The Relationship between Inflation and Inflation ...

International Research Journal of Finance and Economics ISSN 1450-2887 Issue 66 (2011) © EuroJournals Publishing, Inc. 2011 http://www.eurojournals.com/finance.htm

The Relationship between Inflation and Inflation Uncertainty: A Case Study for Saarc Region Countries Anum Asghar Graduate Student, Department of Economics University of Gujrat E-mail: [email protected] Khalil Ahmad Corresponding Author, Assistant Professor, Department of Economics University of the Punjab, Lahore E-mail: [email protected] Sami Ullah Lecturer, Department of Economics University of Gujrat E-mail: [email protected] Bedi-uz-Zaman Assistant Professor, Department of Economics University of Sargodha E-mail: [email protected] Muhammad Tahir Rashid Ph. D Scholar at G C. University Lahore E-mail: [email protected] Abstract Inflation uncertainty is the major cause of inflation that can manipulate the decision making of economic agents. This study examines the link between inflation and inflation uncertainty in SAARC region countries (Pakistan, India and Sri Lanka) over the period 1980Q1-2009Q4. Inflation uncertainty is modeled as a time varying process by employing the EGARCH framework. In the second stage, the asymmetric behavior of inflation uncertainty is also analyzed through this method. Finally, bi-variate Granger-Causality test is applied to investigate the causality between inflation and inflation uncertainty. The results obtained from the study demonstrated that positive shocks to inflation create more uncertainty in Pakistan, India and Sri Lanka. The results of Granger- causality test proved the existence of bi-directional causality between inflation and inflation uncertainty in SAARC region countries.

Keywords: Inflation, Inflation Uncertainty, EGARCH, Granger Causality, SAARC JEL Classification Codes: E31, C22, E37

International Research Journal of Finance and Economics - Issue 66 (2011)

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1. Introduction Inflation, at different rates and in different time periods, has been a problem of every economy. No doubt inflation is a global phenomenon. In the present era, it is difficult to find any country in the world today which is not afflicted by the specter of inflation. For this reason there have been conducted a very large number of theoretical and empirical studies. Further, the phenomenon of inflation has widely attracted the attention of the economists. Inflation is defined today as an increase in prices (Seigl, 2009). While inflation uncertainty refers to a situation in which future prices are unpredictable and general public does not know whether inflation will increase or decrease in the future. In simple words future inflation rate is fickle to public. Inflation uncertainty is both the cause and result of inflation. “Inflation uncertainty is considered one of the major costs of inflation since it not only distorts the decisions regarding future saving and investment due to lower predictability of the real value of future nominal payments, but it also extends the adverse affects of these distortions to the efficiency of resource allocation and the level of real activity” (Rizvi and Naqvi, 2008, p. 2). So without uncertainty economic agents can better plan for the future. Now the question arises how inflation uncertainty interrelates with the economy? Whenever expected inflation is a factor in an economic decision, uncertainty about inflation is also likely to be a factor (Golob, 1994). However, the issue of liaison between inflation and inflation uncertainty is still debatable and there exist different views about the nature of their relation in economic literature. Friedman (1977) and Ball (1990) provide evidence in support of the Friedman hypothesis that inflation hits inflation uncertainty. While Cukierman and Mezler (1986), and Perry and Grier (1998) for Japan and France provide evidence, contrary to the above and in support of the causality running from inflation uncertainty to inflation. Although it is a fact that high level of inflation is harmful for all kinds of economies but its harms are the worst for developing nations. The existence of poverty, illiteracy, unemployment and weak financial sectors make the structure of less developed economies very different from the developed ones. So, the presence of all these factors along with the phantom of inflation makes monetary policy more challenging for developing nations. Due to this reason, this paper has investigated the relationship between inflation and inflation uncertainty in South Asian Association for Regional Cooperation (SAARC) countries. SAARC is an economic and Political Organization which consists of eight South Asian countries: Pakistan, Nepal, Sri Lanka, India, Bhutan, Afghanistan, Maldives and Bangladesh. It was established on December 8, 1985. The South Asia is relatively poor and less developed region in the world. This region faces the problems of inflation, poverty, unemployment and income inequalities. So the main objectives of SAARC include acceleration of economic growth, social progress and cultural development and promotion of active collaboration and mutual assistance in the social, economic, technical and scientific fields. The main purpose of this study is to make comparative analysis about the situation of inflation and inflation uncertainty in Pakistan, Sri Lanka and India1. Because all these are developing countries and almost have same economic problems, such as overpopulation, illiteracy, inflation, poverty and unemployment, therefore we have selected these for testing the relation between inflation and inflation uncertainty by examining SAARC region countries. The rest of the paper proceeds as following: Section 2 discusses the literature review. An overview of the rationale underlying different hypotheses regarding the relation between inflation and inflation uncertainty is discussed in Section 3. Data and methodological issues are discussed in section 4. Section 5 contains econometric methodology and estimation of results. Finally, Section 6 summarizes findings and offers some implications for policy.

1

This study confined its analysis only for these three countries of SAARC due to non-availability of data about rest of the member countries.

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2. Literature Review The relationship between inflation and inflation uncertainty in Turkey was examined by Nas and Perry (2000) using GARCH (1, 1) models. The data used in this study included monthly inflation from January 1960 to March 1998. Results showed that inflation raised inflation uncertainty in Turkey while, the impact of inflation uncertainty on inflation depends on the time period examined. Bhar and Hamori (2001) used Markov Switching Heteroskedasticity model to analyze the interaction between inflation and its uncertainty over the short and long run period in G7 countries: Canada, France, Germany, Italy, Japan, UK and USA. For this purpose they used the data on the quarterly price level from 1961Q1-1999Q4. The results showed that high inflation increased its uncertainty in the future. Kontonikas (2004) considered the effect of inflation targeting on average inflation and inflation uncertainty with the help of GARCH models by using monthly and Quarterly data of UK CPI from 1972-2002. The results showed that there existed a positive relationship between inflation and inflation uncertainty. Hamilton’s (2001) flexible regression model had been used in the study of Chan and Xie (2003) to find the link between inflation and inflation uncertainty in Taiwan. The data on monthly CPI was used from 1980:01 to 2002:12. The results supported Friedman hypothesis that inflation rate affected inflation uncertainty. It is important to note that the relation was only nonlinear. Bredin and Fountas (2006) used a Markov regime-switching Heteroskedasticity model to examine the relationship between inflation and inflation uncertainty in four European countries (Germany, Italy, UK, and Holland). Quarterly data on GDP deflator as a proxy for the price level had been used from 1968-2005. The results showed that there existed affirmative relationship between inflation and inflation uncertainty. Thornton (2006) employed a GARCH model and found a positive and significant relationship between the level and variability of monthly inflation in India during the period 1957-2005. The causation was running from inflation to inflation uncertainty, as theorized by Friedman. It is well recognized that inflation uncertainty negatively affects output. So, this makes stronger the case for the central bank to focus on price stability as one of the prime objectives of monetary policy. Inflation uncertainty in Pakistan had been measured by Rizvi and Naqvi (2008) using quarterly data of CPI from 1976:01 to 2008:02 with T-GARCH and EGARCH methodology. This study was the first effort to measuring and analyzing inflation uncertainty in Pakistan. Findings showed that inflation hit inflation uncertainty in Pakistan. Nazar et al (2010) used a time series of inflation uncertainty in Iran from 1959-2009 and examined the affiliation between inflation and inflation uncertainty through EGARCH framework. The results indicated that there was an asymmetric liaison between inflation and inflation uncertainty. The positive shocks to inflation had a greater effect on inflation uncertainty as compared with negative effects. Granger Causality test also verified that inflation Granger-caused inflation uncertainty. Coporale et al (2009) estimated a time varying AR-GARCH model of inflation producing measure of inflation uncertainty for the Euro area, and investigated the link between them in a VAR framework. For this purpose monthly Harmonized Index of Consumer Prices had been used from 1980-2009. The findings showed that in the Euro period the Friedman-Ball hypothesis was empirically supported. Moradi (2008) explored the correlation between inflation and inflation uncertainty using the Iranian data of monthly CPI over the period of 1959-2008. GARCH models had been used to examine this relationship along with Granger Causality test. TGARCH models had also been considered to investigate asymmetry in one conditional variance of inflation. The findings showed that increased inflation raises inflation uncertainty. Using the standard TGARCH models the presence of asymmetry was found in the conditional variance of annualized inflation. Fountas and Karanasos (2000) contributed to the literature on the inflation and inflation uncertainty by using a GARCH model that allows for simultaneous feedback from one conditional mean variance of inflation. The data used in this study consisted of US CPI covering the period from


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1960M1-1999M2. Findings showed that there was a strong positive bi-directional link between inflation and inflation uncertainty. Fountas and Karanasos (2003) considered six European countries (France, Germany, Italy, Spain and Netherlands) to investigate the relationship between inflation and inflation uncertainty from the period 1960-1999 with EGARCH technique. They concluded that, except in Germany, inflation raised inflation uncertainty in all other countries. In Germany and Netherlands increased inflation uncertainty lowered inflation. And in Italy, Spain and France increased inflation uncertainty raised inflation. Payne (2007) extended the literature on the relationship between inflation and inflation uncertainty by examining three Caribbean countries (Bahamas, Barbados and Jamaica). This study employed ARMA-GARCH models using monthly data based on CPI. The empirical findings showed that inflation caused an increase in inflation uncertainty in all three countries. However, in Jamaica there existed a bi-directional relationship between these two variables. Thornton (2007) used monthly data on CPI for 12 emerging market economies (Colombia, India, Malaysia, Mexico, South Africa, Thailand, Turkey, Indonesia, Korea, Israel, Hungary and Jordan) with varying sample periods. The relationship between inflation and inflation uncertainty was checked by GARCH model and Granger Causality test. The results suggested that higher inflation raised inflation uncertainty in all economies. The evidence on the effect of inflation uncertainty on inflation was mixed. In Colombia, Israel, Mexico and Turkey increased inflation uncertainty lowered average inflation. While in Hungary, Indonesia and Korea the results were opposite. Ajevskis (2007) tested causality between inflation and inflation uncertainty in Latvia. CPI monthly growth from 1994-2007 was used as a measure of inflation. Conditional variance of ARMA model’s forecast error was used to measure inflation uncertainty. The application of the GARCH-M model in GARCH equation proved that there existed a bi-directional relationship between inflation and inflation uncertainty. The results suggested that Latvia’s data supported the Friedman-Ball and Cukierman-Meltzer theories. Rizvi and Naqvi (2009) used asymmetric GARCH specifications to find the relationship between inflation and inflation uncertainty in ten Asian countries: China, Hong Kong, India, Malaysia, Pakistan, Philippines, Singapore, South Korea, Indonesia and Thailand. The data set composed of quarterly CPI from 1987-2008. Their results showed that in Pakistan, India, Indonesia and Thailand inflation causes inflation uncertainty while there was bi-directional causality between inflation and inflation uncertainty in other countries. Most of the studies reviewed so far, have used GARCH models to find out the affiliation between inflation and inflation uncertainty and accept the hypothesis of Friedman (1977) who argued that increase in inflation ultimately increases its uncertainty. The results regarding the question whether inflation uncertainty causes average inflation to increase or decrease are mixed and depend on the economic situation of countries and time period tested.

3. Mechanism of Inflation and Inflation Uncertainty The question of relationship between inflation and inflation uncertainty was first brought by Friedman (1977). He set out an informal two-part argument about the real effects of inflation. In the first part, an increase in inflation may induce an erratic policy response by monetary authorities and therefore, lead to more uncertainty about the future rate of inflation. He described the channel of inflation and inflation uncertainty in this way:

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International Research Journal of Finance and Economics - Issue 66 (2011) Figure 1: Friedman Hypothesis

So uncertainty about Future Inflation will rise.

Policymakers will be reluctant to disinflate b/c of the recessionary effects of contractionary monetary policy.

When Inflation is high.

Future monetary Policy will be unpredictable for public.

Political Pressure to reduce it.

Now the question arises why there should be the political pressure to reduce inflation. Because elected officials like to give gifts to voters in order to carry favors and to create votes for themselves. So to get the trust of general public price stability is preferred by them. While policymakers are reluctant to reduce inflation as they are afraid from the recessionary effects of contractionary monetary policy. In this way inflation creates uncertainty in the future because monetary policy becomes unpredictable for general public. While in the second part, Friedman argued that this inflation uncertainty negatively affects the output level of the economy. Inflation Uncertainty ↑ Economic growth ↓ There are several underlying principles which explain the mechanism of uncertainty through which growth rate of a country is affected2. Second mechanism was given by Pourgerami and Maskus (1987) as well as by Ungar and Zilberfarb (1993). They argued that an increase in inflation causes a decrease in inflation uncertainty. Figure 2: Pourgerami and Maskus Hypothesis When Inflation increases

Agents expend more resources in forecasting inflation So Inflation Uncertainty will decrease

This hypothesis of Pourgerami and Maskus (1987) demonstrates that there exists a negative relationship between inflation and inflation uncertainty. When inflation increases economic agents 2

One channel through which this may occur can be investment. Higher unemployment uncertainty, or output uncertainty, implies greater uncertainty about the future marginal product of capital (Dixit and Pindyck 1994).


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spend more resources in forecasting future inflation because they have to do different decisions regarding investment, consumption, production etc. So in this way inflation can be anticipated to some extent and this will decrease the inflation uncertainty in the future. While this relationship was also considered in reverse direction by Cukierman and Meltzer (1986), they argued that inflation uncertainty causes average inflation to increase. Figure 3, given below explains their hypothesis. Figure 3: Cukierman and Meltzer Hypothesis

When uncertainty about inflation increases

Incentive for policy makers to create inflation surprises to stimulate growth

Cukierman and Meltzer suggested that causality runs from inflation uncertainty to inflation. Because when uncertainty about inflation increases there exists an inducement for policy makers to create inflation surprises to achieve sustainable economic growth. Holland (1995) proposed stabilization hypothesis that an increase in inflation uncertainty causes a decrease in inflation. The channel behind this hypothesis is as: Figure 4: Holland Hypothesis

So, Holland argued that more uncertainty can lead to a lower average inflation rate if the central bank attempts to minimize the welfare losses arising from more inflation uncertainty. Thus it contradicts with Cukierman and Meltzer (1986) hypothesis. Inflation can also increase inflation uncertainty through the following channel:

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International Research Journal of Finance and Economics - Issue 66 (2011) Figure 5: Fiscal Policies and Uncertainty

Because if reducing inflation causes an increase in the rate of unemployment, a high rate of inflation would produce greater uncertainty about the future direction of government policy and therefore future rates of inflation. These are some of the possible channels through which the relationship between inflation and inflation uncertainty can be determined. The results of this study will determine which of the above hypothesis holds true for the case of SAARC region countries.

4. Data and Methodology The variables used in this study are inflation and inflation uncertainty. Pakistan and Sri Lanka use Consumer Price Index (CPI) for measuring inflation while India uses Wholesale Price Index (WPI) for this purpose3. The data of CPI and WPI on Quarterly basis has been utilized in this study from 1980Q01-2009Q044. The inflation data are taken from International Financial Statistics (IFS) and computed as π t = [ ( Pt − Pt −1 ) / Pt −1 ] * 100 Where π t represents inflation rate, Pt stands for current prices, while Pt −1 corresponds to previous period prices. After computing inflation rate with the help of above formula this study has applied some descriptive statistics to the inflation series of all the countries. These descriptive statistics include Mean, Median, Standard deviation, Skewness, Kurtosis and Jarque-Bera test. Descriptive statistics are mostly used to portray the basic features of the data in a study. They provide simple summaries about the sample and the measures and on the basis of these statistics, different techniques are applied in this study for data analysis and for modeling purposes.

3 4

Graphical representation of inflation and inflation uncertainty for these countries are given in the Appendix. Quarterly and even lower frequency data are more appropriate from the point of view of the monetary authorities, due to the long lags in the implementation of monetary policy.


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This paper also adds a test of heteroskedasticity, (The Modified Levene Test) which is proposed by Brown and Forsythe (1974). It shows whether the variances of data differ across different time period or not5. The analysis begins with an examination of the time series properties of inflation using the Augmented Dickey Fuller (ADF) unit root test. ADF is the wider version of Dickey Fuller (DF) test. DF is only valid for AR (1) process due to this drawback Dickey and Fuller (1979) introduced ADF test which includes extra lagged terms of the dependent variable in order to eliminate autocorrelation. The ADF unit root test is based on the null hypothesis that the time series are difference stationary. This test can be conducted by the following three equations: Without intercept and trend ∆π = θ π + ∑ d ∆π + ε With intercept ρ ρ

t

t −1

t−i

i

t

i=1

∆ π t = c1 + θ π t −1 +

∑d ∆π i

+ εt

t −i

i =1

With intercept and trend

ρ

∆ π t = c1 + θ π t − 1 + c 2 t +

∑d

i

∆ π t −i + ε t

i =1

4.1. Detection of Heteroskedasticity in Inflation In this section, Autoregressive Conditional Hetroscedastic (ARCH) model and its extension as Generalized Autoregressive Conditional Hetroscedastic (GARCH) model are used to detect the ARCH/GARCH effects in these SAARC countries. These models explain the trend in inflation with the passage of time. ARCH models are specially designed to model and forecast conditional variance. The first ARCH model was presented by Engle (1982). The model suggests that the variance of the residuals at time t depends on the squared error terms from past periods. So the ARCH (p) process will be as: Mean equation p

πt =µ +

∑θ

j

π t− j + ε t

j =1

ε t ~ iid N (0, ht ) Where π t represents rate of inflation. This is simply an AR (p) process. Variance equation q

h t = ω 0 + ∑ α j ε t2− j j =1

Here ht represents variance which is a deterministic function of historical returns. The GARCH model was developed independently by Bollerslev (1986). The GARCH model allows the conditional variance to be dependent upon previous own lags and the squared residual terms lags. In this case the GARCH (p, q) model will beq as: p ht = ω 0 +

∑β i =1

i

ht − i +

∑α

j

ε t2−

j

j =1

4.2. Inflation Uncertainty Framework To model inflation uncertainty this study has used Exponential Generalized Autoregressive Conditional Hetroskedastic (EGARCH) model of Nelson (1991) because it does not impose the nonnegativity constraints on the parameters by modeling the logarithm of the conditional variance as compared to conventional GARCH models6. The EGARCH model also allows for testing of asymmetries in terms of negative and positive shocks. The variance equation for this model is as: 5 6

This paper only includes its features of grouping data in different time periods, not properly tested. Alternative measures of uncertainty that were used in the past included survey-based forecasts and a moving standard deviation of inflation.

93


ln ( ht ) = ω0 + Σip=1β i ln(ht −i ) + Σ qj =1α j |

εt− j ht − j

ε t− j

q

| + ∑γ j =1

j

ht − j

Where ω , β , α and γ are parameters to be estimated. When γ is non-zero, the impact of inflation on inflation uncertainty is asymmetric, when γ is positive an increase in inflation causes more inflation uncertainty and vice versa. 4.3. Granger Causality Test Next Granger Causality has been used to test the existence of causality between Inflation ( INFt ) and Inflation uncertainty ( INFUNCt ). The Granger causality test determines whether one variable is useful in forecasting the other or not (Granger, 1969). The bi-variate regression function is as follows: n n (i) IN F t = c 0 + ∑ IN F t − i + ∑ IN F U N C t − i + ε t (i ) i =1

i =1

n

IN F U N C t = c 0 +

∑ IN F U N C i =1

n t−i

+

∑ IN F

t−i

+ µt

(ii)

( ii )

i =1

Where c0 denotes the constant term in the Granger regression, n represents the lag length chosen for the causality analysis. The null hypothesis in equation (i) is that inflation uncertainty does not granger cause inflation. Likewise the null hypothesis in equation (ii) is that inflation does not granger cause inflation uncertainty.

5. Results and Discussion Descriptive statistics for inflation series are shown in Table 1 for the respective countries. Sri Lanka has the highest mean, median and variability in inflation rate as measured by the standard deviation. The distribution of inflation for Pakistan and India is positively skewed with leptokurtosis as the values of skewness are far away from zero and kurtosis is also above 3. Further, the Jarque- Bera test also rejects the null hypothesis of normal distribution for these countries. While Sri Lanka almost contains normal distribution of inflation rate as the value of Jarque-Bera statistic is very close to zero. Table 1:

Summary Statistics for Inflation (Monthly Data): 1980Q1-2009Q4

Statistic Pakistan India Mean 2.022967 1.685602 Median 1.779124 1.554615 Maximum 7.943973 8.253767 Minimum -0.652734 -2.743670 Std. Dev. 1.512013 1.704047 Skewness 1.292241 0.402666 Kurtosis 5.823123 4.377493 Jarque-Bera 72.63743 12.62415 Probability 0.000000 0.001814 Source: Calculated by the authors based on data from International Financial Statistics (IFS)

Sri Lanka 2.685262 2.602393 8.045430 -3.726554 2.150766 -0.014494 3.361772 0.653108 0.721405

Table 2 shows the results of descriptive statistics after applying Modified Levene Test. To check this test the series of inflation have been taken on annual basis. The results show that the distribution of inflation for all countries except Pakistan is normally distributed. Whereas Pakistan and Sri Lanka depict greater volatility in inflation rates as measured by standard deviation. The results of Augmented Dicky Fuller test (ADF) for inflation are given in Table 3. The ADF is based on the null hypothesis that series is non-stationary. The result shows that the series of inflation for all above countries is stationary at first difference at 1% significance level. Because at level all values of test statistics are less than critical values which correspond to the acceptance of null hypothesis of non-stationarity

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International Research Journal of Finance and Economics - Issue 66 (2011) Table 2:

Summary Statistics for Inflation Annual Data): 1980-2009

Statistic Pakistan India Mean 7.973781 6.686220 Median 8.303732 6.583087 Maximum 22.47650 13.26580 Minimum 2.060394 2.319371 Std. Dev. 4.139636 2.764897 Skewness 1.249034 0.453893 Kurtosis 6.003074 2.712189 Jarque-Bera 19.07350 1.133637 Probability 0.000072 0.567328 Source: Calculated by the authors based on data from International Financial Statistics (IFS)

Table 3:

Sri Lanka 10.65154 10.32967 18.99689 0.047157 4.914195 -0.143309 2.342049 0.643812 0.724766

Unit Root Test for Inflation

Level First Difference Country Test Statistic Critical Values Test Statistic Critical Values Pakistan* -0.556965 -2.585773 -7.367742 -3.490210 Sri Lanka* -2.184483 -2.585226 -10.70474 -2.585226 India -1.950586 -3.491928 -6.641815 -3.491928 * Stationarity is checked without intercept and trend, whereas for India it is tested with intercept and time trend.

5.1. Implementation of ARCH/GARCH Models The descriptive statistics presented above indicate that it is appropriate to use ARCH models to describe the volatility process. Following tables will explore conditional mean and variance equations of inflation for SAARC region countries. ARCH/GARCH models are ranked through minimized Akaike information (AIC) and Schwartz Bayesian criterion (SBC). The minimum value of AIC and SBC in the model suggests the significance of model in explaining heteroskedasticity. All types of ARCH models are applied in Eviews, one by one, to obtain the best fitted model under the criteria of AIC and SBC. But for simplicity only preeminent model is given in tables. The empirical results are presented in separate tables for each country along with estimated coefficients, p-values and ARCH/GARCH models based on minimum AIC and SBIC values. Used tests tell the order of autoregressive order of inflation series in mean equation as AR (1) process. The Inflation uncertainty for these countries is modeled through EGARCH (1, 1) because it allows for the testing of asymmetries in terms of negative and positive shocks. 5.1.1. Pakistan Table 4 presents the results of ARCH/GARCH effects for Pakistan. Ranking by AIC and SBC favors the ARCH (1) model over other specifications. The positive and significant value of α in ARCH (1) Table 4 shows that ARCH effects are present in the residuals of inflation series. Through EGARCH (1, 1) inflation uncertainty has been modeled. The value of γ = 0.339262 in EGARCH corresponds to the presence of asymmetric information because it is non zero. The positive and significant value of γ shows that positive shocks to inflation create more inflation uncertainty in Pakistan. Table 4:

Estimated Coefficients of Inflation for Pakistan

Country Pakistan Variable C INF(-1) Variance Equation ω α β γ AIC SBC

ARCH (1)

EGARCH (1 , 1) Mean Equation

Coefficient 0.859670 0.481917

p-value 0.0000 0.0000

Coefficient 0.944577 0.494660

p-value 0.0000 0.0000

0.900714 0.634961

0.0000 0.0016

-0.027468 0.156722 0.700743 0.339262

0.8831 0.5128 0.0000 0.0069

3.389905 3.483827

3.349174 3.490056

95 Table 5: Country India C INF(-1) ω α β γ AIC SBC

International Research Journal of Finance and Economics - Issue 66 (2011) Estimated Coefficients of Inflation for India GARCH (1 , 1) Coefficient p-value Mean Equation 1.308974 0.0000 0.144881 0.1314 Variance Equation 0.210503 0.1507 0.086416 0.1966 0.809854 0.0000 3.783762 3.901164

EGARCH (1 , 1) Coefficient p-value 1.392883 0.127974

0.0000 0.2105

-0.060080 0.182922 0.870191 0.212557 3.761907 3.902789

0.6107 0.2038 0.0000 0.0575

5.1.2. India The results for India on the basis of minimum AIC and SBC suggest that GARCH (1, 1) effects are present in the inflation series because the value of β is positive and significant. The value of γ = 0.212557 in EGARCH model indicates the presence of asymmetric information because it is non zero. The positive and significant value of γ shows that positive shocks to inflation produce more inflation uncertainty in India. 5.1.3. Sri Lanka Table 6 below illustrates the mean and conditional variance equations for Sri Lanka. AIC and SBC supports GARCH model over other models. The results of GARCH suggest that there exist GARCH effects in inflation series. A minute view reveals that the coefficients of α + β are almost equal to 1 which implies that shocks are persistent. The value of γ = 0.185261 in EGARCH model indicates the presence of asymmetric information. The positive and significant value of γ shows that positive shocks to inflation create more inflation uncertainty in Sri Lanka. Table 6: Country Sri Lanka

Estimated Coefficients of Inflation for Sri Lanka GARCH (1 , 1) Coefficient

C INF(-1)

1.881590 0.29157

ω α β γ AIC SBC

0.135449 -0.095952 1.072000

p-value Mean Equation 0.0000 0.0000 Variance Equation 0.1539 0.0126 0.0000 4.153689 4.271091

EGARCH (1 , 1) Coefficient p-value 1.895116 0.323761

0.0000 0.0010

0.041802 - 0.016892 0.986555 0.185261

0.7829 0.9092 0.0000 0.0322 4.239287 4.380169

5.2. Granger Causality Test The Granger causality test determines whether one variable is helpful in forecasting another or not. This study uses GARCH variances of inflation series as a proxy of inflation uncertainty and checked the causality between them through this test. The p-value less than 0.05 in Granger Causality test correspond to the rejection of null hypothesis at 5% significance level. The results in Table 7 suggest that in all three SAARC Countries (Pakistan, India and Sri Lanka) p-value is less than 0.05 at 5% significance level in both hypotheses. So, the findings propose that there exist bi-directional relationship between inflation and inflation uncertainty in all these countries.

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International Research Journal of Finance and Economics - Issue 66 (2011) Table 7:

Granger Causality Test

Null Hypothesis Pakistan INFUNC does not Granger Cause INF INF does not Granger Cause INFUNC India INFUNC does not Granger Cause INF INF does not Granger Cause INFUNC Sri Lanka INFUNC does not Granger Cause INF INF does not Granger Cause INFUNC

F-Statistics

Probability

4.86287 181.759

0.0095 1.E-35

4.58415 136.455

0.0122 1.E-30

4.31303 1633.63

0.0157 5.E-83

The results for all these three countries are consistent with the studies of Friedman (1977), Ball (1990), Golob (1994), Nas and Perry (2000), Fountas and Karansos (2003), Perry and Grier (1998), Payne (2007), Ajveskis (2007) and Rizvi and Naqvi (2009). These studies provide evidence in support of the Friedman (1977) and Cukierman and Meltzer (1986) hypotheses that, on one side, inflation hits inflation uncertainty and on other, it is affected by the later.

6. Conclusion and Policy Recommendations The rate of inflation is an important macroeconomic indicator and one of the key variables that most central banks around the world keep in view while setting their main policy variables. Inflation uncertainty is the major cost of high inflation that can influence the decision making of economic agents, because it negatively affects economic variables such as growth, consumption, investment etc. Due to all these awful effects of inflation uncertainty this study tests the link between inflation and inflation uncertainty in SAARC region countries. The variables used in this study are inflation and inflation uncertainty. Inflation uncertainty is modeled through EGARCH for each country and then Granger causality test is used to test the causality between inflation and inflation uncertainty. The results suggest that inflation is stationary, for each country, at first difference at 1% significance level. ARCH effects are present in Pakistan, while GARCH effects are there in India and Sri Lanka on the basis of minimized Akaike information (AIC) and Schwartz Bayesian criterion. Through EGARCH, asymmetry has also been found in inflation series. The results propose that in all the three countries; Pakistan, India and Sri Lanka positive shocks to inflation cause more uncertainty about future inflation. Lastly, application of granger causality test suggests that there exists a bi-directional causality between inflation and inflation uncertainty in all these countries at 5% significance level. So the result provides support for both Friedman (1977) and Cukierman and Meltzer (1986) hypotheses. The major challenge faced by the SAARC countries in recent years mainly centered around fighting inflation. The policy implication for these high inflation countries is to aim low average inflation rate in order to reduce the negative consequences of inflation uncertainty. This may be achieved through the following: • Developing regional strategies against inflation with the help of economic policy makers of SAARC. These countries can better control negative effects of inflation in their economies and can enhance economic development. • Forming a Monetary Union of SAARC countries, can be a solution to inflation management. This will not only ensure price stability through the reduction of Central bank’s power to print excess money but also enhance economic integration from which several political and economic advantages can be achieved.

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Appendix Figure 6: Inflation and Inflation in Selected SARC Countries