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Inflation and inflation uncertainty in the ASEAN-5 economies
Journal of Asian Economics 21 (2010) 105–112
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Journal of Asian Economics
Inflation and inflation uncertainty in the ASEAN-5 economies Komain Jiranyakul a,1, Timothy P. Opiela b,* a b
School of Development Economics, National Institute of Development Administration, Bangkapi, Bangkok 10240, Thailand Kellstadt Graduate School of Business and Department of Economics, DePaul University, Chicago, IL 60604, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 19 August 2009 Accepted 22 September 2009
This study explores the linkage between inflation and inflation uncertainty in the ASEAN-5 countries over the period 1970:01–2007:12. Inflation uncertainty is estimated as a conditional variance in an AR(p)-EGARCH(1,1) model. Granger causality tests show that rising inflation increases inflation uncertainty and that rising inflation uncertainty increases inflation in all five countries. The ASEAN-5 have had low inflation rates relative to other emerging markets. Thus, our study shows that even in low inflation emerging markets inflation can lead to inflation uncertainty and uncertainty can lead to inflation. Given current inflationary pressures in these countries, our results warn of possible costs of not keeping inflation in check. ß 2009 Elsevier Inc. All rights reserved.
JEL classification: E31 C22 Keywords: Inflation uncertainty GARCH EGARCH
1. Introduction Friedman (1977) lays out a framework for how inflation can cause inflation uncertainty, leading to inefficient decisions and decreases in economic growth. Cukierman and Meltzer (1986) suggest that inflation uncertainty could lead to inflation and also lower long-run economic growth. The empirical examination of the relation between inflation and inflation uncertainty has received much attention over the last 15 years. However, the evidence on the two above-mentioned hypotheses appears mixed, particularly for the latter hypothesis. These mixed results are thought to arise due to differences in econometric techniques used to estimate uncertainty, due to the choice of countries, and due to the choice of sample period and data frequency. On the first issue, most studies use the standard GARCH(1,1) model to estimate inflation uncertainty. However, this model makes restrictive assumptions about the behavior of inflation uncertainty that are inconsistent with Friedman’s notion of uncertainty. Imbedded in the latter two issues are the possibility that this linkage may depend on the level of inflation, and monetary policy regimes and economic development, all of which differ among countries and over time. Most of the studies on inflation uncertainty have explored this relation in well-developed, industrialized countries where inflation rates have been historically low to moderate. The few studies that exist on emerging markets look at countries with fairly high inflation rates. Several studies also look at groups of countries that share similar financial development, economic shocks and monetary regimes. Our paper focuses on the relationship between inflation and inflation uncertainty for the ASEAN-5 countries: Indonesia, Malaysia, the Philippines, Singapore and Thailand. These emerging market economies have many common financial and real
* Corresponding author. Tel.: +1 312 362 5584. E-mail addresses: [email protected] (K. Jiranyakul), [email protected] (T.P. Opiela). 1 Tel.: +66 02 727 3183. 1049-0078/$ – see front matter ß 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.asieco.2009.09.007
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sector similarities, and are exposed to common regional shocks. All five countries have gone through substantial financial and industrial development, though at different times and to differing degrees over the last four decades. These countries also have had low to moderate inflation rates relative to other emerging market economies. Despite these commonalities, there are notable differences. Average annual inflation rates between these countries have varied from 3% to 11% over the last 40 years. The variance in inflation among these countries is also high relative to other uncertainty studies on groups of countries. Additionally, there is a big dispersion in the rankings of central bank independence among these countries. That the ASEAN-5 have so many similarities, yet are different in ways that may impact on the linkage between inflation and inflation uncertainty raises the question as to whether they share similar links between inflation and inflation uncertainty. We explain this linkage by estimating a conditional variance series using the EGARCH technique. This flexible GARCH model allows for an asymmetric response of inflation uncertainty to positive and negative inflation shocks. We find that inflation causes inflation uncertainty in all five countries. That is, even in the countries that have had persistently low inflation it appears that inflation causes inflation uncertainty. We also find evidence that inflation uncertainty causes inflation in all five countries. These results can be contrasted with those of uncertainty studies of industrialized countries with low inflation rates, which often show strong support for the Friedman hypothesis, but mixed results for the Cukierman–Meltzer hypothesis. We can also contrast our results with uncertainty studies on emerging markets with high inflation rates, which show mixed results for both hypotheses. Given the poor central bank governance in most of the ASEAN-5 countries, our results do not bode well for the prospects of controlling inflation in the current uncertain financial environment. Our paper is organized as follows. Section 2 provides a review of the literature on measuring inflation uncertainty and the results from past studies. Section 3 sets up a model for estimating a conditional variance as a measure of inflation uncertainty in an AR(p)-EGARCH(1,1) specification. Section 4 summarizes and discusses properties of the data. Section 5 presents the empirical results. The last section concludes the paper. 2. Literature review Friedman (1977) argues that high inflation can give rise to political pressure to reduce it. The monetary authority, however, may or may not be reluctant to lower inflation, resulting in future inflation uncertainty. He further contends that uncertainty could cloud economic decisions, reducing economic growth. Ball (1992) formalizes this relationship in a model of asymmetric information between policy makers and the public. Conversely, Cukierman and Meltzer (1986) suggest the possibility that inflation uncertainty could cause higher inflation as the central bank takes advantage of an uncertain environment to produce inflation surprises to stimulate the economy. This relation may further encourage a central bank’s inflationary bias, leading to lower long-run economic growth. The literature that explores the relation between inflation and inflation uncertainty centers on the empirical estimation of inflation uncertainty that conforms to the notion of uncertainty as set forth by Friedman. This literature then uses causality tests to establish the relation between inflation and the estimated measures of inflation uncertainty. The early measures of inflation uncertainty failed to conform to that outlined by Friedman. Okun (1971) computes the variance of inflation over time and Logue and Willett (1976) compute the cross-sectional dispersion of inflation at a point in time that is averaged over time. These measures do not measure a forecast of future inflation uncertainty. Kline (1977) measures the moving standard deviation of the inflation rate to compute the conditional means (a ten-period moving average) and conditional variances (a five-period moving average) of inflation. However this procedure produces a misspecification of the means and, therefore, biased estimates of the variances. According to the theories of Ball (1992), and Cukierman and Meltzer (1986), inflation uncertainty is the variance of the unpredictable component of an inflation forecast. That is, the conditional variance of inflation. Engle (1983) employs an autoregressive conditional heteroskedasticity (ARCH) model to estimate the conditional mean and variance of inflation from U.S. data. ARCH models provide time-varying estimates of the conditional variance of inflation, specified as a linear function of current and past squared forecast errors. Evans (1991) employs an ARCH model and finds a positive link between inflation and inflation uncertainty, but does not establish causation. Bollerslev (1986) develops a generalized ARCH (or GARCH) model in which the time-varying estimates of the conditional variance also include past variances. He finds that the conditional variance does not appear to be closely related to inflation. Several subsequent studies employ the simple GARCH(1,1) model to measure inflation uncertainty and then use these measures in Granger causality test to explore the relation between inflation and inflation uncertainty (Grier & Perry, G–P, 1998, Thornton, 2007). These studies employ the two-step procedure of estimating an inflation process, which is then used to estimate a GARCH variance series. However, the problem of generated regressors arises in this two-step procedure, which may bias the results of the Granger causality tests (G–P, 1998). Consequently, some studies estimate the inflation/GARCH equations simultaneously in a bivariate GARCH-in-mean (GARCH-M) model (Baillie et al., 1996, G–P, 2000, Wilson, 2006). This technique avoids the problem of generated regressors, but does not allow lags in the causality tests. Because the effect of inflation on uncertainty is thought to take several periods, this limits the ability to establish causality. We can compare the results of these two techniques for those studies that examine the same countries. For Japan, G–P (1998) find support for both Friedman–Ball and Cukierman–Meltzer hypotheses, while Wilson (2006) finds support for only the first hypothesis. For the U.S., G–P (1998) finds that inflation causes inflation uncertainty and that increased uncertainty leads to lower inflation, while G–P (2000) only find support for the first hypothesis.
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The simple GARCH(1,1), or symmetric GARCH, model imposes symmetry on the response of uncertainty to inflation shocks. This restriction does not conform well to the Friedman–Ball notion of uncertainty, which should decrease if inflation falls. Several varieties of asymmetric GARCH models provide fewer restrictions on the behavior of uncertainty. Power GARCH (PGARCH) is used by Daal et al. (2005), threshold GARCH (TGARCH) is employed by Zakoian (1994) and exponential GARCH (EGARCH) is estimated by Fountas, Ioannidis, & Karanasos (2004) and Wilson (2006). Each of these models allows positive inflation shocks to have different effects on estimates of uncertainty than that of negative shocks. We can compare the results of these models with those from the symmetric GARCH models for similar countries. Using the symmetric GARCH model, G–P (1998) find that inflation causes inflation uncertainty and uncertainty causes inflation for France, Germany and Italy, the latter two countries have negative or mixed signs. Using EGARCH, Fountas et al. (2004) find that inflation causes inflation uncertainty for France and Italy, but not Germany and they find that uncertainty causes inflation for France and Germany with a negative sign. Using PGARCH, Daal et al. (2005) find evidence of the first hypothesis for France, Germany and Italy, but find support for the second hypothesis only for Italy. Thus, even with the same countries and flexible GARCH models, the results appear to be mixed. Inflation uncertainty studies also differ in examining countries at different stages of development. A group of papers look at individual industrialized countries and groups of industrialized countries: G–P (2000) for the U.S., Wilson (2006) for Japan, G–P (1998) and Daal et al. (2005) for the G-7 countries and Fountas et al. (2004) for five European countries. Except for Italy and Spain the countries chosen have low to moderate average inflation. Most of these studies find that there is support for the Friedman–Ball hypothesis, but little evidence to support the Cukierman–Meltzer hypothesis. Another group of papers concentrate on emerging market economics: Nas and Perry (2000) for Turkey, Daal et al. (2005) for a group of Latin American, Asian, and Middle Eastern countries, and Thornton (2007) for Argentina. All of these studies find that inflation causes inflation uncertainty and that this relation is positive, with the exception being Peru (Daal et al., 2005), which has no relation. Support for the hypothesis that inflation uncertainty causes inflation is mixed in both sign and significance. Almost all of the above studies examine emerging market countries that have moderate to high inflation. There seems to be agreement that support for the Friedman–Ball hypothesis is strong for both developed and emerging markets. The Cukierman–Meltzer hypothesis seems to be mixed across all studies. It is also unclear what role the level of inflation plays in this relation since the combination of financial development and low inflation go hand-in-hand. Our study explores the relation between inflation and inflation uncertainty for the ASEAN-5 countries. We employ an EGARCH model to estimate inflation uncertainty. As in the above-mentioned studies, this model provides a flexible alternative to the standard symmetric GARCH(1,1) model. The countries we choose are emerging markets. Unlike the emerging markets studied in Daal et al. (2005), the ASEAN-5 have both low to moderate average inflation over our sample. Unlike other studies of a group of countries, the ASEAN-5 have a wider variance in their average inflation rates. Our group of countries also has a variance in the degree of financial and industrial development. These differences in characteristics may have an impact on the relation between inflation and inflation uncertainty. Hence, it may be of interest to explore this relation for these five economies. 3. Measuring inflation uncertainty The standard time-series model of inflation is usually given as an autoregressive, AR(p), specification:1
pt ¼ a0 þ
p X ai pti þ et
(1)
i¼1
where p is the inflation rate computed as the percentage change in a price index and et is the error term, which is assumed to be N(0, s2). Eq. (1) can be used to estimate the conditional mean of inflation. However, the estimated variance in Eq. (1) is not time varying. Bollerslev (1986) introduces the GARCH(1,1) model to calculate inflation uncertainty. The GARCH process that allows lagged conditional variances to enter into the model is specified as: ht ¼ a0 þ a1 e2t1 þ b1 ht1
(2) t 2Þ
where ht is the conditional variance ðs and where a0 0, a1 0, and b1 0,. The left-hand-side term is the time-varying residual variance representing a series of inflation uncertainty estimates. This measure of inflation uncertainty mostly conforms to the notion of inflation uncertainty expressed by Friedman and Ball.2 Brunner and Hess (1993) point out that the GARCH model places symmetry restrictions on the conditional variance that are inconsistent with the notion of inflation uncertainty in Friedman. Because the conditional variance is a function of the square of the residuals, it assumes that agents are more uncertain about future inflation whether inflation falls or rises. However, according to Friedman and Ball, information that inflation is lower should reduce uncertainty rather than increase it.
1 For our data an ARMA(p, q) – GARCH(1,1) with q = 0 performs well in generating the estimated GARCH variance series as a measure of inflation uncertainty. There is no one accepted structural model for inflation. Autoregressive specifications are popular in the empirical literature and are often employed to analyze the relationship between inflation and inflation uncertainty (see Grier and Perry, 1998 and Joyce, 1995 for examples). 2 Note that if b1 = 0, then Eq. (3) will collapse to an ARCH model in Engle (1983).
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Several models have been proposed for easing this symmetry restriction. We employ the asymmetric exponential-GARCH (EGARCH) model proposed by Nelson (1991). The GARCH model requires non-negative coefficients, whereas the EGARCH specification models the logarithm of the conditional variance and does not impose non-negativity constraints. The mean and variance equations of the AR(p)-EGARCH(1,1) model can be expressed as:
pt ¼ a0 þ
p X ai pti þ et
(3)
et1 t1 þ g peffiffiffiffiffiffiffiffiffi log ht ¼ a0 þ a1 log ht1 þ b ht1 ht1 i¼1
(4)
If g is non-zero, the impact of inflation on inflation uncertainty is asymmetric. When g is positive an increase in inflation causes more inflation uncertainty, whereas a decrease in inflation produces less uncertainty. This interpretation is similar to that of Friedman (see Brunner and Hess, 1993). Besides the flexibility of this asymmetry the logarithm specification assumes greater weight is placed on higher levels of inflation in estimating uncertainty, which conforms to the Friedman–Ball hypothesis. 4. Granger causality tests: the relationship between inflation and inflation uncertainty We employ Granger causality tests to explore the linkage between inflation and inflation uncertainty. The standard Granger causality test is a test of temporal ordering between two variables, allowing us to examine whether inflation (pt) precedes inflation uncertainty (ht) and whether inflation uncertainty precedes inflation. We use the following equations for inflation and inflation uncertainty to perform these two tests: ht ¼ a0 þ
pt ¼ g 0 þ
k X
k X
ti
i¼1
ai hti þ
bi pti þ nt
k X
k X
i¼1
i¼1
g i pti þ
di hti þ ht
(5)
(6)
Eq. (5) is used to test whether inflation causes inflation uncertainty while Eq. (6) is used to test whether inflation uncertainty causes inflation. This test procedure requires finding the optimal lag length for the above two equations. This is usually determined by using the Akaike information criterion (AIC). As mentioned above, there is a tradeoff between using the two-step approach and suffering with the problem of generated regressors, and using the simultaneous estimation technique and only being able to use contemporaneous variables in causality tests (see Fountas et al., 2004, p. 225, Grier & Perry, 1998, footnote 17). We choose the two-step procedure and the use of lagged variables in the Granger causality equations. 5. The inflation data and their characteristics For each country, the CPI for all items is taken from January 1970 to December 2007. The data are obtained from the International Monetary Fund IFS database. The inflation rate is computed as the monthly percentage change in the CPI. Summary statistics for the inflation data for the five countries in our sample are reported in Table 1. The average monthly inflation rate of Indonesia is the highest, at 0.960% per month, and Singapore is the lowest, at 0.247% per month. Malaysia has the lowest standard deviation of inflation, while Indonesia has the highest. The skewness measure indicates that all countries’ series are positively skewed and the kurtosis measure indicates that all series are highly leptokurtic relative to the normal distribution. The Jarque–Bera normality test rejects the null hypothesis of a normal distribution for each series, confirming non-normality. We need to establish the stationarity properties of these five series before estimating the EGARCH model. We use several unit root tests to explore these stationarity properties. The two standard unit root tests used in most studies are the augmented Dickey–Fuller (ADF) test and the Phillips–Perron (PP) test. Table 2 reports the results from ADF and PP tests with and without a deterministic linear trend. The optimal lag length of the ADF test is determined by the Schwartz information criterion, while the PP test uses the optimal bandwidth determined by the Bartlett kernel. According to these criteria, all five of our inflation series are integrated of order zero, meaning that each of the inflation rate series is stationary. Two additional unit root tests have recently been developed. Ng and Perron (2001) propose a modification of the conventional unit root test by using Generalized Least Squares (GLS) detrending methods to correct the size distortion and power of the test. The optimal lag length for their test can be selected by using the modified Akaike information criterion (MAIC). The Ng–Perron statistics are given as MZa and MZt. Another approach is proposed by Kwiatkowski, Phillips, Schmidt, & Shin (1992), who test the null hypothesis of stationarity against the alternative hypothesis of non-stationarity. Their KPSS test statistic is used to determine stationarity. We perform these two additional tests, the results of which are reported in Table 3.
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Table 1 Summary statistics on monthly inflation rates for the period 1970–2007. Country
Indonesia
Malaysia
Philippines
Singapore
Thailand
Mean S.D. Skewness Kurtosis Jarque–Bera
0.960 1.545 3.197 19.112 5696.823***
0.307 0.499 1.034 8.001 603.074***
0.852 1.194 2.188 16.033 3583.363***
0.247 0.767 2.197 13.331 2389.526***
0.443 0.717 1.316 7.437 504.518***
***
Significance at the 1% level.
Table 2 Unit root tests for inflation rate series for the 1970–2007 period. Country
Indonesia Malaysia Philippines Singapore Thailand
ADF statistic
ADF statistic
PP statistic
PP statistic
(no trend)
(with trend)
(no trend)
(with trend)
5.406 5.992 5.992 4.611 4.066
[17] [13] [13] [15] [14]
(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
5.574 6.764 6.764 5.225 4.797
[17] [13] [13] [15] [14]
(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
12.873 13.434 13.434 21.340 16.195
[5] (0.000)*** [9] (0.000)*** [9] (0.000)*** [13] (0.000)*** [11] (0.000)***
12.897 13.951 13.952 21.554 16.383
[5] (0.000)*** [9] (0.000)*** [9] (0.000)*** [14] (0.000)*** [11] (0.000)***
Notes: (a) The numbers in brackets indicate the optimal lag-length for the ADF test determined by AIC, and for the optimal bandwidth for the PP test. (b) The numbers in parentheses give the probability of accepting the null hypothesis of the unit root. *** Significance at the 1% level.
Table 3 Ng and Perron, and KPSS unit root tests of inflation (1970–2007). Country
MZa stat.
MZt stat.
Lag
KPSS stat.
Bandwidth
Indonesia Malaysia Philippines Singapore Thailand
25.497*** 5.543 79.853*** 40.944*** 13.088**
3.556*** 1.636* 29.965*** 4.516*** 2.518***
17 16 13 15 14
0.128*** 0.567** 0.757 0.539*** 0.625**
12 13 14 14 13
Note: The critical values for the KPSS test are 0.739, 0.463, and 0.347, which denote significance at the 1%, 5% and 10% level, respectively. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
The additional tests show that the inflation series are stationary for all countries except Malaysia, which has mixed results. For this country the MZa statistic is insignificant, indicating a non-stationary inflation series, but the MZt shows stationarity at the 10% level. The KPSS test statistics show stationarity for all countries except the Philippines. Although some of the results show ambiguity, overall it appears that the five inflation series show stationary during 1970–2007 period. Given this result, we need not transform any of these series before estimating our AR(p)-EGARCH model. 6. Estimation and empirical results In this section we test for the relation between inflation and inflation uncertainty by first estimating our AR(p)EGARCH(1,1) model to generate time-varying estimates of inflation uncertainty. We then use these uncertainty estimates in Granger causality tests to test for the causal relation between inflation and uncertainty. This is followed by a discussion on the robustness of our results. 6.1. Estimating inflation Our stationary inflation series are used to estimate the AR(p)-EGARCH(1,1) model. A stationary series possesses the characteristic that the effects of a given shock will die out overtime, with the series reverting to its long-run mean. This property allows us to estimate the inflation AR(p) process in Eq. (3). The coefficients for our five inflation equations are given in Table 4. Two characteristics of these estimates are worth noting. The residuals exhibit no serial correlation, but they do exhibit heteroskedasticity, the latter of which characterizes ARCH effects. Under these data characteristics, the GARCH specification is deemed suitable to generate a conditional variance series as a measure of inflation uncertainty (see e.g., Engle, 1983). The optimal lag length for each series is determined by likelihood ratio (LR) tests. The lags chosen for Indonesia, Malaysia, Philippines, Singapore, and Thailand are 13, 11, 13, 12, and 12 respectively. The ordinary least squares parameter estimates and diagnostic tests are also reported in Table 4.
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Table 4 OLS estimates from autoregressive model of inflation (1970–2007). Coefficient Intercept: a0 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 R2 F-Statistic Log likelihood Q(4) Q(12) Q2(4) Q2(12)
Indonesia
Malaysia ***
0.369 0.506*** 0.116** 0.197*** 0.056 0.012 0.041 0.107 0.128** 0.101* 0.071 0.017 0.105** 0.092** 0.289 13.388 742.629 0.287 (0.991) 1.519 (0.999) 16.035 (0.000) 36.699 (0.000)
Philippines ***
1.000 0.261*** 0.005 0.076 0.089* 0.001 0.187*** 0.002 0.012 0.008 0.001 0.101**
0.186 8.993 277.035 0.183 3.364 58.247 116.630
(0.999) (0.992) (0.000) (0.000)
***
0.289 0.429*** 0.055 0.116** 0.174*** 0.113** 0.143*** 0.108** 0.015 0.186*** 0.030 0.034 0.079 0.163*** 0.424 24.281 580.087 0.264 (0.992) 1.895 (0.999) 22.943 (0.000) 47.330 (0.000)
Singapore
Thailand
*
0.072 0.038 0.101** 0.148*** 0.120*** 0.167*** 0.151*** 0.035 0.022 0.059 1.165*** 0.017 0.127*** 0.221 10.167 445.101 0.964 4.958 285.520 635.440
(0.915) (0.959) (0.000) (0.000)
0.106** 0.285*** 0.010 0.199*** 0.053 0.087* 0.058 0.080 0.003 0.111** 0.005 0.112 0.224*** 0.273 13.482 411.269 1.260 4.322 34.429 132.420
(0.868) (0.977) (0.000) (0.000)
Note: The numbers in parentheses indicate probabilities. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
The autoregressive model fits well for each of the five countries. The Q statistics show that the residuals are white noise. The sample autocorrelation functions of the residuals show no autocorrelation. The Q(4) and Q(12) test statistics show that the null hypothesis of uncorrelated inflation rates cannot be rejected. Additionally, the sample autocorrelation function of the squared residuals (Q2 test statistic) is used to test for the presence of homoskedasticity in the autoregressive equation. The Q2 statistics suggest that conditional homoskedasticity for each equation can be rejected. This indicates that the inflation
Table 5 Estimates of AR(p)-EGARCH(1,1) model of inflation (1970–2007). Coefficient
Indonesia
Intercept: a0 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13
0.476*** 0.413*** 0.109** 0.196*** 0.104*** 0.037 0.149*** 0.189*** 0.141*** 0.082** 0.066** 0.061** 0.275*** 0.176*** 0.491*** 0.433*** 0.699*** 0.552*** 0.224 7.190 618.245 4.213 (0.548) 17.999 (0.116) 0.462 (0.977) 10.690 (0.556)
a0 a1 b g R2 F-Statistic Log likelihood Q(4) Q(12) Q2(4) Q2(12)
Malaysia 0.090*** 0.171*** 0.061 0.033 0.037 0.049 0.149*** 0.101** 0.047 0.030 0.069 0.104***
0.002*** 0.981*** 0.050*** 0.118*** 0.164 5.625 204.618 0.792 (0.939) 11.416 (0.494) 5.417 (0.248) 10.214 (0.485)
Note: The numbers in parentheses indicate probabilities. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
Philippines 0.262** 0.381*** 0.056 0.092 0.004 0.159*** 0.131*** 0.023 0.036 0.105** 0.070 0.053 0.168*** 0.159*** 0.448*** 0.864*** 0.540*** 0.176*** 0.390 15.961 490.395 5.533 (0.237) 16.748 (0.551) 0.791 (0.939) 6.167 (0.906)
Singapore 0.039* 0.005 0.129*** 0.157*** 0.053 0.157*** 0.157*** 0.077 0.071 0.019 0.023 0.029 0.274*** 0.095*** 0.980*** 0.080** 0.162*** 0.165 5.270 290.372 0.668 (0.955) 4.364 (0.976) 1.362 (0.851) 5.488 (0.940)
Thailand 0.101*** 0.256*** 0.057 0.142*** 0.043 0.097 0.072 0.054 0.009 0.068 0.034 0.067 0.232*** 0.034* 0.981*** 0.002 0.131*** 0.259 9.328 342.761 1.010 (0.908) 2.754 (0.997) 1.180 (0.881) 4.560 (0.971)
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Table 6 Causality tests using AR(p)-EGARCH(1,1) variance series: 1970–2007. Country
Ho: inflation does not Granger cause uncertainty
Ho: uncertainty does not Granger cause inflation
Optimal lag length
Indonesia Malaysia Philippines Singapore Thailand
14.114*** 192.565*** 19.495*** 120.491*** 250.833***
0.239 (+) 2.204** (+) 5.884*** (+) 6.738*** (+) 4.370*** (+)
3 8 8 8 8
(+) (+) (+) (+) (+)
Notes: (a) The numbers in each cell are the F-statistic associated with the null hypothesis followed by *** and **. (b) The optimal lag length is determined by the Akaike information criterion. (c) (+) indicates that the sum of the coefficients is positive. ** Significance at the 5% level. *** Significance at the 1% level.
models might not capture well the time-varying volatility of inflation. Thus, an EGARCH(1,1) process may be more suitable for describing the errors. 6.2. Estimating GARCH variance series We estimate five conditional variance series using an AR(p)-EGARCH(1,1) model. The results of these equations are reported in Table 5. The a1 parameters are significant for all countries and the b coefficients are significant for all countries except Thailand. The g coefficients, which indicate the asymmetric effects of inflation on uncertainty, are positive and significant at the 1% level or better for all countries. This positive sign indicates that an increase in inflation causes more inflation uncertainty, whereas a decrease in inflation produces less uncertainty. This is consistent with the Friedman–Ball notion of inflation uncertainty. 6.3. Granger causality tests We now turn to the results of the Granger causality tests as specified in Eqs. (5) and (6). The results, shown in Table 6, show bi-directional causality in all cases except Indonesia, where we cannot reject the null hypothesis that inflation uncertainty does not cause inflation. The sum of the coefficients for all causality tests for all countries are positive, indicating that rising inflation raises inflation uncertainty and that rising inflation uncertainty raises inflation. These results are supportive of both the Friedman–Ball and the Cukierman–Meltzer hypotheses. 6.4. Robustness tests Previous studies have shown mixed results based on different measures of inflation uncertainty. We tested the robustness of our above-mentioned results by using three alternative techniques. We used the EGARC-M model, which allows for the conditional variance to give feedback and influence the conditional mean (G–P, 2000). The results are different from the two-step approach reported in Tables 5 and 6. As mentioned before, the simultaneous approach may yield invalid Granger causality tests based on lags. Applying Granger causality tests shows bi-directionality of the relation between inflation and inflation uncertainty for all countries, including Indonesia. We also tried the symmetric GARCH(1,1) and the asymmetric TGARCH(1,1) models to estimate the conditional variance series. Both of these methods produced results that did not pass diagnostic tests on serial correlation. Additionally, the ARCH and GARCH terms were negative for some countries. Next we explored the possibility of structural breaks. The use of the CUSUM and the CUSUMSQ methods, two conventional stability tests, produced multiple breaks for each of the five series. Some of the indicated breaks produced series that were not sufficiently long to use in causality tests. An alternative is to choose a break in the data that is obvious a priori. We used a constant dummy variable in our inflation equations to test for a structural break stemming from the financial crisis. We use a dummy that is 1 from July 1997 to December 2007 and zero otherwise. We find that after the financial crisis there is a decrease in inflation for all countries, except Malaysia. We used these inflation equations to estimate a GARCH variance series. Again we looked at Granger causality tests of our two hypotheses. The results show strong bidirectional causality for all five countries, including Indonesia. We conclude that there is support for bi-directional causality in all of the ASEAN-5 countries. 7. Conclusion This study employs an AR(p)-EGARCH(1,1) model to estimate a conditional variance series for the ASEAN-5 countries. We then use Granger causality tests to test the hypotheses that inflation causes inflation uncertainty and that inflation uncertainty causes inflation over the 1970:01–2007:12 period. The Granger causality tests show strong evidence that
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inflation causes inflation uncertainty for all five countries. We also find that inflation uncertainty affects inflation positively in all countries. Our results have implications for the inflation uncertainty literature and for policy. The results support the notion that an emerging market country with low to moderate inflation (e.g., Singapore and Malaysia) can experience inflation uncertainty. Studies on industrialized countries with low to moderate inflation often have this same result. Additionally, our result that uncertainty causes a positive effect on inflation in all the ASEAN-5 countries can be contrasted with studies on industrialized and emerging market countries that show mixed results. Strong evidence that inflation raises uncertainty about future inflation implies the need for better monetary stabilization. Yet, all of the ASEAN-5 countries, except for the Philippines and Singapore, have some of the lowest rankings in central bank independence in Asia (Ashan et al., 2005). Given the current environment where inflation is increasing in all five countries, there seems little likelihood that central banks will be able to better control inflation. An even more disturbing policy implication follows from our result that increased inflation uncertainty raises inflation. There may be the potential for central banks with poor governance to succumb to political pressure to increase inflation in an uncertain inflation environment, with the potential for adverse effects on future growth. References Ashan, A., Skully, M., & Wickramanayake, J. (2005). Central bank independence and governance and inflation in Asia Pacific, working paper. Baillie, R., Chung, C., & Tieslau, F. (1996). Analyzing inflation by the fractionally integrated ARFIMA-GARCH model. Journal of Applied Econometrics, 199, 23–40. Ball, L. (1992). Why does high inflation raise inflation uncertainty. Journal of Monetary Economics, 29, 371–388. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327. Brunner, A., & Hess, G. (1993). Are higher levels of inflation less predictable? A state dependent conditional heteroskedasticity approach. Journal of Business and Economic Statistics, 11, 187–197. Cukierman, A., & Meltzer, A. (1986). A theory of ambiguity, credibility, and inflation under discretion and asymmetric information. Econometrica, 54, 1099–1128. Daal, E., Naka, A., & Sanchez, B. (2005). Re-examining inflation and inflation uncertainty in developed and emerging countries. Economics Letters, 89, 180–186. Engle, R. (1983). Estimates of the variance of U.S. inflation rates based on the ARCH model. Journal of Money Credit and Banking, 15, 286–301. Evans, M. (1991). Discovering the link between inflation rates and inflation uncertainty. Journal of Money, Credit, and Banking, 23, 169–184. Fountas, S., Ioannidis, A., & Karanasos, M. (2004). Inflation uncertainty and a common European monetary policy. The Manchester School 72 221-204. Friedman, M. (1977). Nobel lecture: Inflation and unemployment. Journal of Political Economy, 85, 451–472. Grier, K., & Perry, M. (1998). On inflation and inflation uncertainty in the G-7 countries. Journal of International Money and Finance, 17, 671–689. Grier, K., & Perry, M. (2000). The effects of real and nominal uncertainty on inflation and output growth: Some GARCH-M evidence. Journal of Applied Econometrics, 15, 45–58. Joyce, M. (1995). Modeling UK inflation uncertainty: The impact of news and the relationship with inflation. Bank of England working paper 30. Kline, B. (1977). The demand for quality-adjusted cash balance: Price uncertainty in the U.S. demand for money function. Journal of Political Economy, 85, 691–715. Kwiatkowski, D., Phillips, P., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54, 159–178. Logue, D., & Willett, T. (1976). A Note on the relation between the rate and variability of inflation. Economica, 43, 151–158. Nas, T., & Perry, M. (2000). Inflation, inflation uncertainty, and monetary policy in turkey: 1960–1998. Contemporary Economic Policy, 18, 170–180. Nelson, D. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347–370. Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69, 1519–1554. Okun, A. (1971). The mirage of steady inflation. Brooking papers on economic activity, 2, 485–498. Thornton, J. (2007). The relationship between inflation and inflation uncertainty in emerging market economies. Southern Economic Journal, 73, 858–870. Wilson, B.K. (2006). The links between inflation, inflation uncertainty and output growth: New time series evidence from Japan. Journal of Macroeconomics, 28, 609–620. Zakoian, J. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18, 931–944.
Contents lists available at ScienceDirect
Journal of Asian Economics
Inflation and inflation uncertainty in the ASEAN-5 economies Komain Jiranyakul a,1, Timothy P. Opiela b,* a b
School of Development Economics, National Institute of Development Administration, Bangkapi, Bangkok 10240, Thailand Kellstadt Graduate School of Business and Department of Economics, DePaul University, Chicago, IL 60604, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 19 August 2009 Accepted 22 September 2009
This study explores the linkage between inflation and inflation uncertainty in the ASEAN-5 countries over the period 1970:01–2007:12. Inflation uncertainty is estimated as a conditional variance in an AR(p)-EGARCH(1,1) model. Granger causality tests show that rising inflation increases inflation uncertainty and that rising inflation uncertainty increases inflation in all five countries. The ASEAN-5 have had low inflation rates relative to other emerging markets. Thus, our study shows that even in low inflation emerging markets inflation can lead to inflation uncertainty and uncertainty can lead to inflation. Given current inflationary pressures in these countries, our results warn of possible costs of not keeping inflation in check. ß 2009 Elsevier Inc. All rights reserved.
JEL classification: E31 C22 Keywords: Inflation uncertainty GARCH EGARCH
1. Introduction Friedman (1977) lays out a framework for how inflation can cause inflation uncertainty, leading to inefficient decisions and decreases in economic growth. Cukierman and Meltzer (1986) suggest that inflation uncertainty could lead to inflation and also lower long-run economic growth. The empirical examination of the relation between inflation and inflation uncertainty has received much attention over the last 15 years. However, the evidence on the two above-mentioned hypotheses appears mixed, particularly for the latter hypothesis. These mixed results are thought to arise due to differences in econometric techniques used to estimate uncertainty, due to the choice of countries, and due to the choice of sample period and data frequency. On the first issue, most studies use the standard GARCH(1,1) model to estimate inflation uncertainty. However, this model makes restrictive assumptions about the behavior of inflation uncertainty that are inconsistent with Friedman’s notion of uncertainty. Imbedded in the latter two issues are the possibility that this linkage may depend on the level of inflation, and monetary policy regimes and economic development, all of which differ among countries and over time. Most of the studies on inflation uncertainty have explored this relation in well-developed, industrialized countries where inflation rates have been historically low to moderate. The few studies that exist on emerging markets look at countries with fairly high inflation rates. Several studies also look at groups of countries that share similar financial development, economic shocks and monetary regimes. Our paper focuses on the relationship between inflation and inflation uncertainty for the ASEAN-5 countries: Indonesia, Malaysia, the Philippines, Singapore and Thailand. These emerging market economies have many common financial and real
* Corresponding author. Tel.: +1 312 362 5584. E-mail addresses: [email protected] (K. Jiranyakul), [email protected] (T.P. Opiela). 1 Tel.: +66 02 727 3183. 1049-0078/$ – see front matter ß 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.asieco.2009.09.007
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sector similarities, and are exposed to common regional shocks. All five countries have gone through substantial financial and industrial development, though at different times and to differing degrees over the last four decades. These countries also have had low to moderate inflation rates relative to other emerging market economies. Despite these commonalities, there are notable differences. Average annual inflation rates between these countries have varied from 3% to 11% over the last 40 years. The variance in inflation among these countries is also high relative to other uncertainty studies on groups of countries. Additionally, there is a big dispersion in the rankings of central bank independence among these countries. That the ASEAN-5 have so many similarities, yet are different in ways that may impact on the linkage between inflation and inflation uncertainty raises the question as to whether they share similar links between inflation and inflation uncertainty. We explain this linkage by estimating a conditional variance series using the EGARCH technique. This flexible GARCH model allows for an asymmetric response of inflation uncertainty to positive and negative inflation shocks. We find that inflation causes inflation uncertainty in all five countries. That is, even in the countries that have had persistently low inflation it appears that inflation causes inflation uncertainty. We also find evidence that inflation uncertainty causes inflation in all five countries. These results can be contrasted with those of uncertainty studies of industrialized countries with low inflation rates, which often show strong support for the Friedman hypothesis, but mixed results for the Cukierman–Meltzer hypothesis. We can also contrast our results with uncertainty studies on emerging markets with high inflation rates, which show mixed results for both hypotheses. Given the poor central bank governance in most of the ASEAN-5 countries, our results do not bode well for the prospects of controlling inflation in the current uncertain financial environment. Our paper is organized as follows. Section 2 provides a review of the literature on measuring inflation uncertainty and the results from past studies. Section 3 sets up a model for estimating a conditional variance as a measure of inflation uncertainty in an AR(p)-EGARCH(1,1) specification. Section 4 summarizes and discusses properties of the data. Section 5 presents the empirical results. The last section concludes the paper. 2. Literature review Friedman (1977) argues that high inflation can give rise to political pressure to reduce it. The monetary authority, however, may or may not be reluctant to lower inflation, resulting in future inflation uncertainty. He further contends that uncertainty could cloud economic decisions, reducing economic growth. Ball (1992) formalizes this relationship in a model of asymmetric information between policy makers and the public. Conversely, Cukierman and Meltzer (1986) suggest the possibility that inflation uncertainty could cause higher inflation as the central bank takes advantage of an uncertain environment to produce inflation surprises to stimulate the economy. This relation may further encourage a central bank’s inflationary bias, leading to lower long-run economic growth. The literature that explores the relation between inflation and inflation uncertainty centers on the empirical estimation of inflation uncertainty that conforms to the notion of uncertainty as set forth by Friedman. This literature then uses causality tests to establish the relation between inflation and the estimated measures of inflation uncertainty. The early measures of inflation uncertainty failed to conform to that outlined by Friedman. Okun (1971) computes the variance of inflation over time and Logue and Willett (1976) compute the cross-sectional dispersion of inflation at a point in time that is averaged over time. These measures do not measure a forecast of future inflation uncertainty. Kline (1977) measures the moving standard deviation of the inflation rate to compute the conditional means (a ten-period moving average) and conditional variances (a five-period moving average) of inflation. However this procedure produces a misspecification of the means and, therefore, biased estimates of the variances. According to the theories of Ball (1992), and Cukierman and Meltzer (1986), inflation uncertainty is the variance of the unpredictable component of an inflation forecast. That is, the conditional variance of inflation. Engle (1983) employs an autoregressive conditional heteroskedasticity (ARCH) model to estimate the conditional mean and variance of inflation from U.S. data. ARCH models provide time-varying estimates of the conditional variance of inflation, specified as a linear function of current and past squared forecast errors. Evans (1991) employs an ARCH model and finds a positive link between inflation and inflation uncertainty, but does not establish causation. Bollerslev (1986) develops a generalized ARCH (or GARCH) model in which the time-varying estimates of the conditional variance also include past variances. He finds that the conditional variance does not appear to be closely related to inflation. Several subsequent studies employ the simple GARCH(1,1) model to measure inflation uncertainty and then use these measures in Granger causality test to explore the relation between inflation and inflation uncertainty (Grier & Perry, G–P, 1998, Thornton, 2007). These studies employ the two-step procedure of estimating an inflation process, which is then used to estimate a GARCH variance series. However, the problem of generated regressors arises in this two-step procedure, which may bias the results of the Granger causality tests (G–P, 1998). Consequently, some studies estimate the inflation/GARCH equations simultaneously in a bivariate GARCH-in-mean (GARCH-M) model (Baillie et al., 1996, G–P, 2000, Wilson, 2006). This technique avoids the problem of generated regressors, but does not allow lags in the causality tests. Because the effect of inflation on uncertainty is thought to take several periods, this limits the ability to establish causality. We can compare the results of these two techniques for those studies that examine the same countries. For Japan, G–P (1998) find support for both Friedman–Ball and Cukierman–Meltzer hypotheses, while Wilson (2006) finds support for only the first hypothesis. For the U.S., G–P (1998) finds that inflation causes inflation uncertainty and that increased uncertainty leads to lower inflation, while G–P (2000) only find support for the first hypothesis.
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The simple GARCH(1,1), or symmetric GARCH, model imposes symmetry on the response of uncertainty to inflation shocks. This restriction does not conform well to the Friedman–Ball notion of uncertainty, which should decrease if inflation falls. Several varieties of asymmetric GARCH models provide fewer restrictions on the behavior of uncertainty. Power GARCH (PGARCH) is used by Daal et al. (2005), threshold GARCH (TGARCH) is employed by Zakoian (1994) and exponential GARCH (EGARCH) is estimated by Fountas, Ioannidis, & Karanasos (2004) and Wilson (2006). Each of these models allows positive inflation shocks to have different effects on estimates of uncertainty than that of negative shocks. We can compare the results of these models with those from the symmetric GARCH models for similar countries. Using the symmetric GARCH model, G–P (1998) find that inflation causes inflation uncertainty and uncertainty causes inflation for France, Germany and Italy, the latter two countries have negative or mixed signs. Using EGARCH, Fountas et al. (2004) find that inflation causes inflation uncertainty for France and Italy, but not Germany and they find that uncertainty causes inflation for France and Germany with a negative sign. Using PGARCH, Daal et al. (2005) find evidence of the first hypothesis for France, Germany and Italy, but find support for the second hypothesis only for Italy. Thus, even with the same countries and flexible GARCH models, the results appear to be mixed. Inflation uncertainty studies also differ in examining countries at different stages of development. A group of papers look at individual industrialized countries and groups of industrialized countries: G–P (2000) for the U.S., Wilson (2006) for Japan, G–P (1998) and Daal et al. (2005) for the G-7 countries and Fountas et al. (2004) for five European countries. Except for Italy and Spain the countries chosen have low to moderate average inflation. Most of these studies find that there is support for the Friedman–Ball hypothesis, but little evidence to support the Cukierman–Meltzer hypothesis. Another group of papers concentrate on emerging market economics: Nas and Perry (2000) for Turkey, Daal et al. (2005) for a group of Latin American, Asian, and Middle Eastern countries, and Thornton (2007) for Argentina. All of these studies find that inflation causes inflation uncertainty and that this relation is positive, with the exception being Peru (Daal et al., 2005), which has no relation. Support for the hypothesis that inflation uncertainty causes inflation is mixed in both sign and significance. Almost all of the above studies examine emerging market countries that have moderate to high inflation. There seems to be agreement that support for the Friedman–Ball hypothesis is strong for both developed and emerging markets. The Cukierman–Meltzer hypothesis seems to be mixed across all studies. It is also unclear what role the level of inflation plays in this relation since the combination of financial development and low inflation go hand-in-hand. Our study explores the relation between inflation and inflation uncertainty for the ASEAN-5 countries. We employ an EGARCH model to estimate inflation uncertainty. As in the above-mentioned studies, this model provides a flexible alternative to the standard symmetric GARCH(1,1) model. The countries we choose are emerging markets. Unlike the emerging markets studied in Daal et al. (2005), the ASEAN-5 have both low to moderate average inflation over our sample. Unlike other studies of a group of countries, the ASEAN-5 have a wider variance in their average inflation rates. Our group of countries also has a variance in the degree of financial and industrial development. These differences in characteristics may have an impact on the relation between inflation and inflation uncertainty. Hence, it may be of interest to explore this relation for these five economies. 3. Measuring inflation uncertainty The standard time-series model of inflation is usually given as an autoregressive, AR(p), specification:1
pt ¼ a0 þ
p X ai pti þ et
(1)
i¼1
where p is the inflation rate computed as the percentage change in a price index and et is the error term, which is assumed to be N(0, s2). Eq. (1) can be used to estimate the conditional mean of inflation. However, the estimated variance in Eq. (1) is not time varying. Bollerslev (1986) introduces the GARCH(1,1) model to calculate inflation uncertainty. The GARCH process that allows lagged conditional variances to enter into the model is specified as: ht ¼ a0 þ a1 e2t1 þ b1 ht1
(2) t 2Þ
where ht is the conditional variance ðs and where a0 0, a1 0, and b1 0,. The left-hand-side term is the time-varying residual variance representing a series of inflation uncertainty estimates. This measure of inflation uncertainty mostly conforms to the notion of inflation uncertainty expressed by Friedman and Ball.2 Brunner and Hess (1993) point out that the GARCH model places symmetry restrictions on the conditional variance that are inconsistent with the notion of inflation uncertainty in Friedman. Because the conditional variance is a function of the square of the residuals, it assumes that agents are more uncertain about future inflation whether inflation falls or rises. However, according to Friedman and Ball, information that inflation is lower should reduce uncertainty rather than increase it.
1 For our data an ARMA(p, q) – GARCH(1,1) with q = 0 performs well in generating the estimated GARCH variance series as a measure of inflation uncertainty. There is no one accepted structural model for inflation. Autoregressive specifications are popular in the empirical literature and are often employed to analyze the relationship between inflation and inflation uncertainty (see Grier and Perry, 1998 and Joyce, 1995 for examples). 2 Note that if b1 = 0, then Eq. (3) will collapse to an ARCH model in Engle (1983).
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Several models have been proposed for easing this symmetry restriction. We employ the asymmetric exponential-GARCH (EGARCH) model proposed by Nelson (1991). The GARCH model requires non-negative coefficients, whereas the EGARCH specification models the logarithm of the conditional variance and does not impose non-negativity constraints. The mean and variance equations of the AR(p)-EGARCH(1,1) model can be expressed as:
pt ¼ a0 þ
p X ai pti þ et
(3)
et1 t1 þ g peffiffiffiffiffiffiffiffiffi log ht ¼ a0 þ a1 log ht1 þ b ht1 ht1 i¼1
(4)
If g is non-zero, the impact of inflation on inflation uncertainty is asymmetric. When g is positive an increase in inflation causes more inflation uncertainty, whereas a decrease in inflation produces less uncertainty. This interpretation is similar to that of Friedman (see Brunner and Hess, 1993). Besides the flexibility of this asymmetry the logarithm specification assumes greater weight is placed on higher levels of inflation in estimating uncertainty, which conforms to the Friedman–Ball hypothesis. 4. Granger causality tests: the relationship between inflation and inflation uncertainty We employ Granger causality tests to explore the linkage between inflation and inflation uncertainty. The standard Granger causality test is a test of temporal ordering between two variables, allowing us to examine whether inflation (pt) precedes inflation uncertainty (ht) and whether inflation uncertainty precedes inflation. We use the following equations for inflation and inflation uncertainty to perform these two tests: ht ¼ a0 þ
pt ¼ g 0 þ
k X
k X
ti
i¼1
ai hti þ
bi pti þ nt
k X
k X
i¼1
i¼1
g i pti þ
di hti þ ht
(5)
(6)
Eq. (5) is used to test whether inflation causes inflation uncertainty while Eq. (6) is used to test whether inflation uncertainty causes inflation. This test procedure requires finding the optimal lag length for the above two equations. This is usually determined by using the Akaike information criterion (AIC). As mentioned above, there is a tradeoff between using the two-step approach and suffering with the problem of generated regressors, and using the simultaneous estimation technique and only being able to use contemporaneous variables in causality tests (see Fountas et al., 2004, p. 225, Grier & Perry, 1998, footnote 17). We choose the two-step procedure and the use of lagged variables in the Granger causality equations. 5. The inflation data and their characteristics For each country, the CPI for all items is taken from January 1970 to December 2007. The data are obtained from the International Monetary Fund IFS database. The inflation rate is computed as the monthly percentage change in the CPI. Summary statistics for the inflation data for the five countries in our sample are reported in Table 1. The average monthly inflation rate of Indonesia is the highest, at 0.960% per month, and Singapore is the lowest, at 0.247% per month. Malaysia has the lowest standard deviation of inflation, while Indonesia has the highest. The skewness measure indicates that all countries’ series are positively skewed and the kurtosis measure indicates that all series are highly leptokurtic relative to the normal distribution. The Jarque–Bera normality test rejects the null hypothesis of a normal distribution for each series, confirming non-normality. We need to establish the stationarity properties of these five series before estimating the EGARCH model. We use several unit root tests to explore these stationarity properties. The two standard unit root tests used in most studies are the augmented Dickey–Fuller (ADF) test and the Phillips–Perron (PP) test. Table 2 reports the results from ADF and PP tests with and without a deterministic linear trend. The optimal lag length of the ADF test is determined by the Schwartz information criterion, while the PP test uses the optimal bandwidth determined by the Bartlett kernel. According to these criteria, all five of our inflation series are integrated of order zero, meaning that each of the inflation rate series is stationary. Two additional unit root tests have recently been developed. Ng and Perron (2001) propose a modification of the conventional unit root test by using Generalized Least Squares (GLS) detrending methods to correct the size distortion and power of the test. The optimal lag length for their test can be selected by using the modified Akaike information criterion (MAIC). The Ng–Perron statistics are given as MZa and MZt. Another approach is proposed by Kwiatkowski, Phillips, Schmidt, & Shin (1992), who test the null hypothesis of stationarity against the alternative hypothesis of non-stationarity. Their KPSS test statistic is used to determine stationarity. We perform these two additional tests, the results of which are reported in Table 3.
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Table 1 Summary statistics on monthly inflation rates for the period 1970–2007. Country
Indonesia
Malaysia
Philippines
Singapore
Thailand
Mean S.D. Skewness Kurtosis Jarque–Bera
0.960 1.545 3.197 19.112 5696.823***
0.307 0.499 1.034 8.001 603.074***
0.852 1.194 2.188 16.033 3583.363***
0.247 0.767 2.197 13.331 2389.526***
0.443 0.717 1.316 7.437 504.518***
***
Significance at the 1% level.
Table 2 Unit root tests for inflation rate series for the 1970–2007 period. Country
Indonesia Malaysia Philippines Singapore Thailand
ADF statistic
ADF statistic
PP statistic
PP statistic
(no trend)
(with trend)
(no trend)
(with trend)
5.406 5.992 5.992 4.611 4.066
[17] [13] [13] [15] [14]
(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
5.574 6.764 6.764 5.225 4.797
[17] [13] [13] [15] [14]
(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
12.873 13.434 13.434 21.340 16.195
[5] (0.000)*** [9] (0.000)*** [9] (0.000)*** [13] (0.000)*** [11] (0.000)***
12.897 13.951 13.952 21.554 16.383
[5] (0.000)*** [9] (0.000)*** [9] (0.000)*** [14] (0.000)*** [11] (0.000)***
Notes: (a) The numbers in brackets indicate the optimal lag-length for the ADF test determined by AIC, and for the optimal bandwidth for the PP test. (b) The numbers in parentheses give the probability of accepting the null hypothesis of the unit root. *** Significance at the 1% level.
Table 3 Ng and Perron, and KPSS unit root tests of inflation (1970–2007). Country
MZa stat.
MZt stat.
Lag
KPSS stat.
Bandwidth
Indonesia Malaysia Philippines Singapore Thailand
25.497*** 5.543 79.853*** 40.944*** 13.088**
3.556*** 1.636* 29.965*** 4.516*** 2.518***
17 16 13 15 14
0.128*** 0.567** 0.757 0.539*** 0.625**
12 13 14 14 13
Note: The critical values for the KPSS test are 0.739, 0.463, and 0.347, which denote significance at the 1%, 5% and 10% level, respectively. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
The additional tests show that the inflation series are stationary for all countries except Malaysia, which has mixed results. For this country the MZa statistic is insignificant, indicating a non-stationary inflation series, but the MZt shows stationarity at the 10% level. The KPSS test statistics show stationarity for all countries except the Philippines. Although some of the results show ambiguity, overall it appears that the five inflation series show stationary during 1970–2007 period. Given this result, we need not transform any of these series before estimating our AR(p)-EGARCH model. 6. Estimation and empirical results In this section we test for the relation between inflation and inflation uncertainty by first estimating our AR(p)EGARCH(1,1) model to generate time-varying estimates of inflation uncertainty. We then use these uncertainty estimates in Granger causality tests to test for the causal relation between inflation and uncertainty. This is followed by a discussion on the robustness of our results. 6.1. Estimating inflation Our stationary inflation series are used to estimate the AR(p)-EGARCH(1,1) model. A stationary series possesses the characteristic that the effects of a given shock will die out overtime, with the series reverting to its long-run mean. This property allows us to estimate the inflation AR(p) process in Eq. (3). The coefficients for our five inflation equations are given in Table 4. Two characteristics of these estimates are worth noting. The residuals exhibit no serial correlation, but they do exhibit heteroskedasticity, the latter of which characterizes ARCH effects. Under these data characteristics, the GARCH specification is deemed suitable to generate a conditional variance series as a measure of inflation uncertainty (see e.g., Engle, 1983). The optimal lag length for each series is determined by likelihood ratio (LR) tests. The lags chosen for Indonesia, Malaysia, Philippines, Singapore, and Thailand are 13, 11, 13, 12, and 12 respectively. The ordinary least squares parameter estimates and diagnostic tests are also reported in Table 4.
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Table 4 OLS estimates from autoregressive model of inflation (1970–2007). Coefficient Intercept: a0 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 R2 F-Statistic Log likelihood Q(4) Q(12) Q2(4) Q2(12)
Indonesia
Malaysia ***
0.369 0.506*** 0.116** 0.197*** 0.056 0.012 0.041 0.107 0.128** 0.101* 0.071 0.017 0.105** 0.092** 0.289 13.388 742.629 0.287 (0.991) 1.519 (0.999) 16.035 (0.000) 36.699 (0.000)
Philippines ***
1.000 0.261*** 0.005 0.076 0.089* 0.001 0.187*** 0.002 0.012 0.008 0.001 0.101**
0.186 8.993 277.035 0.183 3.364 58.247 116.630
(0.999) (0.992) (0.000) (0.000)
***
0.289 0.429*** 0.055 0.116** 0.174*** 0.113** 0.143*** 0.108** 0.015 0.186*** 0.030 0.034 0.079 0.163*** 0.424 24.281 580.087 0.264 (0.992) 1.895 (0.999) 22.943 (0.000) 47.330 (0.000)
Singapore
Thailand
*
0.072 0.038 0.101** 0.148*** 0.120*** 0.167*** 0.151*** 0.035 0.022 0.059 1.165*** 0.017 0.127*** 0.221 10.167 445.101 0.964 4.958 285.520 635.440
(0.915) (0.959) (0.000) (0.000)
0.106** 0.285*** 0.010 0.199*** 0.053 0.087* 0.058 0.080 0.003 0.111** 0.005 0.112 0.224*** 0.273 13.482 411.269 1.260 4.322 34.429 132.420
(0.868) (0.977) (0.000) (0.000)
Note: The numbers in parentheses indicate probabilities. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
The autoregressive model fits well for each of the five countries. The Q statistics show that the residuals are white noise. The sample autocorrelation functions of the residuals show no autocorrelation. The Q(4) and Q(12) test statistics show that the null hypothesis of uncorrelated inflation rates cannot be rejected. Additionally, the sample autocorrelation function of the squared residuals (Q2 test statistic) is used to test for the presence of homoskedasticity in the autoregressive equation. The Q2 statistics suggest that conditional homoskedasticity for each equation can be rejected. This indicates that the inflation
Table 5 Estimates of AR(p)-EGARCH(1,1) model of inflation (1970–2007). Coefficient
Indonesia
Intercept: a0 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13
0.476*** 0.413*** 0.109** 0.196*** 0.104*** 0.037 0.149*** 0.189*** 0.141*** 0.082** 0.066** 0.061** 0.275*** 0.176*** 0.491*** 0.433*** 0.699*** 0.552*** 0.224 7.190 618.245 4.213 (0.548) 17.999 (0.116) 0.462 (0.977) 10.690 (0.556)
a0 a1 b g R2 F-Statistic Log likelihood Q(4) Q(12) Q2(4) Q2(12)
Malaysia 0.090*** 0.171*** 0.061 0.033 0.037 0.049 0.149*** 0.101** 0.047 0.030 0.069 0.104***
0.002*** 0.981*** 0.050*** 0.118*** 0.164 5.625 204.618 0.792 (0.939) 11.416 (0.494) 5.417 (0.248) 10.214 (0.485)
Note: The numbers in parentheses indicate probabilities. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
Philippines 0.262** 0.381*** 0.056 0.092 0.004 0.159*** 0.131*** 0.023 0.036 0.105** 0.070 0.053 0.168*** 0.159*** 0.448*** 0.864*** 0.540*** 0.176*** 0.390 15.961 490.395 5.533 (0.237) 16.748 (0.551) 0.791 (0.939) 6.167 (0.906)
Singapore 0.039* 0.005 0.129*** 0.157*** 0.053 0.157*** 0.157*** 0.077 0.071 0.019 0.023 0.029 0.274*** 0.095*** 0.980*** 0.080** 0.162*** 0.165 5.270 290.372 0.668 (0.955) 4.364 (0.976) 1.362 (0.851) 5.488 (0.940)
Thailand 0.101*** 0.256*** 0.057 0.142*** 0.043 0.097 0.072 0.054 0.009 0.068 0.034 0.067 0.232*** 0.034* 0.981*** 0.002 0.131*** 0.259 9.328 342.761 1.010 (0.908) 2.754 (0.997) 1.180 (0.881) 4.560 (0.971)
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Table 6 Causality tests using AR(p)-EGARCH(1,1) variance series: 1970–2007. Country
Ho: inflation does not Granger cause uncertainty
Ho: uncertainty does not Granger cause inflation
Optimal lag length
Indonesia Malaysia Philippines Singapore Thailand
14.114*** 192.565*** 19.495*** 120.491*** 250.833***
0.239 (+) 2.204** (+) 5.884*** (+) 6.738*** (+) 4.370*** (+)
3 8 8 8 8
(+) (+) (+) (+) (+)
Notes: (a) The numbers in each cell are the F-statistic associated with the null hypothesis followed by *** and **. (b) The optimal lag length is determined by the Akaike information criterion. (c) (+) indicates that the sum of the coefficients is positive. ** Significance at the 5% level. *** Significance at the 1% level.
models might not capture well the time-varying volatility of inflation. Thus, an EGARCH(1,1) process may be more suitable for describing the errors. 6.2. Estimating GARCH variance series We estimate five conditional variance series using an AR(p)-EGARCH(1,1) model. The results of these equations are reported in Table 5. The a1 parameters are significant for all countries and the b coefficients are significant for all countries except Thailand. The g coefficients, which indicate the asymmetric effects of inflation on uncertainty, are positive and significant at the 1% level or better for all countries. This positive sign indicates that an increase in inflation causes more inflation uncertainty, whereas a decrease in inflation produces less uncertainty. This is consistent with the Friedman–Ball notion of inflation uncertainty. 6.3. Granger causality tests We now turn to the results of the Granger causality tests as specified in Eqs. (5) and (6). The results, shown in Table 6, show bi-directional causality in all cases except Indonesia, where we cannot reject the null hypothesis that inflation uncertainty does not cause inflation. The sum of the coefficients for all causality tests for all countries are positive, indicating that rising inflation raises inflation uncertainty and that rising inflation uncertainty raises inflation. These results are supportive of both the Friedman–Ball and the Cukierman–Meltzer hypotheses. 6.4. Robustness tests Previous studies have shown mixed results based on different measures of inflation uncertainty. We tested the robustness of our above-mentioned results by using three alternative techniques. We used the EGARC-M model, which allows for the conditional variance to give feedback and influence the conditional mean (G–P, 2000). The results are different from the two-step approach reported in Tables 5 and 6. As mentioned before, the simultaneous approach may yield invalid Granger causality tests based on lags. Applying Granger causality tests shows bi-directionality of the relation between inflation and inflation uncertainty for all countries, including Indonesia. We also tried the symmetric GARCH(1,1) and the asymmetric TGARCH(1,1) models to estimate the conditional variance series. Both of these methods produced results that did not pass diagnostic tests on serial correlation. Additionally, the ARCH and GARCH terms were negative for some countries. Next we explored the possibility of structural breaks. The use of the CUSUM and the CUSUMSQ methods, two conventional stability tests, produced multiple breaks for each of the five series. Some of the indicated breaks produced series that were not sufficiently long to use in causality tests. An alternative is to choose a break in the data that is obvious a priori. We used a constant dummy variable in our inflation equations to test for a structural break stemming from the financial crisis. We use a dummy that is 1 from July 1997 to December 2007 and zero otherwise. We find that after the financial crisis there is a decrease in inflation for all countries, except Malaysia. We used these inflation equations to estimate a GARCH variance series. Again we looked at Granger causality tests of our two hypotheses. The results show strong bidirectional causality for all five countries, including Indonesia. We conclude that there is support for bi-directional causality in all of the ASEAN-5 countries. 7. Conclusion This study employs an AR(p)-EGARCH(1,1) model to estimate a conditional variance series for the ASEAN-5 countries. We then use Granger causality tests to test the hypotheses that inflation causes inflation uncertainty and that inflation uncertainty causes inflation over the 1970:01–2007:12 period. The Granger causality tests show strong evidence that
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inflation causes inflation uncertainty for all five countries. We also find that inflation uncertainty affects inflation positively in all countries. Our results have implications for the inflation uncertainty literature and for policy. The results support the notion that an emerging market country with low to moderate inflation (e.g., Singapore and Malaysia) can experience inflation uncertainty. Studies on industrialized countries with low to moderate inflation often have this same result. Additionally, our result that uncertainty causes a positive effect on inflation in all the ASEAN-5 countries can be contrasted with studies on industrialized and emerging market countries that show mixed results. Strong evidence that inflation raises uncertainty about future inflation implies the need for better monetary stabilization. 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