This article was downloaded by: [McMaster University]On: 21 December 2014, At: 10:53Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Macroeconomics and Finance inEmerging Market EconomiesPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/reme20

Inflation–growth nexus: some bivariateEGARCH evidence for BangladeshBiru Paksha Paul aa State University of New York at Cortland, EconomicsDepartment , SUNY Cortland , USAPublished online: 06 Aug 2012.

To cite this article: Biru Paksha Paul (2013) Inflation–growth nexus: some bivariate EGARCHevidence for Bangladesh, Macroeconomics and Finance in Emerging Market Economies, 6:1, 66-76,DOI: 10.1080/17520843.2012.695385

To link to this article: http://dx.doi.org/10.1080/17520843.2012.695385

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Inflation–growth nexus: some bivariate EGARCH evidence for

Bangladesh

Biru Paksha Paul*

State University of New York at Cortland, Economics Department, SUNY Cortland, USA

(Received 13 February 2012; final version received 16 May 2012)

This study examines the inflation–growth nexus for Bangladesh over the period1976–2009 in a bivariate exponential generalized autoregressive conditionalheteroscedasticity in mean (EGARCH-M) model. This work finds that bothgrowth and inflation adversely affect each other in a lagged fashion inBangladesh. Inflation uncertainty appears to be conducive to growth for thecountry, contradicting the Friedman hypothesis. Growth uncertainty, which isalso thought to be inimical to growth, affects the average growth rate positively.Thus, the Central Bank should shift its target from controlling inflationuncertainty to reducing a rise in inflation to ensure faster growth in Bangladesh.

Keywords: inflation; growth; Bangladesh economy; inflation uncertainty; growthvolatility; EGARCH

JEL Codes: E31; E58; C22; O53

1. Introduction

The inflation–growth nexus has been one of the most examined relationships inmacroeconomic research. Most studies, however, find this relationship inconclusive,particularly for emerging economies where both economic growth and inflation arehigh. Bangladesh, being one of the fastest growing economies in South Asia, hasdisplayed moderately high inflation and price volatility since independence in 1971.This scenario has raised a number of questions such as: 1) Is high inflationdetrimental to Bangladesh’s growth? 2) Does growth have any impact on thecountry’s inflation? 3) Does inflation uncertainty reduce growth? 4) Does inflation orgrowth show persistence of any degree? These questions have remained unansweredin Bangladesh’s context, creating a gap. This study attempts to fill that gap byaddressing these questions.

The inflation–growth interaction has produced conflicting results in theliterature. Friedman (1977) proposed that higher mean inflation increases inflationvolatility, which reduces economic growth. Holland (1995) argues that inflationuncertainty will have a negative effect on the average rate of inflation due tomonetary contraction of the Central Bank during price volatility. In contrast, Dotseyand Sarte (2000) show that inflation uncertainty can have a positive effect on outputthrough a rise in precautionary savings. Cukierman and Meltzer (1986) argue thatthe Central Bank implements an expansionary monetary policy during higher

*Email: biru.paul@cortland.edu

Macroeconomics and Finance in Emerging Market Economies,

� 2013 Taylor & Francis

2013

Vol. 6, No. 1, 66–76, http://dx.doi.org/10.1080/17520843.2012.695385

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

inflation uncertainty to surprise agents and get output gains. Mirman (1971)postulates that an increase in income uncertainty would result in an increase inprecautionary savings, which in the Solow-Swan neoclassical model would result inhigher economic growth. Devereux (1989) hypothesizes that output volatility couldhave a positive effect on inflation. Since Bangladesh is a country noted for highgrowth and inflation, it is imperative to see which hypotheses conform to the case ofa developing nation like it.

In econometric models of changing conditional variance, the most widely usedare the family of autoregressive conditional heteroscedasticity (ARCH) or general-ized ARCH (GARCH) models, introduced by Engle (1982) and Bollerslev (1986),respectively. Nelson (1991), however, introduced the exponential GARCH(EGARCH) model that overcame the limitations of ARCH and GARCH models.For example, the feature that allows conditional variance to respond asymmetricallyto positive and negative residuals is embodied in the EGARCH model, but not in itspredecessors. Furthermore, the non-negativity constraints on the autoregressivecomponents in the variance equation can create difficulties in estimating GARCHmodels, whereas EGARCH models overcome these constraints. Nelson (1991) alsoargues that it is difficult to evaluate in GARCH models whether shocks to variance‘persist’ or not. In the EGARCH model, however, the logarithm of variance [ln(st

2)]is a linear process, and its stationarity and ergodicity are easily checked.

Literature on the inflation–growth interaction along with their volatility isconspicuously absent in Bangladesh. Working over the period 1976 to 2009, arelatively liberalized regime for Bangladesh, this study finds that both inflation andgrowth adversely affect each other in a lagged fashion. While a rise in the inflation ratebecomes detrimental to growth, a rise in the growth rate also reduces inflation in thefollowing years. Although growth volatility has no impact on inflation, inflation andgrowth volatility exert a positive effect on growth. Both inflation and output growthdisplay a high level of persistence in their respective autoregressive models. While adeviation of inflation reduces inflation uncertainty, a deviation of growth raises outputvolatility. Neither growth volatility nor inflation uncertainty displays any asymmetricbehaviour in response to either positive or negative signs of their respective shocks.

The remainder of this article comprises four sections. A brief literature review ispresented in Section 2. Section 3 describes the methodology of an EGARCH-Mmodel. Section 4 presents data and estimations. Section 5 concludes.

2. Literature review

Although any paper closely related to the present approach that that deals with theinflation–growth interaction embodying volatility is conspicuously absent inBangladesh, I present a number of papers that deals with inflation and output inthis country. Chowdhury et al. (1996) use quarterly data over the period 1974–1992and investigate the relationship between money, prices, output, and the exchangerate in Bangladesh. They use the vector autoregressive (VAR) model to derivevariance decomposition for all four variables. They argue that inflationary pressurein Bangladesh is not entirely caused by monetary factors. Their results, however,show no significant effect of output on inflation.

Working with annual data from 1974 to 1997, Mallik and Chowdhury (2001)examine the relationship between inflation and gross domestic product (GDP)growth for four South Asian countries: Bangladesh, India, Pakistan, and Sri Lanka.

67Macroeconomics and Finance in Emerging Market Economies

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

The authors find evidence of a long-run positive relationship between GDP growthrate and inflation for all four countries. There is also significant feedback betweeninflation and economic growth. Based on these results, the authors suggest thatmoderate inflation is helpful to growth, but faster economic growth feeds back intoinflation. The main limitation of this study is the sample size. Only 23 observationsof growth and inflation may not produce robust results for policymakers.

Working with annual data from 1980 to 2005, Ahmed and Mortaza (2005) find along-run negative relationship between inflation and economic growth forBangladesh. They find an annual rate of 6% as the threshold level of inflation,above which inflation adversely affects economic growth. The aspect of why theystart from 1980 is not clear in their paper. The authors’ threshold calculation wouldhave been different had they included data of the 1970s when most countries,including Bangladesh, suffered from high inflation. In the error correction model,they show that output growth inversely affects inflation. The aspect whether inflationaffects growth was not clear in their study.

Akhtaruzzaman (2005) works over the period 1973Q1–2002Q2 and identifies thevariables that are believed to generate inflation in Bangladesh. He finds that theexchange rate, money supply, and the deposit interest rate have statisticallysignificant roles in explaining the inflationary process of Bangladesh. He finds thatinflation is negatively related with real income.

Rahman (2005) works with quarterly data from 1974 Q1 to 2003 Q4. He findsthat real income growth positively affects inflation. The main message of his paper isthat the absence of pure monetary neutrality exists in Bangladesh. Covering theperiod from 1980 to 2008, Mujeri et al. (2009) find the application of the P-starmodel for measuring inflationary pressure in Bangladesh. They estimate inflation asa function of the output gap along with other factors. The output gap turns out to besignificant at the 10% level. This result, however, does not claim that the Phillipscurve exists by any means, nor do the authors pronounce so.

Islam and Uddin (2011) provide evidence that the monetary sector of Bangladeshhas gained a considerable degree of maturity and fulfils a number of prerequisites toadopt inflation targeting strategies over the period 1980–2010. The paper finds thatthe Central Bank of Bangladesh is neither targeting inflation nor following any otherrule-guided monetary policy. Nasir (2011) uses annual data from 1982 to 2005 andincorporates three new measures of institutional rigidities to estimate an inflationmodel for Bangladesh. He finds that a higher degree of institutional rigidities leads tohigher inflation rates in Bangladesh. Evidence also suggests that inflation is unlikelyto be a monetary phenomenon in Bangladesh.

Despite multiple studies on Bangladesh’s inflation, a comprehensive study toexamine the inflation–growth interaction along with their respective volatility is stillabsent. Moreover, the relationship between inflation and growth in Bangladeshappears to be inconclusive. Hence, further research is required. This work attemptsto fill that gap by deploying a bivariate EGARCH-M approach.

3. Empirical methodology of a bivariate EGARCH-M model

First I consider a GARCH model of inflation:

pt ¼ mþXmi¼1

bipt�i þ et; et � Nð0; htÞ ð1Þ

68 B.P. Paul

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

ht ¼ hþXri¼1

yiht�iþXsj¼1

lje2t�j ð2Þ

where pt is inflation, ht is its conditional variance, and et is the error term. Otherparameters are self-explanatory. The conditional variance of inflation estimated by thisGARCHmodel in Equation (2) is a function only of the magnitudes of inflation shocks(e2t-j), not also the signs of such shocks, and thus by construction is blind as to whetherinflation is rising or falling. Thus, the GARCH model does not yield a time-varyingmeasure of inflation uncertainty capable of providing a reliable assessment of theresponse of inflation uncertainty to inflation surprises (Wilson 2006). An EGARCHmodel overcomes this shortcoming. It explicitly allows its measure of uncertainty torespond to the positivity or negativity of shocks. Its time-varying volatility is capable ofresponding to the series mean; a relationship observed in economic time series.Replacing Equation (2) is now necessary to obtain an EGARCH model of inflation:

log ht ¼ hþXri¼1

yi log ht�i þXsj¼1

lj nt�j���

���� E nt�j���

���� �

þXp

k¼1fknt�k ð3Þ

where nt-j/k¼ et-j/k/(ht-j/k)0.5 is independent and identically distributed (i.i.d.) with

zero mean and unit variance. E calculates the expected value. This implies that theleverage effect is exponential and that forecasts of the conditional variance areguaranteed to be nonnegative. If lj4 0, a deviation of jnt-jj from its expected valuecauses inflation uncertainty (ht) to rise, and vice versa. Unlike the GARCH model,however, EGARCH allows this effect to be asymmetric. If fk4 0, ht will rise more inresponse to positive inflation shocks (et-k4 0) than to negative shocks, and viceversa. Developed by Engle et al. (1987), GARCH in mean (GARCH-M) allows theconditional variance of a series to feed back and influence the conditional mean.Since this study is interested in examining the relationship between inflation andgrowth along with the uncertainty of each variable, I specify a bivariate EGARCH-M model of inflation (pt) and growth (gt):

pt ¼ mp þXmi¼1

bipt�i þXnj¼1

cjgt�j þ gph0:5p;t þ jgh

0:5g;t þ ep;t; ð4aÞ

log hp;t ¼ hp þXri¼1

yp;i log hp;t�iþXsj¼1

lp;j np;t�j���

���� E np;t�j���

���n o

þXp

k¼1fp;knp;t�k; ð4bÞ

gt ¼ mg þXm0

i¼1bipt�i þ

Xn0

j¼1cjgt�j þ gph

0:5p;t þ jgh

0:5g;t þ eg;t; ð5aÞ

log hg;t ¼ hg þXr0

i¼1yg;i log hg;t�i þ

Xs0

j¼1lg;j ng;t�j

������� E ng;t�j

������

n oþXp0

k¼1fg;kng;t�k; ð5bÞ

where h0:5p;t is inflation uncertainty and h0:5g;t is growth uncertainty. Other notationsare the same as before. Inflation and the conditional variance of inflation, as shown

69Macroeconomics and Finance in Emerging Market Economies

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

in Equation (4a) and Equation (4b), are determined in an EGARCH-M model.Growth and its conditional variance, as shown in Equation (5b), are determinedsimultaneously in the same model. Simply, an EGARCH-M can determine theeffects of uncertainty on the mean rate of any variable, and it also determines thedynamics of uncertainty and asymmetry of volatility, if present. Thus, this model canbe a powerful tool to examine the inflation–growth interaction in any country.

4. Data issues and estimations

After its independence in 1971, Bangladesh pursued a socialist planning when pricecontrols were massive. The inflation dynamics of the country is likely to be restrictedduring that period. With the regime change in 1975, the country entered a transitionfrom socialist planning to the pro-market economy. Thus, Bangladesh began its firstphase of liberalization since 1976 (Ahmed and Sattar 2004). Hence, I select the yearof 1976 as the beginning of our sample to derive a fair interaction between inflationand growth in Bangladesh. The consumer price index (CPI) data for the period from1976 to 1985 are collected from the Bangladesh Bureau of Statistics (BBS 1980–1986). The International Financial Statistics (IFS) of the IMF (2011) provides theCPI for Bangladesh over the period from 1986 to 2009. The series of Bangladesh’sGDP at the 2000 constant dollar prices is collected from the World DevelopmentIndicators (WDI) of the World Bank (World Bank 2011). The logarithmic values ofthe series are used to conduct the unit root and cointegration tests.

Nelson and Plosser (1982) find that most macroeconomic variables arecharacterized by unit-root processes. The variables must be integrated of orderone, i.e. I(1), before they can be tested for cointegration. The augmented Dickey-Fuller (ADF) test is widely used in this regard (Dickey and Fuller 1979, 1981).Phillips and Perron (1988) proposed a modification of the Dickey-Fuller (DF) testand have developed a comprehensive theory of unit roots. The Phillips-Perron (PP)test has introduced a t-statistic on the unit-root coefficient in a DF regression,corrected for autocorrelation and heteroscedasticity. Formally, the power of a test isequal to the probability of rejecting a false null hypothesis. Monte Carlo simulationsshow that the power of the various DF tests can be very low (Enders 2010, 234).Maddala and Kim (1998, 107) comment that the DF test does not have serious sizedistortions, but it is less powerful than the PP test. Choi and Chung (1995) assert thatfor low frequency data like mine the PP test appears to be more powerful than theADF test. Accordingly, I adopt the PP methodology to test unit roots in thevariables.

As Table 1 shows, both the series are I(1). Now testing them for cointegration asper the Johansen approach (Johansen 1988; Johansen and Juselius 1990) becomesnecessary. While the Johansen method has five options, only Options 3 are 4 areapplicable in most macroeconomic series like GDP and the CPI. Because Options 1and 2 do not assume any deterministic trend and Option 5 assumes quadraticdeterministic trend in data. In contrast, Option 3 includes an intercept in thecointegrating equation (CE) and the test vector autoregression (VAR). Option 4includes an intercept and a trend in the CE without any trend in the VAR. The trace(lTrace) and maximum eigenvalue (lMax) tests, as shown in Table 2, are computed byallowing for linear deterministic trend in data. The decision on the lag length is madeby running the variables in an unrestricted VAR. The Schwartz Bayesian criterion(SBC) is adopted here to determine the lag length. Although there are other criteria

70 B.P. Paul

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

such as the Akaike information criterion (AIC), the modified AIC (MAIC), and thelikelihood ratio (LR), a study with limited number of observations, as is the casewith this work, should follow the SBC that ensures greater parsimony than others(see Enders 2010, 120). The SBC chooses the lag length to be one.

Both options in Table 2, however, deliver conflicting results: the maximumeigenvalue test complies with no cointegration, whereas the trace test signals two orone cointegrating relation among these variables. Cheung and Lai (1993)recommend following the trace statistic, but Johansen and Juselius (1990) suggestthat the maximum eigenvalue test gives better results. Enders (2010, 392) asserts thatwhen the results conflict, the maximum eigenvalue test is usually preferred for itsability to pin down the number of cointegrating vectors. Hence, I follow the lMax

test, and find no cointegrating relation between Bangladesh’s GDP and the CPI.Table 3 shows the regressions of inflation and growth in Bangladesh over the

period 1976–2009. Both regressions have been derived after some experimentation toensure the lowest value of the SBC. Nelson (1991) refers to Hannan (1980) and

Table 1. Phiilips-Perron unit root tests with the output series and price index of Bangladesh:1976–2009.

Variables: In levels In fisrt difference Integration

GDP 6.85 75.80 I(1)(1.00) (0.01)

CPI 72.86 73.58 I(1)(0.19) (0.05)

Note: Estimations include intercept and trend as long as they are significant. The null hypothesis statesthat the variable has a unit root. p-values are shown in the parentheses under each adjusted t-statistic. Thecritical values and details of the test are presented in Phiilips and Perron (1988). Source: BSS (1980–1986),IMF (2011), WB (2011).

Table 2. Johansen cointegration tests with the output series and price index of Bangladesh:1976–2009.

Option 3 Option 4

l Stat CV CE l Stat CV CE

ltrace tests:H0: r¼ 0 HA: r4 0 21.79 15.49 2 28.99 25.87 1H0: r� 1 HA: r4 1 10.15 3.84 11.28 12.52

lmaxtests:H0: r¼ 0 HA: r¼ 1 11.64 14.26 0 17.71 19.39 0H0: r¼ 1 HA: r¼ 2 10.15 3.84 11.28 12.52

Note: The ltrace and lmax are calculated as per Johansen (1995). CV signifies critical values calculated forthe 5 percent significance level. CE stands for cointegrating equation. H0 and HA denote the null andalternative hypotheses, respectively. Option 3 includes an intercept in the CE and the test VAR, where asOption 4 includes an intercept and a trend in the CE without any trend in the VAR. The lTrace and lMax

test statistics under both models are computed by allowing for linear deterministic trends in data. The laglength is determined by the SBC. r stands for the rank of the matrix, which denotes the number of the CEbetween the variables. Source: Same as in Table 1.

71Macroeconomics and Finance in Emerging Market Economies

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

argues that the SBC provides consistent-order estimation in linear autoregressivemodels. In addition, a battery of diagnostic tests, as shown in the lower part of thetable, ensures that both estimations are free of three things: 1) serial correlation 2)heteroscedasticity and 3) non-normal errors. Regression 1 estimates inflation in anEGARCH-M model. The coefficient on Inflation(t-1) shows an autoregressivepersistence of inflation by 34%. The coefficient on the lagged growth is high, -0.48,and significant at the 1% level. Thus, last year’s growth adversely affect this year’sinflation, possibly through the supply channel. A year with a higher growth rate maysignal lower inflation next year. Hence, pursuing higher growth may work as analternative strategy to curb inflation in Bangladesh. The effects of output and pricevolatility, however, are not significant on inflation in the country.

The conditional variance of inflation follows an oscillatory pattern, as shown bythe coefficient on the lagged value of conditional variance. The coefficient onjStandardized Errorj(t-1) is negative and highly significant, suggesting that anydeviation of standardized error from its expected value in the last year causesinflation variance to be smaller than otherwise this year. Last year’s deviation might

Table 3. Estimating inflation and output growth in Bangladesh in EGARCH-M models:1976–2009.

Regression No. ! 1 2LHS Variable. ! Inflation Growth

Regressors:Constant 0.066*** (0.001) 0.026*** (0.005)Inflation (t-1) 0.344*** (0.013) 70.156*** (0.017)Growth (t-1) 70.477*** (0.041) 0.293*** (0.077)Growth (t-2) 0.096* (0.052)Inflation Volatility (t) 70.187 (0.153) 0.403*** (0.065)Growth Volatility (t) 70.001 (0.116) 0.588** (0.262)

Log (GARCH)(t) Regressors:Constant 75.607*** (0.001) 711.096*** (3.241)Log (GARCH) (t-1) 70.011*** (0.001) 0.078 (0.362)jStdzd. Errorj (t-1) 72.894*** (0.112) 2.074*** (0.815)Stdzd. Error (t-1) 70.065 (0.322) 0.735 (0.467)

R-squared 0.21 0.28Log likelihood 77.17 106.57Schwartz Bayesian criterion 74.12 75.77Serial Correlation Test:Q-stat at lag 1 0.94 [0.33] 0.05 [0.82]Q-stat at lag 4 1.59 [0.81] 0.38 [0.98]Q-stat at lag 8 2.66 [0.95] 1.52 [0.99]

Heteroskedasticity Test:Q-stat at lag 1 0.64 [0.43] 0.28 [0.60]Q-stat at lag 4 2.42 [0.66] 1.48 [0.83]Q-stat at lag 8 7.06 [0.53] 4.08 [0.85]

Normality Test:Jarque-Bera stat 0.71 [0.70] 2.21 [0.33]

Note: Bold numbers are the coefficients of interest in this study. *, **, *** indicate that the coefficients aresignificant at the 10%, 5%, and 1% levels, respectively. ‘‘stat’’ stands for statistic. ‘‘Stdzd’’ meansstandardized. jj signifies absolute values. Null for serial correlation tests: No serial correlation Null forheteroskedasti city tests: No heteroskedasti city. Null for normality tests: No nonnormal errors inresiduals. Values in parentheses against cofficients are their standard errors, and values in bracket are p-values of the respective statistics. Source: Same as in Table 1.

72 B.P. Paul

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

have made the Central Bank conservative on money supply this year. Inflationuncertainty, however, does not show any asymmetry in response to either positivityor negativity of inflation surprises.

Regression 2 in the same table estimates growth in an EGARCH-M model, asbefore. As the coefficient on Growth (t-1) shows, almost 30% of the last year’sgrowth is reflected this year, implying a considerable amount of growth persistencein Bangladesh. The coefficient on Inflation(t-1) is -0.16, and is highly significant,suggesting an adverse effect of inflation on growth in the country. Examining theunderlying channel of this negative effect of inflation on growth goes beyond thescope of this paper. I, however, hypothesize that a high rate of inflation is likely todiscourage both consumption and investment, which account for the lion share ofoutput in Bangladesh. Thus, high inflation reduces output growth in a lagged fashionin the country.

While inflation is detrimental to growth in Bangladesh, inflation volatilityappears to be conducive to growth. The coefficient on inflation volatility, 0.40, ishighly significant. Growth volatility appears to be contributing to growth as well. Asthe equation of conditional variance in growth shows, any deviation of standardizederror from its expected value in the last year causes output variance to be larger thanotherwise this year. Growth uncertainty, however, does not show any asymmetry inresponse to either positivity or negativity of growth surprises.

The result that uncertainty in both inflation and growth appears to be conduciveto growth in Bangladesh requires further clarification. Although Friedman (1977)argues that inflation volatility is detrimental to growth, empirical evidence puts hishypothesis into question. While Ball (1992) and Fountas et al. (2002) support thenegative effect of inflation volatility on growth, Dotsey and Sarte (2000) oppose theFriedman hypothesis and suggest a rise in precautionary savings and further growth

Figure 1. Interacting practices of bourgeois leisure in the Bois de la Cambre in Brussels,1897. Despite what is being suggested by the artist, the relationship between cyclists and horseriders was fraught with tensions (Source: Duquenne, Xavier. Le Bois de la Cambre. Brussels,1989).

73Macroeconomics and Finance in Emerging Market Economies

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

in the face of higher inflation uncertainty. Expansionary monetary policies duringhigher inflation uncertainty, as argued in Cukierman and Meltzer (1986), also soundplausible in developing economies. Some authors argue that fiscal policy uncertainty,which is likely to be connected with inflation volatility, may promote growth byencouraging precautionary savings (Fountas and Karanasos 2007). Examining thesechannels between inflation uncertainty and growth, although intriguing, goes beyondthe scope of this work. In addition, the paucity of appropriate data has always beenan issue for a developing nation like Bangladesh.

While growth uncertainty may appear to be disturbing to output growth, there isa considerable number of papers that suggests the reverse. Jiranyakul and Opiela(2010) argue that the effect of uncertainty on macro variables often varies betweenthe developed and developing countries. The hypothesis of Mirman (1971), whichpostulates higher growth during higher income uncertainty, has subsequently beenevidenced by numerous studies. According to Black (1987), growth uncertaintyinduces investors to spend more in risky technologies, which are likely to accelerategrowth. Blackburn (1999) argues that business cycle volatility increases growth in theend. While Wilson (2006) does not get any significant result between growthuncertainty and output growth for Japan, Narayan et al. (2009) show that highoutput volatility increases economic growth in China. Hence, the positive result ofthe effect of growth uncertainty on growth, as found in both China and Bangladesh,is not surprising.

5. Conclusion

Economic growth and inflation are the two most discussed variables in an economy.As a result, the interaction between inflation and output growth has been one of thehighest experimented topics in macroeconomics. Bangladesh has managed almost5% annual growth or more for the last two decades. The country, being one of thefastest growing economies in South Asia, has displayed moderately high inflationand price volatility since independence in 1971. This scenario has raised a number ofquestions such as: 1) Is high inflation inimical to growth in Bangladesh? 2) Doesgrowth affect the country’s inflation? 3) Does inflation uncertainty reduce growth?No study in the past has addressed these questions together. As a result, a gap hasbeen created, and this study attempts to fill that gap.

This study examines the inflation–growth nexus for Bangladesh in a bivariateEGARCH-M model. Working over a relatively liberalized regime from 1976 to2009, this research finds that both growth and inflation adversely affect each other ina lagged fashion in Bangladesh. While a rise in inflation negatively affectsBangladesh’s growth, inflation uncertainty appears to be conducive to growth forthe country. Growth uncertainty, which is often thought to be harmful for growth,affects growth positively. While a deviation of inflation reduces inflation uncertainty,a deviation of growth raises output volatility. Thus, the Central Bank should shift itstarget from controlling inflation uncertainty to reducing a rise in inflation to ensurefaster growth in Bangladesh.

This work raises some additional questions such as: 1) What are the channels ofinflation’s negative effect on output in Bangladesh? 2) Why does inflation volatilityaffect growth in a positive direction in the country? 3) What is the underlyingmechanism that helps growth dampen inflation in Bangladesh? These questions arecertainly intriguing, and thus are left for future investigation.

74 B.P. Paul

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

Notes on contributor

Dr Biru Paksha Paul received his B.S.S. (Honors) and M.S.S., both in economics from theUniversity of Dhaka in 1986 and 1989, respectively. He earned his MBA in finance from theUniversity of Technology–Sydney in 1999. He received his MA in applied economics fromthe State University of New York at Binghamton in 2004. He received his PhD in economicsfrom the same university in 2007. His papers have been accepted in numerous journals such asthe Journal of Asian Economics, Journal of Quantitative Economics, Indian Economic Review,and Energy Economics.

References

Ahmed, S., and M.G. Mortaza. 2005. Inflation and Economic Growth in Bangladesh: 1981–2005. WP 0604, Policy Analysis Unit, Bangladesh Bank. www.bangladeshbank.org.bd/research/pau.html.

Ahmed, S., and Z. Sattar. 2004. Trade liberalization, growth and poverty reduction: The caseof Bangladesh. South Asia Discussion Paper No. IDP-190, World Bank.

Akhtaruzzaman, M. 2005. Inflation in the open economy: An application of the errorcorrection approach to the recent experience in Bangladesh. WP 0602, Policy AnalysisUnit, Bangladesh Bank. www.bangladesh-bank.org/openpdf.php.

Ball, L. 1992. Why does high inflation raise inflation uncertainty? Journal of MonetaryEconomics 29: 371–88.

BBS. 1980–1986. Monthly Statistical Bulletin, various issues. Bangladesh: Bangladesh Bureauof Statistics, Ministry of Planning.

Black, F. 1987. Business cycles and equilibrium. New York: Basil Blackwell.Blackburn, K. 1999. Can stabilization policy reduce long-run growth? Economic Journal 109:

67–77.Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of

Econometrics 31, no. 3: 307–27.Cheung, Y.W., and K.S. Lai. 1993. Finite sample sizes of Johansen’s likelihood ratio tests for

cointegration. Oxford Bulletin of Economics and Statistics 55: 313–28.Choi, I., and B. Chung. 1995. Sampling frequency and the power of tests for a unit root: A

simulation study. Economics Letters 49: 131–6.Chowdhury, A.R., M.Q. Dao, and N.M. Wahid. 1996. Monetary policy, output, and inflation

in Bangladesh: A dynamic analysis. In The economy of Bangladesh: Problems andprospects, ed. N.M. Wahid, and C.E. Weis, 107–18. London: Praeger.

Cukierman, A., and A. Meltzer. 1986. A theory of ambiguity, credibility, and inflation underdiscretion and asymmetric information. Econometrica 54: 1099–128.

Devereux, M. 1989. A positive theory of inflation and inflation variance. Economic Inquiry 27:105–6.

Dickey, D., and W.A. Fuller. 1979. Distribution of the estimates for autoregressive time serieswith a unit root. Journal of the American Statistical Association 74: 427–31.

Dickey, D., and W.A. Fuller. 1981. Likelihood ratio statistics for autoregressive time serieswith a unit root. Econometrica 49: 1057–72.

Dotsey, M., and P. Sarte. 2000. Inflation uncertainty and growth in a cash-in-advanceeconomy. Journal of Monetary Economics 45: 631–55.

Enders, W. 2010. Applied Econometric Time Series. 3edn. Hoboken, NJ: Wiley.Engle, R.F. 1982. Autoregressive conditional heteroskedasticity with estimates of the variance

of United Kingdom inflation. Econometrica 50, no. 4: 987–1007.Engle, R.F., D.M. Lilien, and R.P. Robins. 1987. Estimating time varying risk premia in the

term structure: The ARCH-M model. Econometrica 55: 391–407.Fountas, S., and M. Karansos. 2007. Inflation, output growth and nominal and real

uncertainty: Empirical evidence for the G7. Journal of International Money and Finance 26:229–50.

Fountas, S., M. Karanasos, and J. Kim. 2002. Inflation and output growth uncertainty andtheir relationship with inflation and output growth. Economics Letters 75: 293–301.

Friedman, M. 1977. Nobel lecture: Inflation and unemployment. Journal of Political Economy85: 451–72.

75Macroeconomics and Finance in Emerging Market Economies

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014

Hannan, E.J. 1980. The estimation of the order of an ARMA process. The Annals of Statistics8: 1071–81.

Holland, S.A. 1995. Inflation and uncertainty: Tests for temporal ordering. Journal of Money,Credit and Banking 27: 827–37.

IMF. 2011. International Financial Statistics (IFS). CD ROM. Washington, DC: InternationalMonetary Fund.

Islam, M.S., and M.T. Uddin. 2011. Inflation targeting as the monetary policy framework:Bangladesh perspective. Economia Seria Management 14, no. 1: 106–19.

Jiranyakul, K., and T.P. Opiela. 2010. Inflation and inflation uncertainty in the ASEAN-5economies. Journal of Asian Economics 21: 105–12.

Johansen, S. 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamicsand Control 12: 231–54.

Johansen, S., and K. Juselius. 1990. Maximum likelihood estimation and inference on coin-tegration with application to the demand for money. Oxford Bulletin of Economics andStatistics 52: 169–209.

Maddala, G.S., and I.M. Kim. 1998. Unit roots, cointegration, and structural change.Cambridge: Cambridge University Press.

Mallik, G., and A. Chowdhury. 2001. Inflation and economic growth: Evidence from SouthAsian countries. Asian Pacific Development Journal 8: 123–35.

Mirman, L. 1971. Uncertainty and optimal consumption decisions. Econometrica 39: 179–85.Mujeri, M.K., M. Shahiduzzaman, and M.E. Islam. 2009. Application of the P-Star model for

measuring inflationary pressure in Bangladesh. Bangladesh Development Studies 32, no. 1:1–22.

Narayan, P.K., S. Narayan, and R. Smyth. 2009. Understanding the inflation–output nexus inChina. China Economic Review 20: 82–90.

Nasir, A.B.M. 2011. Nexus between inflation rates and institutional rigidities in Bangladesh:An overlooked aspect for the cause of inflation. The Journal of Developing Areas 45: 339–58.

Nelson, C., and C. Plosser. 1982. Trends and random walks in macroeconomic time series:Some evidence and implications. Journal of Monetary Economics 10: 130–62.

Nelson, D.B. 1991. Conditional heteroskedasticity in asset returns: A new approach.Econometrica 59, no. 2: 347–70.

Phillips, P.C., and P. Perron. 1988. Testing for a unit root in time series regression. Biometrika75, no. 2: 335–46.

Rahman, Md.H. 2005. Money growth and inflation: A case study of Bangladesh. BangladeshJournal of Political Economy 22, no. 1–2: 469–85.

Wilson, B.K. 2006. The links between inflation, inflation uncertainty and output growth: Newtime series evidence from Japan. Journal of Macroeconomics 28: 609–20.

World Bank. 2011. World Development Indicators 2011. CD-ROM. Washington, DC: WorldBank.

76 B.P. Paul

Dow

nloa

ded

by [

McM

aste

r U

nive

rsity

] at

10:

53 2

1 D

ecem

ber

2014