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Is there Inflation Threshold Effect for the PINE Countries?

Fentahun Baylie Sendkie1, Workineh Asmare Kassie

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April 2018

Abstract: This study investigates whether there is a threshold level of inflation above and

below which the impact of inflation on per capita GDP growth changes. We use the method

developed by Hansen (1999, 2000) to analyse the inflation-growth nexus for four rapidly

growing developing country economies – Philippines, Indonesia, Nigeria and Ethiopia

(PINE). We considered the period from 1968 to 2013 in the interest of data. Unlike previous

studies, we supplement the threshold regression method of Hansen (1999, 2000) by the

dynamic common correlated effects pooled mean group estimation method of Chudik and

Pesaran (2015). Both methods account for cross-sectional dependence. The findings show

that there is a double threshold (non-linear relationship) between which the level of inflation

can be optimally set to impact growth positively. A 1% increase in inflation increase

economic growth by 0.44% within the optimal range (5.59% - 7.59%). For inflation rates

out of this range, inflation either does not contribute or impedes economic growth. In

addition, while the impact of initial income is positive, investment as a share of GDP

adversely affects growth. Hence, the countries in the sample shall target inflation rates

between the recommended ranges.

Key words: Inflation • Economic growth • Threshold effect analysis • Cross-section

dependence • Panel data.

JEL classification code: C23, E31, O42, O57.

1 PhD candidate at Addis Ababa University, Addis Ababa, Ethiopia (email: fbaylie@yahoo.com) 2 Lecturer at University of Gondar, School of Economics, Gondar Ethiopia (email: workineh.asmare@uog.edu.et)

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1. Introduction

Ensuring price stability and sustained economic growth are among the macroeconomic policy

objectives of any country. Stable macroeconomic framework is defined as a macroeconomic

environment that is conducive to improved economic growth. Such an environment is

contrived to tender low and predictable inflation, appropriate real interest rates, sustainably

stable fiscal policy, competitive and predictable real exchange rates, and perceptively viable

balance of payments condition (Barro, 1998). However, uncertainty lends the inseparable link

between the stability of macroeconomic frameworks and countries’ growth performances.

Policy induced macroeconomic uncertainty may decrease the level and growth rate of

productivity by reducing the efficiency of the price mechanism. Uncertainty about the macro-

economy also tends to reduce the rate of investment both through capital flight and distorting

the incentives to potential investors to commit their resources (Pindyck and Solimano, 1993;

Lucas, 1973). In effect, businesses and households in unstable and unpredictable economy are

widely thought to perform poorly.

Basic macroeconomic policy indicators such as the rate of inflation, budget surplus (deficit)

and black-market exchange premium may be used in measuring the stability of country’s

macroeconomic framework (Fischer, 1993)3. Amongst the macroeconomic policy variables

whose behaviour is a distinguished parameter for macroeconomic stability, inflation is the

subject of this paper in which the rate of inflation is argued to indicate whether

macroeconomic policies are conducive to economic growth. In other words, it tells the ability

of governments to manage the economy implying that the government has lost control of the

economy lest it is producing high inflation. Moreover, businesses and households in a high

and unpredictable inflation economy behave poorly with the uncertainty and consequent

distortions of the incentive structure.

However, it is worth regarding the developments in the broader policy debate in which the

issue of price stability (inflation) is presented to be an intermediate goal while anchoring

country’s long-term and equitable growth as the ultimate concern of a macroeconomic policy

framework (Stiglitz et al., 2006). The use of productive capacity, specifically the employment

of capital and labour at their highest potential level, and the growth of this productive capacity

for a given economy are brought to the centre of economic policy debates in view of

3 Note that neither defining and measuring macroeconomic framework stability nor the optimal level of macroeconomic policy variables (optimal rate of inflation, appropriate interest rate, real exchange rate or so) is readily available.

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prioritization. The long-held view in mainstream economics – price stability as the primary

policy objective – is thus challenged for its confusion in that high inflation is argued of no

concern except its role as an ‘easy-to-see indicator’ of economic mal-performance. Under this

strand of the policy debate, inflation is argued to be endogenous and hence a mere symptom

of external shocks to the economic system that could result in inflation itself while

maintaining that unexpected and volatile (hyper) inflation creates huge uncertainty about

changes in relative prices. So, broader adjustment in real variables to serve the long-term

societal well-being in an equitable and sustainable manner, while mitigating shocks, than

ensuring price stability by tightening standard macroeconomic policies is argued as the

cardinal goal of economic policy.

In contemplating on the empirical relationship between macroeconomic stability and growth

by price stability, deflation has equally become as worrisome as inflation for it compels many

debtors to pay increasingly large amounts and for its subsequent effect in slowing economic

growth (Greenwald and Stiglitz, 1993). Experiences of several economies (e.g. Japan)

inspired a renewed attention to deflation in which thinking about the desirable rate (level) of

inflation got momentum with a significant move into the practice of inflation targeting as an

alternative monetary policy strategy. With inflation targeting regimes, however, central banks

do not have a complete control over the targets. There are unanticipated random disturbances

such as rise in oil prices and bountiful harvest which may lead to higher/lower levels of

inflation than planned/targeted ones. The consensus is that the optimal inflation is somewhat

positive provided that such optimal rate can be analytically defined and empirically estimated

for an economy (Akerlof et al., 2000).

In this paper, we examine the inflation-growth nexus under the overarching assumption that

inflation can be an important indicator to the stability of a macroeconomic policy framework.

Generally, the relationship between inflation and growth remains contentious in both theory

and empirical studies that analyse (if any) the nature of the relationship and direction of

causality. Theoretical developments in the literature documented different impacts of inflation

on economic growth (Drukker et al., 2005). One set of prediction posited no effect of inflation

on growth – money is super-neutral (Sidrauski, 1967). However, Tobin (1965) implied a

positive effect of inflation on long-run economic growth. The other set, contrarily, maintained

a negative effect of inflation on growth (Stockman, 1981). Another set still maintained a

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negative effect of inflation on long-run growth, but only if the level of inflation is above some

threshold level (Huybens and Bruce, 1998).

In the same vein, an enduring policy debate has long been taking place between structuralists

and monetarists in which structuralists maintained that inflation is essential for economic

growth contrary to the detrimental view of monetarists. For instance, even though empirical

evidence was scanty up until mid-1970s, casting substantial doubt on the direction of the

relationship, some development theories adhered to the idea that inflation is important to

growth through the forced savings mechanism of the structuralists’ approach. The relationship

is inconclusive for the global economy which has experienced all possibilities including

inflation with (without) growth and stability with (without) growth (Friedman, 1973).

Apparently, real growth rates have sometimes happened impressive in periods of high

inflation, even, far better than growth rates in countries that managed inflation down.

Moderate rates of inflation are often associated with rapid economic growth while periods of

low inflation are accompanied by the slowest rates of growth, suggesting loose relationship

between inflation and growth in so long as it is below some threshold (Stiglitz et al., 2006).

Intensive empirical researches have been undertaken on the existence and nature of the

relationship that established a case for an unequivocal negative temporal association between

high inflation and (medium) long term growth (e.g. Fischer, 1993; Bruno and Easterly, 1995).

The causal relationship is generally considered to run from the distortive effect of high

inflation and resulting high variability in the relative prices to lower output growth. The

empirical evidence is recently overwhelming. Policy makers, during the last two decades or

so, have maintained that lowering inflation is conducive to economic growth both in

developed industrial countries and their counterpart low income countries (Khan and

Senhandji, 2001). More so, the relationship between inflation crisis and growth is established

to be a ‘phenomenon’ by its own right than a mere reflection of the growth effects of shocks

and policies confounding the relationship (Bruno and Easterly, 1995).

Inspired primarily by the huge concern in Ethiopia, we examine the relationship between

inflation and economic growth for the panel of PINE countries (Philippines, Indonesia,

Nigeria, Ethiopia). Part of the impetus for this study is the simultaneous experience of both

economic growth and increasing inflation for these developing countries. In fact, the

inconclusive nature of the empirical evidence on the relationship and the single-digit inflation

targeting regime adhered by countries are equally considered important points of departure.

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The commonly shared idea has become testing if the inflation-growth relationship is non-

linear in that it could be positive or non-existent for low rates of inflation and becomes

negative for reasonably higher rates of inflation. Following this line of argument, the

objectives of the study are twofold: (1) to test if the relationship between inflation and growth

is non-linear (has threshold) in a panel of developing countries including Ethiopia; and (2) to

estimate (if it does exist) the threshold level of inflation at which the relationship inflects. If a

threshold effect does not exist, we examine whether there is a robust negative relationship

between inflation and growth. The possibility a non-linear relationship and supporting

evidence from several country and cross-country studies are well documented in the literature

(Fischer, 1993; Barro, 1996; Judson and Orphanides, 1996; Bruno and Easterly, 1998; Ghosh

and Phillips, 1998; Sarel, 1996; Christofferson and Doyle, 1998; and Khan and Senhadji,

2001). Inflation threshold effect (if it does exist) differs across countries and the existence and

level of the threshold is important to establish a link between the stability of macroeconomic

policy framework and economic growth.

2. Inflation and growth in the PINE countries

PINE is an acronym for a group of four countries; namely Philippines, Indonesia, Nigeria and

Ethiopia. The term was first used by the Time magazine correspondent Michael Schuman in

March 2013 to report on the economic performance of these countries. Since then, there are

mounting discussions on whether PINEs can take over the BRICS. The shift is assumed to be

caused by pessimist future about the BRICS in particular; falling economic growth rates in

Brazil and India, economic sanctions in Russia and rising debt in China. The PINEs represent

a population of more than 600 million from two different continents (IMF, 2017). This group

of countries exhibit sensible pattern in growth and inflation.

Table 1: Feature of the PINE Economy

Country Population

(million)

(2018*)

Real GDP per

capita (US$)

(2018*)

Average growth

rate (1999-2008)

(%)

Average inflation

rate (1999-2008)

(%)

Philippines 106.5 3894 6.9 4.6

Indonesia 266.8 4116 5.0 4.0

Nigeria 195.9 2563 7.5 11.6

Ethiopia 107.5 838 8.1 10.2

Note: an asterisk shows forecast for that year. (Source: IMF, 2017)

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Ethiopia is an agrarian economy placed in the horn of Africa. Inflation and growth of real GDP

followed the same directional trend for the post 2000 period. Inflation is sometimes erratic and

higher than growth rate of the real GDP. Inflation rate was 18.7% while the growth rate of real

GDP was 10% during the quarters between 2002Q1 and 2010Q4 (Tezera, 2013). An average

annual inflation rate of 15.4% was recorded during the period 2006-2010. The average growth

rate was 11% during the same period (Teshome, 2011). However, there are variation across

studies for whether changes inflation rates at such higher levels affect economic growth

positively or negatively.

Indonesia is an island located between the Asian continent and Australia. Indonesia is a

member of the OPEC organization which stems its revenue mainly from the exports of oil and

gas. Though the inflation rate is prone to oil price in the world market, it follows the path of

the real GDP growth. The real GDP has been growing by about 5% in the past decade while

the inflation rate grows by 4% on average in the same period (Hossain, 2005).

Nigeria is another oil dependent economy from west Africa where growth and inflation shows

the same trend. The Nigerian economy recently grows by about 5% on average between 2011

and 2015 with an average inflation rate of nearly 10% in the same period (NBS, 2016). The

economy might have enjoyed a better rate of growth if the inflation rate had been lower than

the current rate (Obi et al., 2016)

The Philippine economy is one of the rapidly growing economies in east Asia. Inflation rate

has remained stable for long except for the mid-2000s where it has increases by more than

double due to the rise in oil price. The economy’s growth is averaged 6.3% while inflation

remains 2.4% for the period 2014 - 2016 (WB, 2017).

3. Theoretical framework

2.1 Modelling the threshold effect in the inflation–growth nexus

Based on the theoretical developments and empirical contributions thus far, researchers would

generally agree that economic time series are mainly non-stationary. They would agree on the

possible nonlinearities in economic time series and in economic relationships, as well. To

distinguish nonlinear time series from non-stationary series is thus of critical importance in

time series applications. To this end, threshold models have gradually gotten momentum. First

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developed by Tong (1978/83) to capture the nonlinear movements in a time series, threshold

Autoregressive (TAR) model and its extensions are substantially applied in economic research

while enormously influencing econometric theory. Following the seminal work of Tong

(1983), several variants are also proposed including, inter alia, the smooth transition threshold

autoregressive (STAR) model of Chan and Tong (1986); the multivariate TAR model of Tsay

(1998). Of the major applications in economics, TAR is used to model output, interest rates,

stock returns, prices, exchange rates, and forecasting in general (Hanson, 2011).

In the application to business cycle specifically, the first generation of applications used

univariate models while the second generation of applications happened to use multivariate

models – threshold VECM and VTAR – to jointly model aggregate output and other variables.

Extensions and applications of the TAR model are overwhelming in that several contributions

are made with respect to the nature of data, the threshold variable and assumed number of

regimes. For we are particularly interested in testing the presence of threshold effects in the

relationship between inflation and growth, a TAR model with a single threshold variable is

used to model the threshold effect in the inflation-growth nexus (if it does exist) while

controlling for correlates of economic growth in accordance with the standard empirical

growth literature. The general form of a threshold autoregressive model can be specified as in

what follows in which is an indicator panel series taking values J=1, 2, ..., T and i=1, 2, …,

N and (Tong, 2010):

With a simplifying assumption of lower order ( ), indicator panel series (

); and for some threshold ‘ ’, a positive integer delay parameter ( ) and a state determining

variable ( ); the TAR model can be expressed equivalently as:

The TAR model assumes that different regimes can be determined based on the threshold

variable ( ) and the threshold value ( ). With further developments in testing for threshold

effects, TAR models offered supporting evidence for a more meaningful non-linear

specification because it happened to capture the effect of abrupt and sharp movements of

economic variables and in their empirical relationships. The statistical implication has thus

become testing the hypothesis of unit root (linear non-stationary) against a stationary TAR

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(non-linear stationary) and linearity against the alternative of non-linearity while allowing for

the possibility of non-stationary under the respective null hypothesis (Hansen, 2011). A log

(semi-log in case there are negative observations) transformation of the inflation variable is

employed as in what follows by specifying a hybrid function of inflation ( )

(Sarel, 1996; Ghosh and Phillips, 1998). Distribution of the transformed inflation data is much

more symmetric and befit with the assumption of normality. And, is continuously

differentiable.

[3]

is an indicator variable Such that:

Defining further the threshold level of inflations by , we introduced a dummy

variable to define the threshold regimes as follows ( ):

We thus specified the following two regime threshold model in a single threshold

variable to test for the existence of a threshold effect.

[6]

In this specification, – vector of control variables – is included in the threshold model as

the most important regressors found in the empirical growth literature. The effect of inflation

on growth is given by the parameter estimate ‘ ’ for inflation levels less than (equal to) the

threshold level ( ) and by ‘ ’ for inflation levels greater than the threshold level that divide

the relationship into two regimes.

2.2 Dynamic common correlated effects pooled mean group estimation (CCEPMG)

Hansen’s (1999, 2000) method undertakes a short-run analysis. As part of a robustness check,

we employ the dynamic common correlated pooled mean group estimation (Chudik and

Pesaran, 2015). This method helps to examine the nature of relationship between inflation and

economic growth and determines whether the relationship is a short-run and/or long-run

phenomenon. The method provides pooled estimates for the coefficients in the short-run

which may be comparable to the results of Hansen’s (1999, 2000) threshold estimation.

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Moreover, it also provides country-specific short-run and pooled long-run estimates for the

entire group. The dynamic common correlated pooled mean group estimation method has

several merits over short-run traditional methods such as Hansen’s (1999, 2000), some of

which may include its natures to allow for heterogeneity of short-run coefficients, and account

for cross-section dependence. And, CCEPMG estimator is the most ideal estimator in

providing the best combination between consistency and efficiency (Cavalcanti et al., 2015).

Like the Hansen’s (1999, 2000) method, the method of Chudik and Pesaran (2015) accounts

for cross-sectional dependence which may result from any common unobserved factor

incorporated in the error term. Cross-sectional dependency associated with multiple factors

correlated with regressors, and serial correlation in errors and lagged dependent variables are

accounted by the latter method (Shin 2014; Chudik and Pesaran, 2015). Cross-sectional

averages of dependent and explanatory variables and their lagged values are used to filter out

linear combinations of unobserved common factors. These cross-section averages are then

used in the regression.The following form of the dynamic common correlated effects pool

mean group (CCEPMG) estimator is used in this paper (Cavalcanti et al., 2015).

[7]

is the log of real GDP per capita for country i at time t. is a vector of explanatory

variables including inflation in semi-log. and are cross-section averages of the

variables. is country-specific factor (Cavalcanti et al., 2015). Cross-section averages are

assumed to be approximations of common unobserved factors such as,

where (Chudik and Pesaran, 2015). Estimated coefficients of cross-section

averaged variables are not interpretable in a meaningful way. They are merely present to alter

out the biasing impact of unobservable common factors (Eberthardt, 2011).

4. Methodology of the Study

4.1 Pesaran’s cross-sectional dependence test

The problem of cross-section dependence mixes information from different cross-sections and

this may result biased estimates and spurious inference during panel estimation (Cavalcanti et

al., 2015). To prevent this from happening, there arises a need to test for and correct the

problem of cross-section dependency. The problem may be caused by common unobserved

factors, spatial effects, or socioeconomic network effects (Breitung and Pesaran, 2007; Shin,

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2014). Though there are alternative tests, Pesaran's (2004) cross-sectional dependence (CD)

test is used in this paper. It is the most powerful test (Eberhardt, 2011) and is given by:

[8]

and are two groups with pairwise correlation coefficient , T is the number of common

observations, and (Omay et al., 2015).

4.2 Panel unit root tests

Two panel unit root tests which account for cross-section independence are used in this

section. These are the Im-Pesaran-Shin (IPS) and Fisher type tests. The tests are also helpful

in dealing with unbalanced data in which N smaller relative to T. The first test is a cross-

sectionally augmented IPS (CIPS) test which is defined by a standard ADF test which further

augmented the regression equation with cross-section means of lagged levels and first-

differences of individual series. The common factors are approximated by the cross-section

means of the dependent variable and its lagged values (Breitung and Pesaran, 2007; Pesaran,

2007).

[9]

where

for an observation on ith cross-section unit at time t. It is tested

for the and .

The second panel unit root test used in this study is the Fisher test. This test also accounts for

cross-section dependence. Different p-values are combined from panel-specific unit-root

tests in the Fisher-test. There are alternative Fisher-type tests. The most powerful test of the

Fisher-type test, the inverse normal Z statistic, is used in this study (Maddala and Wu, 1999).

[10]

This study employs two types of estimation methods as shown in section 2: panel threshold

regression methods as framed by Hansen (1999, 2000) and the dynamic common correlated

effect approach of Chudik and Pesaran (2015). Hansen’s (1999, 2000) method uses fixed

effects model which requires all the variables to be stationary at label. Hence any

nonstationary series shall be used in its differenced form in the regression analysis. In Chudik

and Pesaran’s (2015) method, nonstationary series are required. Both methods of estimation

account for cross-sectional dependence and serial correlation.

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4.3 Panel Co-integration Tests

Panel cointegration tests are of two types; first- and second-generation test. We use

Westerlund’s second generation panel cointegration test in this study (Persyn and Westerlund,

2008). Second-generation tests account for cross-section dependence. The methods bootstrap

p-values to account for dependence and heterogeneity within and across cross-sectional units.

The null hypothesis of no co-integration is tested against the alternative hypothesis of co-

integrated relationship by using error correction mechanism for individual panel members or

for the panel. The error correcting model is as given in eq. (10) (Eberthardt, 2011).

[10]

where is the error correction term. If , with (no cointegration) and

(there is cointegration).

4.4 Data and Variables

This empirical analysis uses annual data ranging from 1968 to 2013 for four developing

countries (Ethiopia, Indonesia, Nigeria, and Philippines) from Penn World Table 9.0 and

World Development Indicators databases. The choice of study period for each country

depends on data availability. The countries are selected based on the record of inflation-

growth nexus. The countries in the sample experience high inflation rates (double-digit)

during periods of high rates of economic growth (double-digit).

Maintaining the view that the long-run relationship between inflation and economic growth is

a phenomenon by its own right, on one hand, and in accordance with the standard practice in

empirical growth studies, the variables of interest – dependent, threshold and control variables

– are selected. Despite the expressed interest to study the threshold effect of inflation on

economic growth, the influence of other economic variables that are correlated with inflation

must be controlled in empirical analyses. Thus, the growth rate of GDP per capita is

considered as a dependent variable and the measure of inflation (variable) is treated as a

regime-dependent regressor while serving as the threshold variable in the relationship between

growth and inflation. Following, inter alia, Islam (1995), Khan and Senhadji (2001), and

Kremer et al. (2009), we also included investment as a share of GDP and initial income per

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capita. The selection of the variables is subject to the robustness tests as contained in the

works of both Levine and Renelt (1992), and Sala-i-Martin (1997).

The annual growth rate of real GDP per capita ( in constant 2000 prices is computed

(local currency). The rate of inflation ( ) is calculated as the annual percentage change in the

consumer price index (CPI). However, one important implication of using inflation levels in

empirical growth regressions is that the marginal effect of inflation is independent of the

average level of inflation and additive inflation shocks will have identical effects on growth in

high and low inflation periods. This is less plausible compared to using log transformed

inflation in which multiplicative inflation shocks will have identical effects on growth in low

and high inflation periods. As in many applications and following specifically Sarel (1996)

and Ghosh and Phillips (1998), who strongly suggest log models to avoid the effect of

extreme inflation observations and strong asymmetry.

5. Results and discussion

The analysis was begun by performing preliminary tests. In theory, not every unit root test

provides appropriate results in the presence of cross-sectional dependence. Test of cross-

sectional independence was performed to decide the type of panel unit root test to be used.

Using Pesaran CD test, the null of cross-sectional independence was rejected for the raw data

at 1% level of significance (annexure 1). This shows that some series were cross sectionally

dependent initially. Hence, panel unit root tests which account for cross section dependence

are used. IPS and Fisher panel unit root tests are used in such a way that they account for

cross-sectional dependence. The results of both tests with different assumptions are given in

annexure 1. Initial income and investment are stationary after first differencing at 1% level of

significance. Per capita GDP and log of inflation are stationary at levels.

5.1 Panel threshold regression results

The most common issue in the analysis of inflation threshold is whether to use level of

inflation or log of inflation. We made the decision based on theoretical as well as empirical

grounds. Empirically, we made the choice between using log of inflation or inflation based on

the behaviour of their distribution. Though there is a deviation from normal distribution for

both variables, the figures in annexure 1 show that the deviation is smaller for log of inflation.

Hence, we consider using semi-log of inflation in the current analysis.

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This paper adopts sequential method of identifying threshold from Hansen (1999, 2000). In a

three-step procedure, two thresholds are identified for inflation. In the first two steps, the test

for threshold effect shows that we reject the null of no threshold and single thresholds in

favour of single and double threshold alternatives, respectively. In the third step, we fail to

reject double threshold against triple threshold. Therefore, this is an indication of a significant

(double) threshold effect of inflation on economic growth for the countries in the sample. The

identified levels of threshold levels are equal to 5.59% and 7.59% (the results in table 2 are in

logs). The threshold regression that follows table 2 determines whether the optimal levels of

inflation rates are below, between, or above these threshold levels.

Table 2: Threshold effect test (bootstrap = 300 300 300)

Model Threshold Lower Upper Fstat Prob Crit10 Crit5 Crit1

Single 0.7392 0.7223 0.7475 74.02 0.0000 6.0325 7.5891 14.5855

Double 0.8805 0.8476 0.8864 43.47 0.0000 17.1689 18.8043 30.9013

Triple 1.0608 1.0527 1.0621 2.41 0.4833 10.0825 21.981 49.7221

In table 3, the total sample is categorized based on the regimes for inflation threshold. The

threshold regression creates a dummy variable for inflation based on the two threshold levels

identified above. Three regimes are created based on the two thresholds. A regime takes a

value of ''0'' for inflation rates less than 5.59%, a value of ''1'' for inflation rates between

5.59% and 7.59%, and a value of ''2'' for inflation rates greater than 7.59%. For this

categorization, the optimal level of inflation rate is tested for the four countries in the sample.

The impact of inflation is positive and significant only for the second regime. Therefore, the

optimal rates of inflation which are conducive for economic growth are between 5.59% and

7.59%. This means, inflation facilitates economic growth only if the countries in the sample

target to set the rates within this range. The result shows that a 1% increase in inflation rate

increases economic growth by 0.44% within this range.

The result of the regression in table 3 shows the nature of relationships between economic

growth and other covariates, as well. It shows that the impact of initial income per capita is

positive, and that of investment as share of GDP is negative on economic growth. A 1%

increase in initial income improves economic growth by 0.43% when inflation is used as a

regime specific variable. Contrariwise, a 1% increase in the share of investment in GDP

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hampers economic growth by 0.27% when inflation is used as a regime specific variable. This

may be associated with the crowding out effect of public investment on private investment.

Table 3: Fixed-effects regression: Inflation Threshold

Independent

Variables

Double Threshold

Coef. t-stat. P>|t|

-0.2687 -5.47 0.000

0.4258 6.42 0.000

0 -0.0092 -1.47 0.144

1 0.4373 3.01 0.003

2 -0.0172 -1.10 0.271

_cons 0.0216 1.42 0.157

#Obs. 184

R-Squared 0.2865

To make the results of Hansen’s (1999, 2000) estimation method comparable with the

CCEPMG method, the cross-sectional averages of the explanatory variables were included in

the threshold regression. The findings are almost the same with the case where cross-section

averages are not included. The inclusion of these variables does not significantly alter the

result. The threshold regressions including cross-sectional averages of the explanatory

variables are available in annexure 1.

5.2 The CCEPMG regression

The dynamic common correlated effects pooled mean group (CCEPMG) estimation method is

basically meant to analyse a long-run analysis. While it is the case also here in this section, the

emphasis is to assess the results of the short-run analysis which is comparable to the Hansen’s

(1999, 2000) method and may be used as a robustness check. The difference between the two

methods is as follows. The Hansen’s (1999, 2000) method provides pooled short-run

estimates for the countries in the sample. It does not assume country-specific differences. The

CCEPMG method provides country-specific short-run estimates, as well. The interest in this

section is not on comparing optimal levels of inflation rates determined by the two methods.

The main purpose in this section is to determine whether inflation also exhibits non-linear

relationship with growth for each country. The outcomes from both methods reveals that the

relationship between inflation and economic growth non-linear.

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Table 4 The CCEPMG estimator

Sample Pooled long-run coefficients

All countries -0.2681

(2.9717)

0.0165

(0.1823)

-0.0004

(0.0041)

-5.7761

(63.183)

0.7103

(8.129)

Sample Pooled and Country-specific Short-run coefficients

All countries

(pooled)

-0.0364

(0.0649)

0.0007

(0.0022)

1.0E-05

(2.3E-05)

-6.7996***

(2.3331)

-1.2367

(0.9313)

Ethiopia -0.0511***

(0.0018)

0.0002***

(5.6E-06)

4.6E-05***

(2.0E-09)

-13.570

(2230.3)

-1.2671**

(0.2943)

Indonesia 0.0711***

(0.0008)

-0.0021***

(6.5E-07)

1.5E-05***

(3.5E-11)

-5.9953

(106.45)

0.0573

(0.1637)

Nigeria -0.2141***

(0.0134)

0.0071***

(1.4E-05)

-5.7E-05***

(1.2E-09)

-3.0976

(4.2369)

-3.8605**

(0.8433)

Philippines 0.0484***

(0.0018)

-0.0025***

(3.9E-06)

3.6E-05***

(6.8E-10)

-4.5353

(63.608)

0.1232

(0.2578)

Note = log of real GDP per capita. , , and

are inflation in its linear, quadratic and cubic forms, respectively.

is log of real investment as a share of GDP. is initial income per capita. shows first difference. ,

, and

, , and are cross-section means.

***, **, and * refer to significance levels at 1%, 5%, and 10%, respectively. Standard errors in parentheses

The CCEPMG regression in table 4 used inflation in its level- , quadratic- , and cubic-

forms. The model is cubic function about inflation. This helps to determine whether the

marginal effect of inflation is non-linear with growth. The first derivative of the growth

function with respect to inflation is used to determine the number of solutions for the reduced

(quadratic) equation. Using the formula , we determine the number of and signs of

solution for the quadratic equation. If , each quadratic function has two

solutions. Using the formula, the coefficients of country-specific results shows two possible

levels of inflation rates. The country-specific results show that the estimate for the coefficients

of these variables are significant at 1% for all countries in the sample. The results in table 4

are comparable to the results of Hansen’s (1999, 2000) estimation method in the above section

in terms of the additional covariates included in the model (annexure 1). The results in table 4

do not include the report for coefficients of cross-sectional averages as they are not interpreted

in a meaningful. They are merely presented to alter out the effects unobservable common

factors. The regression results including cross-sectional averages are available in annexure 1.

16

6. Conclusions and implications

The findings in this study show that there is negative relationship between inflation and

growth in general. On average, a 10% inflation rate may retard economic growth by 0.1% for

the countries in the sample during the study period when a threshold is not assumed. A

(double) threshold level of inflation is found between which the levels of inflation rates can be

optimally set to impact growth positively. The findings show that inflation rates must be set

between 5.59% - 7.59% to affect the growth of per capita GDP positively. Inflation rate below

and above this range does not contribute to growth. In addition, the results of threshold

regressions show that initial income positively impacts economic growth and the effect is

stronger within the optimal range of the inflation rates. The results also show that investment

as a share of GDP adversely affects economic growth and the effect is stronger within the

optimal range of the inflation rates. The results of the CCEPMG estimation confirm that the

relationship between inflation and economic growth is non-linear.

Therefore, inflation rates have to be targeted to fall within the optimal range 5.59% and

7.59%. Inflation rates set within the range are more conducive for economic growth than

inflation rates above or below the range. Countries in the sample are advised to avoid the

adverse effect of government expenditure which may have been crowding out private

investment in some sectors.

17

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21

Annex 1

Table 1: Descriptive Statistics (all countries) (#of obs.=257)

Variables Obs. Mean Std. Dev. Min. Maxi.

203 0.0221 0.1303 -1.1899 0.4524

207 0.6768 1.6176 -10.809 3.0555

207 21.1342 83.2367 -9.8088 1136.25

207 -2.1274 0.6215 -3.478 -0.9915

ln( ) 203 11.9869 1.1859 9.4521 14.6391

Table 2: Covariance Analysis (Correlation)

Variables ln( )

1

-0.1842 1

-0.006 0.4595 1

ln( ) 0.492 -0.0774 0.2151 1

0.078 -0.06 0.2405 0.5724 1

Table 3: Pesaran CD test and Average Correlation Coefficients

Note Ho: Cross-sectional independence

Variable CD-test P-value Corr. Abs(corr.)

3.4 0.001 0.249 0.291

2.01 0.044 0.138 0.149

-0.24 0.81 -0.018 0.094

4.88 0 0.337 0.344

ln( ) 8.36 0 0.6 0.6

22

Table 4: Panel Unit root tests (IPS and Fisher tests)

Variables Specifications IPS statistics

Fisher statistics

(inverse normal z)

Order of

integration

Individual intercept -9.2078*** -9.1613*** I(0)

Intercept and trend -8.4317*** -8.0627***

Individual intercept -9.7270*** -9.7000*** I(0)

Intercept and trend -9.8341*** -9.3384***

Individual intercept 0.2996 0.3376

Intercept and trend -0.2929 -0.3186 I(1)

Individual intercept -14.8302*** -13.7613***

Intercept and trend -14.9167*** -12.9447***

Individual intercept 0.0708 0.1473

ln( ) Intercept and trend 2.8371 3.0553 I(1)

Individual intercept -9.0550*** -9.0233***

dln( ) Intercept and trend -8.3230*** -7.9767***

***, ** and * indicates rejection of null hypothesis (unit root) at 1%, 5% and 10% respectively

Table 5: Fixed-effects regression: Inflation Threshold

Independent

Variables

Single Threshold Independent

Variables

Double Threshold

Coef. t-stat. P>|t| Coef. t-stat. P>|t|

-0.2940 -6.15 0.000 -0.3017 -6.40 0.000

0.3954 5.89 0.000 0.3906 5.91 0.000

1.0485 9.01 0.000 1.0269 8.94 0.000

0.2605 2.67 0.008 0.2744 2.85 0.005

-0.3741 -2.77 0.006 -0.3601 -2.71 0.008

0 -0.0170 -3.13 0.002

0 -0.0137 -2.48 0.014

1 -0.0031 -0.23 0.819 1 -0.1063 -2.98 0.003

2 -0.0204 -1.37 0.174

_cons -0.0006 -0.04 0.967 _cons 0.0199 1.26 0.209

#Obs. 184 #Obs. 184

R-Squared 0.4838 R-Squared 0.4968

23

Table 6: The CCEPMG estimator

Sample Long-run coefficients

All

countries

-0.2681

(2.9717)

0.0165

(0.1823)

-0.0004

(0.0041)

-5.7761

(63.183)

0.7103

(8.129)

-0.0582

(0.8432)

0.0005

(0.0212)

3.78E-05

(0.0004)

3.78E-05

(59.045)

4.8895

(54.174)

Sample Short-run coefficients

CointEq

All

countries

-0.0364

(0.0649)

0.0007

(0.0022)

1.0E-05

(2.3E-05)

-6.7996***

(2.3331)

-1.2367

(0.9313)

0.2057

(0.1809)

-0.0064

(0.0058)

3.54E-05

(3.55E-05)

-0.3395

(2.0728)

-1.0785

(1.0735)

-0.1041*

(0.0596)

Ethiopia -0.0511***

(0.0018)

0.0002***

(5.6E-06)

4.6E-05***

(2.0E-09)

-13.570

(2230.3)

-1.2671**

(0.2943)

0.1289***

(0.0136)

-0.0039***

(9.4E-06)

2.3E-05***

(3.3E-10)

5.2399

(10.418)

-4.1456

(2.2838)

-0.2771

(9.4022)

Indonesia 0.0711***

(0.0008)

-0.0021***

(6.5E-07)

1.5E-05***

(3.5E-11)

-5.9953

(106.45)

0.0573

(0.1637)

-0.0112***

(0.0014)

0.0013***

(2.4E-06)

-1.8E-05***

(2.7E-10)

0.1949

(1.0979)

-0.1718

(0.2943)

-0.0759

(0.7024)

Nigeria -0.2141***

(0.0134)

0.0071***

(1.4E-05)

-5.7E-05***

(1.2E-09)

-3.0976

(4.2369)

-3.8605**

(0.8433)

0.7375***

(0.0121)

-0.02363***

(1.2E-05)

0.0001***

(5.1E-10)

-4.2485

(40.188)

-0.7947

(6.9341)

-0.0048

(0.0036)

Philippines 0.0484***

(0.0018)

-0.0025***

(3.9E-06)

3.6E-05***

(6.8E-10)

-4.5353

(63.608)

0.1232

(0.2578)

-0.0325***

(0.0011)

0.0004***

(7.9E-07)

-1.7E-06***

(3.0E-11)

-2.5445**

(0.7465)

0.7978**

(0.2185)

-0.0587

(0.4245)

Note = log of real GDP per capita. , , and

are inflation in its linear, quadratic and cubic forms, respectively. is log of real investment as a share of

GDP. is initial income per capita. shows first difference. , , and , , and are cross-section means.

***, **, and * refer to significance levels at 1%, 5%, and 10%, respectively. Standard errors in parentheses

24

Annex 2

0

50

100

150

Frequency

-10 -5 0 5 Figure 2: semi log of inflation, chi

0

20

40

60

80

100

Frequency

0 50 100 150 Figure1: inflation, chi