, ADF and PP methods. Meanwhile, other

two methods found different results (Table 3). When we put the data at first difference, we

found that all methods found the similar results (Table 3). Foreign price (CPI US) is

stationary at first difference in all regions. It means that order of integration of CPI US is

one.

Table 3. Panel Unit Root Test of CPI US



Breitung

t-stat

-2.31

**

-15.91

***

-

1.83**

-12.58

***

-1.64

*

-11.25

***

-

1.64*

-11.25

***

-1.83

**

-12.58

***

Im, Pesaran

and Shin

W-stat

1.42 -18.95

***

1.12 -14.98

***

1.00 -13.40

***

1.00 -13.40

***

1.12 -14.98

***

ADF -

Fisher Chi-

square

5.27 211.95

***

3.30 132.47

***

2.64 105.97

***

2.64 105.97

***

3.30 132.47

***

Observation 176 110 88 88 110


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Similarly, the results of panel unit root test of exchange rate at level indicate that

exchange rate is unit root in all regions. Levin, Lin and Chu’s method found exchange rate

in Sumatra is significant at 10%. It means that exchange rate in Sumatra is not unit root at

level. However, by other methods we found that all regions are not significant meaning

data are not stationary at level (Table 4). At first difference, the data of exchange rate are

stationary in all regions. Table 4 reports that all regions are significant at 1%. Therefore,

the order of integration of exchange rate is one in all regions.

Table 4. Panel Unit Root Test of Exchange Rate



Breitung

t-stat

0.91 -13.81

***

0.72 -10.92

***

0.64 -9.77

***

0.64 -9.77

***

0.72 -10.92

***

Im, Pesaran

and Shin

W-stat

0.83 -13.12

***

0.65 -10.37

***

0.58 -9.28

***

0.58 -9.28

***

0.65 -10.37

***

ADF -

Fisher Chi-

square

7.36 147.37

***

4.60 92.10

***

3.68 73.68

***

3.68 73.68

***

4.60 92.10

***

Observation 176 110 88 88 110


Table 5 reports that variables of money supply at level in all regions are not

significant. These results mean that money supply is unit root at level. Therefore, we

continue to test the variable of money supply at first difference. At first difference, the data

of money supply are stationary in all regions with Breitung t-stat method (Table 5). It

means that the order of integration of money supply is one in all regions.

Table 5. Panel Unit Root Test of Money Supply



Breitung

t-stat

14.61 -5.29

***

14.43 -4.11

***

11.11 -3.64

***

14.02 -3.54

***

9.49 -4.10

***

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Im, Pesaran

and Shin

W-stat

10.01 2.37 11.07 3.92 8.08 2.04 11.28 4.39 5.88 -1.66

**

ADF -

Fisher Chi-

square

2.51 15.31 0.0002 0.64 0.01 2.58 0.0001 0.23 1.91 35.99

***

Observation 176 110 88 88 110


Similarly, we do unit root test for monthly data which is from 1993 to 2010 for 26

provinces. The data are domestic price (CPI), foreign price (CPI US) and exchange rate.

The results report that those variables are not stationary at the level. At the first difference,

they significantly reject unit root. It means the data are stationary. Therefore, we can

conclude that the data have order of integration at one.

4.2 Long-term Determinant of Inflation

Since the empirical results found that domestic price, foreign price, exchange rate

and money supply are stationary at the first difference. It means the data have the similar

level of order of integration which is one. Therefore, we can use the data in the long run

model. The results of panel OLS for domestic price (CPI province) can be seen on Table 6.

In the long run, the foreign price which is represented by CPI US has positively affected

domestic price which is represented by CPI province. The effect of CPI US in all regions is

different. The highest effect of foreign price is in Sumatra and the lowest effect of that is in

Java. The elasticity of foreign price in Sumatra and Java are 4.28 and 1.81 respectively. It

means an increase price in foreign price by 1% can increase domestic price by 4.28% in

Sumatra and by 1.81% in Java.

Meanwhile, exchange rate represented by US Dollar against Indonesia Rupiah is

positively associated with domestic price. Empirical results found that exchange rate is

significant and positive with elasticity by 0.49 in Sulawesi and 0.25 in Kalimantan. It

means depreciation of exchange rate by 1% can increase domestic price by 0.49% in

Sulawesi and by 0.25% in Kalimantan. This finding was supported by the fact that demand

of import in Indonesia is high. The high value of import explains that foreign price and

exchange rate are the main factor of domestic inflation. The domestic price can be

Page 11

determined by foreign price because the import value is high. Therefore, the domestic price

is sensitive to the foreign price. Meanwhile, depreciating exchange rate will be followed by

increasing domestic price.

Table 6. The Long-term Model (Panel Fixed Effect-Period)

Variable Sumatra Java Kalimantan Sulawesi Papua

CPI US 4.28

(0.16)***

1.81

(0.54)***

2.06

(0.56)***

4.08

(0.23)***

3.96

(0.19)***

ER 0.45

(0.02)***

0.27

(0.05)***

0.25

(0.06)***

0.49

(0.03)***

0.43

(0.03)***

M2 0.32

(0.08)***

0.29

(0.08)***

C -20.92

(0.64)***

-10.47

(2.17)***

-10.76

(2.48)***

-20.30

(0.90)***

-19.15

(0.64)***

R2 0.98 0.99 0.99 0.98 0.98

Observation 176 110 88 88 110

Provinces 8 5 4 4 5

Notes: standard errors are shown below the parameter estimates

Significant 1% denoted by ***, 5% denoted by **, 10% denoted by *

Money supply represented by broad money (M2) is positively associated with

domestic price. Empirical results found that money supply is only significant and positive

in Java and Kalimantan. The elasticity of money supply in Java and Kalimantan is 0.32 and

0.29 respectively. It means an increase of money supply by 1% can increase domestic price

by 0.32% in Java and by 0.29% in Kalimantan. The role of money supply in both regions is

important which is comparing to other inflation factors.

To prove that the long run model is robust, we do panel unit root test for the

residual of the long run model (Table 7). The empirical results found that the residual in all

regions are significant at 1%. Therefore, we can conclude that the long run model is valid.

Furthermore, we can use the error terms of those long-term models as error correction

mechanism (ECM) in the short-term models.

Table 7. Panel Unit Root Test of Residual at Level


Levin, Lin & Chu t -5.85*** -5.48*** -5.28*** -4.98*** -6.73***

Breitung t-stat -3.12*** -4.22*** -4.46*** -2.93*** -5.12***

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ADF - Fisher Chi-square 56.37*** 42.85*** 37.41*** 39.25*** 60.89***


In the provincial case we found that the order of integration of domestic price,

foreign price and exchange rate is one. Therefore, we those variables can be used in the

long run regression. The empirical results of OLS for domestic price in each province can

be seen in appendix. The results shows that the foreign price has positively affected

domestic price in the long term. However, the impact of CPI US on domestic price in each

province is various. The highest effect of foreign price is in Banda Aceh (6.06).

Meanwhile, the lowest effect of that is in Gorontalo (3.34). It means an increase price in

foreign price by 1% can increase domestic price by 6.06% in Banda Aceh and by 3.34% in

Gorontalo.

Similarly, the exchange rate is significantly affecting domestic price. The effect is

positive meaning that depreciation of Indonesian Rupiah against US Dollar can increase the

domestic price. The highest effect is in North Maluku with elasticity by 0.50 and the lowest

is in Papua with elasticity by 0.29. This finding confirms that exchange rate is the

important factor of inflation (Kandil & Morsy 2009; Khan & Gill 2010). The test results for

the error of the long term model in each province has significantly rejected that it has unit

root. Therefore, the long term model exists in all provinces. Thus, the error of those long

term models can be applied as ECM in the short term models.

4.3 Short-term Determinant of Inflation

In the short-term inflation model, we put ECM variable that we got from the long-

term inflation model. The ECM variable in each equation is expected to be negative, since

the role of ECM is to restore the equilibrium (Gujarati 2003). The empirical results show

that ECM variable is negative as expected in all equations (Table 9). The coefficient of

ECM in Sumatra, Java, Kalimantan, Sulawesi and Papua is -0.10, -0.11, -0.15, -0.13 and -

0.10 respectively.

In Sumatra, USCPI as representative of the inflation in US has positively affected

inflation in Sumatra and Kalimantan with one lag. The elasticity of US inflation is 0.36 in

Sumatra and 0.22 in Kalimantan. Those mean that an increase in US inflation one year ago

by 1% can increase inflation in Sumatra and Kalimantan now by 0.36% and by 0.22%

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respectively. However, the effect of US inflation is significant in Sumatra, but it is not

significant in Kalimantan. From 2000 to 2010, the value of import in Sumatra is higher

than that of in Kalimantan (Figure 1).

Figure 1. Import in Sumatra and Kalimantan

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

mill

ion

USD

Sumatera

Kalimantan

Source: Bank Indonesia

Meanwhile, the changes of exchange rate can positively affect inflation in all

regions. The effect of exchange rate changes is highly significant at 1%. The elasticity of

exchange rate changes in Sumatra, Java, Kalimantan, Sulawesi and Papua is 0.40, 0.34,

0.32, 0.37 and 0.34 respectively. An increase the changes of exchange rate depreciation by

1% can increase inflation by 0.40% (Sumatra), by 0.34% (Java), by 0.32% (Kalimantan),

by 0.37% (Sulawesi) and by 0.34% (Papua). These findings support previous studies found

that exchange rate significantly affects inflation (Endri, 2008; Gachet, Maldonado and

Perez, 2008; Kandil and Morsy, 2009; Khan and Gill, 2010).

Table 8. The Short-term Model (Panel Fixed Effect-Period)

Variable Sumatra Java Kalimantan Sulawesi Papua

ECM (-1) -0.13

(0.04)***

-0.13

(0.06)**

-0.14

(0.06)**

-0.16

(0.05)***

-0.21

(0.05)**

DCPI US (-1) 0.42

(0.18)**

0.28

(0.18)

0.30

(0.18)

0.21

(0.23)

0.19

(0.24)

DER 0.36

(0.02)***

0.34

(0.03)***

0.31

(0.02)***

0.37

(0.03)***

0.29

(0.02)***

DW

0.05

(0.04)

Page 14

DW (-1) 0.09

(0.04)**

0.03

(0.04)

0.06

(0.05)

0.10

(0.05)**

DTFP -0.34

(0.15)**

-0.31

(0.16)*

-0.46

(0.15)***

-0.37

(0.19)*

-0.52

(0.17)***

DKrs 0.08

(0.02)***

0.05

(0.02)**

0.09

(0.03)***

0.06

(0.02)**

DKrs (-1)

0.04

(0.02)*

C 0.06

(0.01)***

0.05

(0.01)***

0.06

(0.01)***

0.05

(0.01)***

0.05

(0.01)***

R2

0.88 0.89 0.92 0.91 0.88

Observation 160 100 80 80 100

Provinces 8 5 4 4 5



Wage changes positively affected inflation in all regions with different lag. In Java

and Papua, the wage changes affected inflation without lag and insignificant. Meanwhile, it

significantly affected inflation in Sumatra and Kalimantan with one lag. The elasticity of

wage changes in both regions is 0.08 and 0.25 respectively. It means than an increase of

wage changes in previous year by 1% can increase inflation in current year by 0.08% in

Sumatra and by 0.25% in Kalimantan. These findings confirm previous research (Leheyda

2005) found that wages has significantly affected inflation.

Money supply growth positively affects inflation in Papua with one lag, but it is not

significant. Theoretically, money growth is positively associated with inflation (Monfort &

Pena 2008; Boujelbene & Boujelbene 2010). Increasing money growth can cause to

improve aggregate demand which positively affects inflation. The elasticity of money

growth is 0.01 meaning that an increase money growth by 1% in previous year can increase

inflation by 0.01% in Papua in current year.

Meanwhile, interest rate represented by time deposit rate and policy rate represented

by Bank Indonesia rate are positively related with inflation in Java, Kalimantan and

Sulawesi in the short-term. However, these relationships are not significant. Theoretically,

interest rate and policy rate have to negatively affect inflation. An increase policy rate can

increase time deposit rate and lending rate. Thus, increasing both rates can reduce

consumption and investment. Finally, aggregate demand will reduce in which reduces price

index. Therefore, both interest rate and policy rate are negatively associated with inflation.

Page 15

Furthermore, we will discuss the short-term model of inflation in each province in

each region separately. Table 9 reports that ECM in each province in Sumatra has a

negative coefficient as expected. The elasticity of ECM is relatively similar in each

province in Sumatra with the range between -0.03 and -0.05. They are significant at 1% in

all provinces in Sumatra. Meanwhile, expected inflation denoted by inflation lag 1 month

has positive effect on inflation in each province. The lowest impact is in Jambi (0.24%) and

the largest impact is in Riau and South Sumatra (0.38).

The changes of exchange rate can positively affect inflation in all provinces. They

are significant at 1% with the elasticity of exchange rate changes between 0.04 (North

Sumatra) and 0.10 (Bengkulu). An increase the changes of exchange rate depreciation by

1% can increase inflation by 0.10% in Bengkulu. These findings confirms previous studies

found that exchange rate are sources of inflation (e.g. Kandil and Morsy, 2009; Khan and

Gill, 2010). Another short-term factor, money supply changes is significant only in Bandar

Lampung with lag a month. As expected, kerosene price changes are positive and

significant (1%) in all provinces. The lowest impact is in Jambi that its elasticity is 0.06.

Meanwhile, the others are around 0.10.

Table 9. The Short-term Model (Sumatra)

Variable Aceh North Sumatra

West Sumatra

Riau Jambi South Sumatra

Bengkulu Bandar Lampung

ECM (-1)

-0.03

(0.01)***

-0.04

(0.01)***

-0.04

(0.01)***

-0.03

(0.01)***

-0.04

(0.01)***

-0.03

(0.01)***

-0.04

(0.01)***

-0.05

(0.01)***

DCPI

(-1)

0.28

(0.06)***

0.33

(0.05)***

0.27

(0.05)***

0.38

(0.05)***

0.24

(0.06)***

0.38

(0.05)***

0.32

(0.05)***

0.34

(0.05)***

DER (-1)

0.06

(0.02)***

0.04

(0.01)***

0.09

(0.01)***

0.08

(0.01)***

0.07

(0.01)***

0.08

(0.01)***

0.10

(0.01)***

0.05

(0.01)***

DM2 0.01

(0.01)

0.01

(0.01)

0.004

(0.01)

0.004

(0.01)

0.002

(0.01)

0.003

(0.01)

DM2

(-1)

0.01

(0.01)

0.01

(0.01)**

DKrs 0.10

(0.02)***

0.10

(0.01)***

0.09

(0.01)***

0.06

(0.01)***

0.06

(0.01)***

0.08

(0.01)***

0.10

(0.01)***

0.09

(0.01)***

C 0.01

(0.001)***

0.004

(0.001)***

0.01

(0.001)***

0.004

(0.001)***

0.01

(0.001)***

0.004

(0.001)***

0.004

(0.001)***

0.004

(0.001)***

R2 0.36 0.51 0.51 0.53 0.41 0.57 0.55 0.51

Obs 214 214 214 214 214 214 214 214

Notes: Standard errors are shown below the parameter estimates


Page 16

Meanwhile, table 10 shows that ECM with one month lag in each province in Java

has a negative coefficient between -0.03 and -0.04. The expected inflation variable is also

significant at 1% in all provinces. The lowest impact of inflation expectation is in Central

Java (0.34) and the highest impact is in West Java (0.49). That means an increase in

expected inflation by 1% can increase inflation by 0.34% and 0.49% in Central Java and

West java respectively. The US inflation significantly affects domestic inflation in

Yogyakarta only. Its elasticity is 0.21 meaning an increase in US inflation by 1% can raise

inflation in Yogyakarta by 0.21%.

Similar with Sumatra, the changes of exchange rate with one month lag can

positively affect inflation in all provinces in Java. They are significant at 1%. The highest

elasticity of exchange rate changes is in East Java (0.10) and the lowest of that is in West

Java (0.04). An increase the changes of exchange rate depreciation by 1% can increase

inflation by 0.10% and 0.04% in East Java and West Java respectively.

Table 10. The Short-term Model (Java)

Variable Jakarta West Java Central

Java

Yogyakarta East Java

ECM

(-1)

-0.04

(0.01)***

-0.04

(0.01)***

-0.03

(0.01)***

-0.03

(0.01)***

-0.04

(0.01)***

DCPI

(-1)

0.47

(0.05)***

0.49

(0.05)***

0.34

(0.05)***

0.46

(0.05)***

0.41

(0.05)***

DCPIUS 0.21

(0.09)**

DER

(-1)

0.05

(0.01)***

0.04

(0.01)***

0.09

(0.01)***

0.09

(0.01)***

0.10

(0.01)***

DM2

0.001

(0.004)

DM2

(-1)

0.01

(0.004)*

0.01

(0.004)*

0.01

(0.004)*

DIP

-0.001

(0.0004)*

DKrs 0.04

(0.01)***

0.06

(0.01)***

0.08

(0.01)***

0.02

(0.01)***

0.05

(0.01)***

C 0.003

(0.001)***

0.003

(0.001)***

0.003

(0.001)***

0.003

(0.001)***

0.003

(0.001)***

R2

0.58 0.56 0.57 0.65 0.54

Obs 214 214 214 214 215



Page 17

Money supply changes with a month lag is significant in Jakarta, West Java and

Central Java. They have similar elasticity (0.01). Policy rate in this model is significant

only in East Java with low elasticity (-0.001). This situation can be explained that policy

rate affects inflation indirectly and the lag range 3-6 months. The last factor in this model is

kerosene price changes. It is positive and significant (1%) in all provinces with the lowest

effect in Yogyakarta (0.02). It means an increase in kerosene price changes by 1% can

increase inflation by 0.02%.

Table 11. The Short-term Model (Kalimantan)

Variable West

Kalimantan

Central

Kalimantan

South

Kalimantan

East Kalimantan

ECM

(-1)

-0.04

(0.01)***

-0.75

(0.07)***

-0.03

(0.01)***

-0.03

(0.01)***

DCPI

(-1)

0.30

(0.05)***

0.30

(0.05)***

0.29

(0.05)***

DCPI

(-3)

0.003

(0.05)

DCPIUS 0.19

(0.11)*

0.18

(0.11)*

0.18

(0.09)*

DER

(-1)

0.09

(0.01)***

0.12

(0.01)***

0.09

(0.01)***

DM2 0.003

(0.01)

0.001

(0.01)

DM2

(-1)

0.01

(0.004)*

DKrs 0.05

(0.01)***

0.004

(0.16)

0.06

(0.01)***

0.06

(0.01)***

C 0.01

(0.001)***

0.01

(0.01)

0.004

(0.001)***

0.004

(0.001)***

R2

0.54 0.39 0.58 0.59

Obs 215 212 215 214

Notes: Standard errors are shown below the parameter estimates


The short-term inflation model of provinces in Kalimantan can be seen on table 11.

The ECM with lag by 1 month in each province is significant at 1%. The elasticity of ECM

in each province is relatively similar around -0.03, except in Central Kalimantan (-0.75).

Meanwhile, expected inflation has positive effect on inflation in all provinces. They are

significant at 1%, except in Central Kalimantan. The elasticity of inflation expectation is

0.30 (West Kalimantan and South Kalimantan) and 0.29 (East Kalimantan). Foreign

Page 18

inflation significantly affects domestic inflation in West Kalimantan (0.19), South

Kalimantan (0.18), and East Kalimantan (0.18). Therefore, an increase in US inflation by

1% can raise inflation by 0.18% in South Kalimantan and East Kalimantan.

Meanwhile, the changes of exchange rate with one month lag have positive impact

on inflation in three provinces in Kalimantan, such as West Kalimantan, South Kalimantan,

and East Kalimantan. The highest elasticity of exchange rate changes is in South

Kalimantan (0.12) and followed by West Kalimantan and East Kalimantan (0.09).

Meanwhile, money supply changes with a month lag is significant only in East Kalimantan.

Its elasticity is 0.01 which means an increase in money supply changes by 1% can increase

inflation by 0.01% in East Kalimantan. Kerosene price changes have positive impact on

inflation in all provinces in Kalimantan and significant at 1%, except in Central Kalimantan

which is insignificant. The elasticity of kerosene price changes is 0.05 in West Kalimantan

and 0.06 in South Kalimantan and East Kalimantan.

Table 12. The Short-term Model (Sulawesi)

Variable North Sulawesi Central Sulawesi South Sulawesi Southeast Sulawesi

ECM

(-1)

-0.07

(0.01)***

-0.05

(0.01)***

-0.05

(0.01)***

-0.05

(0.01)***

DCPI

(-1)

0.21

(0.06)***

0.25

(0.06)***

0.24

(0.06)***

DCPI

(-3)

0.12

(0.06)***

DCPIUS

0.31

(0.14)**

DER

(-1)

0.03

(0.01)*

0.06

(0.02)***

0.07

(0.01)***

0.07

(0.02)***

DM2

0.004

(0.01)

0.001

(0.01)

0.01

(0.01)

DM2

(-3)

0.01

(0.01)**

DKrs 0.05

(0.01)***

0.03

(0.02)**

0.03

(0.01)**

0.08

(0.02)***

C 0.01

(0.001)***

0.01

(0.001)***

0.01

(0.001)***

0.01

(0.001)***

R2

0.41 0.36 0.43 0.47

Obs 212 214 214 214



Page 19

In Table 12, there are four short-run inflation province models. Similar with

previous regions, ECM with one month lag in each province in Sulawesi is significant at

1%. The coefficient of ECM is -0.05 in all provinces, except in North Sulawesi (-0.07). The

inflation expectation in all provinces is significant at 1% with positive coefficient. North

Sulawesi has the lowest coefficient which is 0.12 meaning an increase in expected inflation

by 1% can lead inflation by 0.12%. Meanwhile, the other province’s coefficients are 0.21

(Central Sulawesi), 0.24 (Southeast Sulawesi) and 0.25 (South Sulawesi). Foreign inflation

influences inflation in Southeast Sulawesi with elasticity 0.31.

A month lag of exchange rate changes positively affects inflation in all provinces in

Sulawesi. The lowest elasticity of exchange rate changes is in North Sulawesi (0.03).

Meanwhile, the other three provinces (Central Sulawesi, South Sulawesi, and Southeast

Sulaesi) have an elasticity range 0.6-0.7. Money supply changes have no significant

affecting inflation in all provinces in Sulawesi, except North Sulawesi. An increase in three

month lag of money supply changes by 1% can lead inflation in North Sulawesi by 0.01%.

Another short-term factor, kerosene price changes, has positive effect on inflation in all

provinces in Sulawesi. The highest effect of that is in Southeast Sulawesi (0.08).

Table 13 reports the short-term inflation factor in all provinces in Papua. The ECM

with one month lag in each province is significant with coefficient between -0.03 and -0.07.

Meanwhile, the expected inflation positively influences inflation in Maluku (0.12), East

Nusa Tenggara (0.27), Bali (0.35), and West Nusa Tenggara (0.40), and. An increase in

inflation expectation by 1% can raise inflation by 0.12%, 0.27%, 0.35% and 0.40% in

Maluku, East Nusa Tenggara, Bali, and West Nusa Tenggara respectively.

Table 13. The Short-term Model Papua

Variable Bali West Nusa

Tenggara

East Nusa

Tenggara

Maluku Papua

ECM

(-1)

-0.03

(0.01)***

-0.05

(0.01)***

-0.03

(0.01)***

-0.07

(0.01)***

-0.05

(0.01)***

DCPI

(-1)

0.35

(0.05)***

0.40

(0.05)***

0.27

(0.06)***

0.12

(0.06)*

DER

0.03

(0.01)**

DER

(-1)

0.06

(0.01)***

0.08

(0.02)***

0.03

(0.02)

DER

(-2)

0.05

(0.01)***

Page 20

DM2 0.002

(0.01)

0.001

(0.01)

0.003

(0.01)

0.01

(0.01)

DM2

(-1)

0.01

(0.01)

DKrs 0.05

(0.01)***

0.07

(0.01)***

0.07

(0.01)***

0.04

(0.02)**

0.03

(0.02)*

C 0.004

(0.001)***

0.004

(0.001)***

0.01

(0.001)***

0.01

(0.001)***

0.01

(0.001)***

R2

0.50 0.49 0.34 0.31 0.13

Obs 214 214 215 215 215



The exchange rate changes significantly influences inflation in all provinces in

Papua region, except in the Province of Papua. The highest elasticity of exchange rate

changes is in Maluku (0.08) and followed by Bali (0.06%), East Nusa Tenggara (0.05), and

West Nusa Tenggara (0.03). On the other hand, money supply changes are insignificant in

all provinces in Papua, even their impacts are positive to inflation. Meanwhile, kerosene

price changes have significantly affected on inflation in all provinces. The lowest effect is

in the Province of Papua (0.03). It means an increase in kerosene price changes by 1% can

increase inflation by 0.03% in this province.

5. Conclusion and Policy Recommendation

In the long run, inflation in each region and province has been determined by

similar factors which are foreign price and exchange rate. The foreign price represented by

CPI of United States positively affects domestic price. It means an increase in foreign price

can raise domestic price. The fact that the value of import in Indonesia has shown a

positive trend, therefore the domestic price is sensitive to foreign price. This condition

verifies the finding that foreign price positively associated with domestic price. In the

short-term, foreign price also affects domestic price in Sumatra and Kalimantan.

Meanwhile, exchange rate represented by US Dollar against Indonesia Rupiah also

positively affects domestic price in the long and short-term. Depreciating Indonesia Rupiah

against US will be followed by increasing domestic price. Since many products in

Indonesia are from foreign countries, the domestic price is more sensitive with exchange

rate. Empirical findings also prove that exchange rate is significantly affecting inflation in

Page 21

the short-term in all regions. This finding strengthens previous studies (Siregar & Rajaguru

2002; Wimanda 2006) found that main determinant of inflation in Indonesia is exchange

rate.

Money supply represented by broad money has also positively affected domestic

price in the long-term in Java and Kalimantan. This finding confirms previous study that

found a strong link between money growth and inflation (Listiani 2006; Monfort & Pena

2008; Boujelbene & Boujelbene 2010). Money supply also affects inflation in the short-

term in Papua, but it is not significant. Other short-term factors of inflation are wage, time

deposit rate and policy rate. Wage is positively affecting on inflation in all regions as

expected. However, these effects are only significant in Sumatra and Kalimantan with one

lag. On the other hand, time deposit rate and policy rate are positively related with

inflation, but they are not significant.

According to the empirical findings, Bank Indonesia as central bank of Indonesia

must maintain the stability of exchange rate since the role of exchange rate is really

important to influence inflation. Moreover, Bank Indonesia should concern with their

policy instrument, since Bank Indonesia rate as the policy rate is not significantly affecting

inflation. However, another monetary instrument, base money is still effective to control

inflation. The finding that money supply is significantly affecting inflation should not be

neglected. Therefore, Bank Indonesia could not rely on their policy rate only, but they can

also use their other policy instrument, such as base money.

The effort of Bank Indonesia to achieve a low and stable inflation is needed the

support of government. Based on the conclusion, the government can support to reduce the

impact of foreign price by issuing import policy. The policy can limit a number of good

imports and prevent domestic price from any various foreign inflation. Moreover, the

government can also release the export policy which supports local entrepreneurs to

increase the value of export. This policy can help Bank Indonesia to strengthen Indonesia

rupiah against foreign currencies.

Page 22

References

Andersson, M, Masuch, K & Schiffbauer, M 2009, 'Determinants of Inflation and Price

Level Differentials Across The Euro Area Countries', ECB Working Paper no.

1129, pp. 1-28.

Anugrah, DF, Dewati, W & Chawwa, T 2007, Model Inflasi Regional, Bank Indonesia,

Jakarta, Indonesia, unpublished manuscript.

Boschi, M & Girardi, A 2007, 'Euro Area Inflation: Long-run Determinants and Short-run

Dynamics ', Applied Financial Economics, vol. 17, no. 1, pp. 9-24.

Boujelbene, T & Boujelbene, Y 2010, 'Long Run Determinants and Short Run Dynamics of

Inflation in Tunisia', Applied Economics Letters, no. 17, pp. 1255-1263.

Breitung, J 2001, 'The Local Power of Some Unit Root Tests for Panel Data', Advances in

Econometrics, vol. 15, pp. 161-177.

Breitung, J & Pesaran, MH 2005, 'Unit Roots and Cointegration in Panels', Center for

Economic Studies & Ifo Institute for Economic Research Working Paper, no. 1565,

pp. 1-55.

Dlamini, A, Dlamini, A & Nxumado, T 2001, A cointegration Analysis of The determinants

of Inflation in Swaziland, http://www.centralbank.org.sz/docs/Inflation_Paper.pdf.

Domac, I & Elbrit, C 1998, 'The Main Determinants of Inflation in Albania', World Bank

Policy Research Working Paper no. 1930, pp. 1-40.

Endri 2008, 'Analisis Faktor-faktor yang Mempengaruhi Inflasi di Indonesia ', Jurnal

Ekonomi dan Pembangunan, vol. 13, no. 1, pp. 1-13.

Fahrer, J & Myatt, J 1991, Inflation in Australia: Causes, Inertia and Policy, RD Paper,

Economic Research Department, Reserve Bank of Australia, Australia

http://www.rba.gov.au/publications/rdp/1991/pdf/rdp9105.pdf.

Friedman, M & Schwartz, AJ 1982, Monetary Trends in the United States and United

Kingdom: Their Relation to Income, Prices and Interest Rates, 1867-1975,

University of Chicago Press, Chicago, USA.

Gachet, I, Maldonado, D & Perez, W 2008, 'Determinants of Inflation in a Dolarized

Economy: The Case of Ecuador', Cuestiones Economicas, vol. 24, no. 1:1-2, pp. 5-

26.

Granger, CWJ & Engle, RF 1987, 'Co-integration and Error Correction: Representation,

Estimation, and Testing', Econometrica, vol. 55, no. 2.

Gujarati, DN 2003, Basic Econometrics, Fourth Edition edn, McGraw-Hill.

Hallwood, CP & MacDonald, R 2000, International Money and Finance, Blackwell

Publishers Inc., Massachusetts, USA.

Im, KS, Pesaran, MH & Shin, Y 1997, 'Testing for Unit Roots in Hetergenous Panels', DAE

Working Paper, University of Cambridge, no. 9526.

Kandil, M & Morsy, H 2009, 'Determinant of Inflation in GCC', IMF Working Paper, vol.

09, no. 82, pp. 1-32.

Katarina, J & Johansen, S 1994, 'Identification of The Long-Run and The Short-Run

Structure an Application to The ISLM Model', Journal of Econometrics, vol. 63, pp.

7-36.

Khan, REA & Gill, AR 2010, 'Determinants of inflation: A Case of Pakistan (1970-2007)',

Journal Economics, vol. 1, no. 1, pp. 45-51.

Laksono, BYG 2005, Identifikasi Jalur Mekanisme Transmisi dan Efektivitas Kebijakan

Moneter dalam Mencapai Tingkat Inflasi yang Mendukung Pertumbuhan Ekonomi

http://www.centralbank.org.sz/docs/Inflation_Paper.pdf

http://www.rba.gov.au/publications/rdp/1991/pdf/rdp9105.pdf

Page 23

Daerah Faculty of Economics, University of Indonesia, Depok, Indonesia,

unpublished manuscript.

Leheyda, N 2005, Determinants of Inflation in Ukraine: a Cointegration Approach, Center

for Doctoral Studies in Ecionomics and Management (CDSEM), University of

Mannheim, Germany, http://www.univ-

orleans.fr/deg/GDRecomofi/Activ/leheyda_strasbg05.pdf.

Listiani, N 2006, 'Faktor-faktor Determinan yang Mempengaruhi Tingkat Inflasi di

Indonesia Periode 1970-2004 ', Jurnal Ekonomi dan Pembangunan, vol. 14, no. 1,

pp. 42-73.

Mankiw, NG 2007, Macroeconomics, 2007 edn, Chaterine Woods and Craig Bleyer, New

York, USA.

Monfort, B & Pena, S 2008, 'Inflation Determinants in Paraguay: Cost Push versus

Demand Pull Factors ', IMF Working Paper, vol. 08, no. 270, pp. 1-41.

Moser, GG 1995, 'The Main Determinants of Inflation in Nigeria', IMF Staff Papers vol.

42, no. 2, pp. 270-289.

Nicolae, C 2002, Determinants of Inflation in Romania,

http://www.dofin.ase.ro/Working%20papers/Nicolae%20Covrig/DETERMINANT

S%20OF%20INFLATION%20IN%20ROMANIA1.ppt.

Romer, D 2001, Advanced Macroeconomics, Second Edition edn, Mcgraw-Hill/Irwin.

Siregar, RY & Rajaguru, G 2002, 'Base Money and Exchange Rate: Sources of Inflation in

Indonesia during the Post-1997 Financial Crisis ', CIES Discussion Paper, vol. No.

0221.

Smith, R & Pesaran, MH 1995, 'Estimating Long-run Relationship from Dynamic

Heterogenous Panels', Journal of Econometrics, vol. 68, pp. 79-113.

Tjahjono, ED & Anugrah, DF 2006, Faktor-faktor Determinan Pertumbuhan Ekonomi

Indonesia , Bank Indonesia, Jakarta, Indonesia, unpublished manuscript.

Wimanda, RE 2006, Regional Inflation in Indonesia: Characteristic, Convergence, and

Determinants, Bank Indonesia, Jakarta, Indonesia, unpublished manuscript.

http://www.univ-orleans.fr/deg/GDRecomofi/Activ/leheyda_strasbg05.pdf

http://www.univ-orleans.fr/deg/GDRecomofi/Activ/leheyda_strasbg05.pdf

http://www.dofin.ase.ro/Working%20papers/Nicolae%20Covrig/DETERMINANTS%20OF%20INFLATION%20IN%20ROMANIA1.ppt

http://www.dofin.ase.ro/Working%20papers/Nicolae%20Covrig/DETERMINANTS%20OF%20INFLATION%20IN%20ROMANIA1.ppt

Page 24

Appendix

The Long-term Model of CPI Province

No Province C CPIUS ER R2

Obs

1 Banda Aceh

-29.10

(0.61)***

6.15

(0.15)***

0.31

(0.02)***

0.97 216

2 North

Sumatra

-23.08

(0.54)***

4.75

(0.13)***

0.42

(0.02)***

0.97 216

3 West Sumatra

-22.80

(0.53)***

4.70

(0.13)***

0.42

(0.02)***

0.97 216

4 Riau

-23.25

(0.56)***

4.84

(0.13)***

0.38

(0.02)***

0.97 216

5 Jambi

-21.76

(0.49)***

4.62

(0.12)***

0.34

(0.02)***

0.97 216

6 South

Sumatra

-24.22

(0.56)***

5.02

(0.13)***

0.40

(0.02)***

0.97 216

7 Bengkulu

-21.69

(0.50)***

4.55

(0.12)***

0.38

(0.02)***

0.97 216

8 Lampung

-22.74

(0.53)***

4.71

(0.13)***

0.40

(0.02)***

0.97 216

9 Jakarta

-20.31

(0.46)***

4.23

(0.11)***

0.40

(0.02)***

0.97 216

10 West Java

-20.77

(0.49)***

4.39

(0.12)***

0.36

(0.02)***

0.97 216

11 Central Java

-20.09

(0.48)***

4.32

(0.11)***

0.32

(0.02)***

0.97 216

12 Yogyakarta

-21.90

(0.50)***

4.57

(0.12)***

0.39

(0.02)***

0.97 216

13 East Java

-20.67

(0.54)***

4.32

(0.13)***

0.39

(0.02)***

0.97 216

14 Bali

-19.54

(0.54)***

4.09

(0.13)***

0.39

(0.02)***

0.96 216

Page 25

15 West

Kalimantan

-21.19

(0.48)***

4.35

(0.11)***

0.43

(0.02)***

0.98 216

16 Central

Kalimantan

-20.30

(0.89)***

4.22

(0.21)***

0.40

(0.03)***

0.91 216

17 South

Kalimantan

-20.55

(0.46)***

4.35

(0.11)***

0.36

(0.02)***

0.97 216

18 East

Kalimantan

-21.88

(0.47)***

4.60

(0.11)***

0.37

(0.02)***

0.97 216

19 North

Sulawesi

-21.49

(0.52)***

4.44

(0.12)***

0.41

(0.02)***

0.97 216

20 Central

Sulawesi

-25.55

(0.63)***

5.18

(0.15)***

0.45

(0.02)***

0.97 216

21 South

Sulawesi

-19.96

(0.51)***

4.20

(0.12)***

0.38

(0.02)***

0.97 216

22 Southeast

Sulawesi

-24.98

(0.58)***

5.15

(0.14)***

0.41

(0.02)***

0.97 216

23 West Nusa

Tenggara

-20.40

(0.53)***

4.26

(0.13)***

0.39

(0.02)***

0.97 216

24 East Nusa

Tenggara

-23.73

(0.57)***

5.06

(0.14)***

0.32

(0.02)***

0.96 216

25 Maluku

-19.52

(0.52)***

4.07

(0.12)***

0.40

(0.02)***

0.97 216

26 Papua

-25.05

(0.55)***

5.35

(0.13)***

0.30

(0.02)***

0.97 216



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