Inflation, inflation uncertainty and output in Tunisia

Hachicha, Ahmed; Lean Hooi Hooi Working Paper Inflation, inflation uncertainty and output in Tunisia Economics Discussion Papers, No. 2013-1 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Hachicha, Ahmed; Lean Hooi Hooi (2013) : Inflation, inflation uncertainty and output in Tunisia, Economics Discussion Papers, No. 2013-1, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/67948 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Licensed under the Creative Commons License - Attribution 3.0 Discussion Paper No. 2013-1 | January 07, 2013 | http://www.economics-ejournal.org/economics/discussionpapers/2013-1 Inflation, Inflation Uncertainty and Output in Tunisia Ahmed Hachicha and Lean Hooi Hooi Abstract This study investigates the relationship between inflation, inflation uncertainty and output in Tunisia using real and nominal data. GARCH-in-mean model with lagged variance equation is employed for the analysis. The result shows that inflation uncertainty has a positive and significant effect on the level of inflation only in the real term. Moreover, inflation uncertainty Grangercauses inflation and economic growth respectively. These results have important implications for the monetary policy in Tunisia. Paper submitted to the special issue Economic Perspectives Challenging Financialization, Inequality and Crises JEL C22 E31 Keywords GARCH-M model; inflation; inflation uncertainty; output Authors Ahmed Hachicha, University of Sfax, Faculty of Economics and Management, Department of Economic Development,Tunisia, Airport road Km 4.5-B.P.1018-Sfax, 3018, [email protected] Lean Hooi Hooi, Economics Program, School of Social Sciences, Universiti Sains, Malaysia,11800 USM, Penang, Malaysia Citation Ahmed Hachicha and Lean Hooi Hooi (2013). Inflation, Inflation Uncertainty and Output in Tunisia. Economics Discussion Papers, No 2013-1, Kiel Institute for the World Economy. http://www.economics-ejournal.org/ economics/discussionpapers/2013-1 2 1. Introduction One of the most valuable subjects in macroeconomics is to treat the effect of inflation on output. Macroeconomists and monetary policy advisors have been convinced that more unstable prices are potentially damaging the economic growth of an economy. This reasonable conviction is inspired from an apperceive hypothesis advanced by Friedman (1977). Empirical studies of the hypothesis have typically pursued one of two approaches of exploration. The former treats the interrelation between the inflation rate and inflation uncertainty (see Cukeirman and Wachtel 1979, Ball and Cecchetti 1990, Evans 1991, Hess and Brunner 1993, Kallon 1994, Grier and Perry 1998, Wilson and Culver 1999, Fountas 2001, Grier et al. 2004, Apergis 2004, Kontonikas 2004, Berument et al. 2011, Hartmann et al. 2012). The latter presents an extension of the relationship by attending the link between inflation uncertainty and output growth (see Grier and Perry 2000, Hayford 2000, Fountas et al. 2002, Fielding 2009, Fountas 2010). The motivation of this paper is from the instability of the inflation rate in Tunisia for the past decades. Tunisia's inflation rate averaged at 5.22 percent and reach an all time high of 16.7 percent in July 1982 and a record low of -1.4 percent in June 1970. In 2009, the Average Inflation was reported at 3.73 percent. Recently, the inflation rate in Tunisia was recorded at 5.60 percent in August 2012. This inflation uncertainty is explained theoretically by the adoption of an expansionary monetary policy marked by decreasing interest rates to lower reserve requirements for the banks (see Azam 1999, Tsangarides 2002, Debrun et al. 2011) 1 . Unfortunately, there is no solid empirical work thus far for the case of Tunisia. The aim of this paper is to empirically explore the relationship between inflation, inflation uncertainty and output by employing the newly developed GARCH in Mean model suggested by Fountas (2010) for the Tunisian case. The remainder of this paper is organized as follows. Section 2 presents the methodology. Section 3 presents the data. Section 4 presents the main empirical results. Finally, concluding remarks are summarized in Section 5. 2. Methodology For the reason that a standard GARCH model does not permit average inflation to affect uncertainty, we adopt GARCH-in-Mean model for our analysis. These models are widely used to investigate the real effects of uncertainty. This method uses the conditional variance of inflation as the measure of inflation uncertainty. We find a great interest to follow Fountas (2010) who modified the model to include the lagged inflation rate in the conditional variance as below: 1 See also Dessus et al. (1998) for a politico-economic model to stabilize Africa. 3 = +∑   +∑ + ℎ   +  (1) = ℎ (2) ℎ = +∑    +∑ℎ +    (3) where π t represents the inflation rate and the conditional variance of  is given by (3). The in-mean coefficient ( γ ) tests the impact of inflation uncertainty on inflation. The effect of inflation on inflation uncertainty is tested by δ . A central bank normally adjusts its money supply growth rate to a change in inflation uncertainty with a time lag. Therefore, inflation is also affected with a time lag following a change in inflation uncertainty. If γ is found not statistically significant, we estimate the GARCH model without the inmean term. We then use the estimated conditional variance of inflation to examine the Granger causality relationships between inflation uncertainty and inflation. The following equation is run to test the lagged effect of inflation uncertainty on inflation: =  +∑   +∑ℎ  +    (4) where ℎ  is the lagged conditional variance of the inflation rate estimated with the GARCH model. Rejection of the null hypothesis that all 0 = j τ implies that inflation uncertainty Granger-causes inflation. On the other hand, to test the effect of inflation uncertainty on economic growth ( t Y), we estimate the following equation: != " +∑"!   +∑#ℎ  +   " (5) Rejection of the null hypothesis that all 0 = j λ implies that inflation uncertainty Grangercauses economic growth. 0p ∑ j λ is consistent with Friedman (1977) who is providing an intuitive argument that higher inflation leads to inflation uncertainty distorting the effectiveness of the price -mechanism in allocating resources efficiently and generating a negative output effect. 3. Data The data comprises quarterly observations from 1988 Q3 to 2011 Q4. All data is obtained from the International Financial Statistics of the International Monetary Fund. We use two sets of variables. The first set is time series includes nominal inflation rate and nominal gross domestic product GDP whilst the second set is real GDP gross domestic product and real inflation rate. The nominal inflation rate is measured by consumer prices index (2005=100) and the nominal gross domestic product is measured by industrial production index (2005=100). 4 All data is transformed to natural logarithm prior the analysis. Figure 1 shows that the inflation rate is more volatile than the economic growth. [Figure 1 here] 4. Empirical Results Table 1 recapitulates respective trend properties. Using two sets of data, one in nominal term and the other in real term, the ADF unit root test results confirm that all variables are I (1). Table 2 summarizes the results of the GARCH-in-Mean estimation by laying out coefficients γ and δ and some diagnostics (the Q-statistics for the residuals and squared residuals). Both γ and δ have positive sign. For the real term model, γ is statistical significant at 10 % level of significance but is not significant for the nominal term model. Similar results are also found in Daal et al. (2005), Fountas (2010), to name a few. However, the Q-statistics show that the in-mean model has the serial correlation problem. The latter could be explained by the presence of asymmetric information between the policymaker and the public and asymmetric stabilization policies (Dematriades, 1988) 2 . Thus, we re-run the model without the in-mean term. All the δ have negative sign but not significant. Hence, we cannot support Friedman (1977) who claimed that a higher inflation rate leads to more uncertainty in the inflation rate. [Table 2 here] Table 3 reports the results of Granger-causality test regarding the effect of inflation uncertainty on inflation and economic growth respectively. We find evidence that inflation uncertainty Granger causes inflation in both models. Inflation uncertainty also Granger causes output growth for the real term model but not for the nominal term model. We also find that j λ ∑ = 0.1264 for the nominal term model and ∑ j λ = - 0.0171 for the real term model. However, both coefficients are not statistically significant. Hence, we are not able to support Friedman's argument that inflation uncertainty has negative real effects. Similar results are also found in Katsimbris (1985), Thornton (1988), Jansen (1989), Levine and Renelt (1992), Levine and Zervos (1993), Bohara and Sauer (1994) and Clark (1997). We deduce that inflation uncertainty affects inflation and output with a time lag. Similar results are also found in Cukierman and Meltzer (1986). They resort to the Barro-Gordon 2 There are conservative, liberal policymakers and public in the economy system. The cost of inflation is abstinent for the liberal policymaker, while it is very high for the conservative policymaker (Ball (1992)). 5 model to prove that a rise in inflation uncertainty will raise the optimal average inflation rate because it provides an incentive to the policymaker to create an inflation surprise in order to stimulate output growth. 5. Concluding Remarks We examine the relationships between inflation, inflation uncertainty and economic growth in Tunisia. Our findings conclude that recession in economy is essentially due to high inflation uncertainty enhanced by lowering the level of the interest rate of the Central Bank of Tunisia. This strategy should find convenient financial reforms as a support, especially after the Tunisian spring revolution, to cover the best conduct of monetary policy eligible to handle the inflation uncertainty level. 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(2002) ‘On Cross‐country Growth and Convergence: Evidence from African and OECD Countries’ Journal of African Economies, 10: 355-389. Wilson, B.K., Culver, S.E. (1999) ‘On measuring the response of real GDP growth to changes in inflation volatility’ Quarterly Journal of Business and Economics, 38: 3–15. 8 Table 1: Unit Root Test for Variables Variable Level First Difference Nominal GDP -0.9974 -4.7330 *** Nominal Inflation -1.5330 -8.4581 *** Real GDP -1.7791 -9.7400 *** Real Inflation -1.5395 -8.4784 *** Note: * ( ** ) *** denote statistical significance at the 10%, 5% and 1% levels respectively. Table 2: Estimated Coefficients and Q-statistics Variables γ δ Q(4) Q 2 (4) Nominal Inflation 0.2149 (0.6818) -0.0029 (0.4869) 1.1077 (0.206) 1.7252 (0.134) Real Inflation 1.9700* (0.0518) -0.0012 (0.8620) 99.219*** (0.0000) 47.050*** (0.0000) Nominal Inflation - -0.0036 (0.4518) 0.6559 (0.242) 1.8633 (0.172) Real Inflation - -0.0036 (0.4414) 0.6540 (0.241) 1.8465 (0.174) Notes: γ is the coefficient of the in-mean effect in the inflation equation. δ is the coefficient on the lagged inflation rate in the conditional variance of inflation equation. The Q-statistics test for serial correlation in the residuals and squared residuals, respectively. P-values are given in brackets. * ( ** ) *** denote statistical significance at the 10%, 5% and 1% levels respectively. Table 3: F-statistic for Granger Causality Tests Model H0 1 H0 2 Nominal Inflation 2.0599* (0.0943) 0.8355 (0.5068) Real Inflation 2.1038* (0.0884) 2.2907* (0.0672) Note: H0 1 : inflation uncertainty does not Granger cause inflation. H0 2 : inflation uncertainty does not Granger cause economic growth. P-values are given in brackets. * ( ** ) *** denote statistical significance at the 10%, 5% and 1% levels respectively.