This section conducts the robustness check of the relationship between inflation bias indicators and the real growth. The conduct of this exercise in a conventional way of bifurcating the sample in this particular case does not seem appropriate. The sample size is not sufficiently large to split into two equal parts while allowing the dynamics to be sufficiently accounted for up to 3 lags. To overcome this issue, only the activist monetary policy period, which spreads over the larger part of the data (from 1971 till 2010) was examined.
Pakistan‘s monetary policy can be divided into two main phases: the first phase from 1960-1970 can be characterized as a moderate monetary policy and the second, from 1971-2010 as monetary activism. In the first phase, the monetary policy remained moderate as the average M2 growth stood at 11.33% (Table 4.5). The overall economic performance in this decade was commendable. The average real growth rate remained high whilst the average inflation remained low and stable. The second phase started after the 1971, where there is a shift in the monetary policy approach from moderate to monetary activism. On average, the M2 growth rates for this period have been raised to 15.45%, resulting in high inflation and relatively lower average real growth rates.
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Table 4.5: Monetary policy shift in Pakistan from moderate to monetary activism
Period M2 growth Inflation Real growth
1961-1970 11.33 3.51 7.24 1971-1980 16.98 12.42 4.72 1981-1990 13.29 6.98 6.29 1991-2000 16.18 9.25 3.96 2001-2010 15.34 8.92 4.63 1971-2010 15.45 9.39 4.90
Source: World Development Indicators (WDI) and author's calculations
The initial two years of 1971 and 1972 were excluded from the analysis to eliminate the potential effect of Pakistan‘s war with India in 1971. This war badly affected the real growth rates in Pakistan as on average a growth rate of 0.64% was witnessed for the years 1971 and 1972. The country also experienced an all-time high average inflation rate of around 24% from 1973 to 1975, due to international oil price shocks and domestic floods in that period.90
To test for the existence of cointegration, the null and alternative hypotheses were
formulated as against the alternative
. The SBC model selection criterion was used for the selection of
optimal lags by imposing a maximum lag of 3. The F-stat for the four regressions on the
basis of , , and are 8.24, 8.46, 8.22 and 7.71, respectively. All these F-
statistics are greater than the corresponding asymptotic critical values at the 1% level both
for Pesaran et al. (2001) and Narayan (2005). This confirmed the presence of cointegration
90 To account for the potential impact of this period, a dummy variable was included, which was dropped subsequently due to its insignificance. The joint test of zero restrictions on the coefficient of this variable also revealed that it should be dropped from all the individual models containing the proposed inflation bias indicators. For example, the P-values of the LM test for the dummies in the models with , , and
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and hence the long-term parameter estimates were obtained. Before obtaining the long-term estimates, it was nevertheless made sure that the models are stable (Figure 4.3).
The results (Table 4.6) for the sub-period 1973-2010 confirm a significant long- term negative relationship between all the inflation bias indicators and the real growth at
the 1% level of statistical significance. For this period, the inflation bias indicators (
and ) are also significant and their effect is quantitatively larger as compared to the
effect of the and . This implies that the severity of the adverse effects of inflation
bias on real growth increases, the more the inflation departs from the optimal and desirable
levels. For example, for , a 1% increase in inflation bias reduces the real growth by
0.05%, whereas for the corresponding reversal in the real growth is 1.21%. This result
also suggests that the higher the average inflation bias the higher are the adverse effects on the real growth. For example, the average inflation bias computed from the observed
inflation i.e. is 8.87% and for is10.27%. For this period the fit of the data
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Table 4.6: Long-term parameter estimates of the proposed inflation bias indicators (1973-2010)
Models /Variables
Variables Fit of the models and the diagnostic tests
̂ ̂
AUTO SPEC NORM HETR
Model 1 (IB1) -0.06*** 0.99 0.14*** 23.00 4.21 (0.01) (0.64) (0.05) (24.26) (2.10) 0.50 [0.36] [0.66] [0.61] [0.65] [0.00] [0.13] [0.00] [0.35] [0.05] Model 2 (IB2) -0.13*** 0.99 0.14*** 22.99 3.95 (0.04) (0.64) (0.05) (24.26) (2.08) 0.50 [0.35] [0.66] [0.61] [0.65] [0.00] [0.13] [0.00] [0.35] [0.06] Model 3 (IB3) -0.20*** 0.99 0.14*** 22.95 3.69 (0.06) (0.64) (0.05) (24.25) (2.06) 0.49 [0.35] [0.66] [0.61] [0.65] [0.00] [0.13] [0.00] [0.35] [0.08] Model 4 (IB4) -1.21*** 0.80 0.14** 18.22 3.87 (0.39) (0.65) (0.05) (24.41) (2.08) 0.48 [0.48] [0.72] [0.58] [0.67] [0.00] [0.22] [0.01] [0.46] [0.07]
This Table reports the cointegrating relationship of the real GDP with inflation bias indicators for the sub period. The P-values of the diagnostic tests are presented sequentially with AUTO denoting the Langrange Multiplier test for Autocorrelation. The SPEC represents a general test for omitted variables and functional form test – Ramsey regression equation specification error test (RESET) test using the square of the fitted values. NORM indicates the test for normality based on a test of skewness and kurtosis of residuals. HETR represents the Heteroscedasticity test based on the regression of squared residuals on squared fitted values. The P-values reported for diagnostic tests are based on F-test except NORM, which uses LM version. All the P-values are given in the brackets and the values in parentheses are the standard errors. The significance level of the coefficients at 1%, 5% and 10% are indicated by ***, ** and *, respectively.
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Figure 4.3: Stability tests
Model 1- Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -5 -10 -15 -20 0 5 10 15 20 1973 1978 1983 1988 1993 1998 2003 2008
Model 1- Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -0.5 0.0 0.5 1.0 1.5 1973 1978 1983 1988 1993 1998 2003 2008
Model 2- Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -5 -10 -15 -20 0 5 10 15 20 1973 1978 1983 1988 1993 1998 2003 2008
Model 2- Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -0.5 0.0 0.5 1.0 1.5 1973 1978 1983 1988 1993 1998 2003 2008
Model 3- Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -5 -10 -15 -20 0 5 10 15 20 1973 1978 1983 1988 1993 1998 2003 2008
Model 3- Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -0.5 0.0 0.5 1.0 1.5 1973 1978 1983 1988 1993 1998 2003 2008
Model 4- Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -5 -10 -15 -20 0 5 10 15 20 1973 1978 1983 1988 1993 1998 2003 2008
Model 4- Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level -0.5 0.0 0.5 1.0 1.5 1973 1978 1983 1988 1993 1998 2003 2008
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4.6CONCLUSION
This study posits that empirical investigation into the extent of the effectiveness of inflation bias per se to stabilize the real growth is crucial for two reasons. First, it may help determine the scope of monetary policy as an inflation or growth-stabilizer. Second, it may augment the decision in favor of or against discretion compared to commitment as an inflation or growth-stabilizer. Nevertheless, probe into the extent of the effectiveness of inflation bias, in the first place, requires the generation of its indicators. This study proposes a framework for generation of inflation bias indicators for the discretionary monetary policy strategy of Pakistan – an ideal case for the analysis of inflation bias. These indicators have been then used to empirically investigate the extent of the long-term effectiveness of inflation bias in enhancing the real growth through ARDL approach while using asymptotic critical values both from
Pesaran et al. (2001) and Narayan (2005).
The estimates show that inflation bias is significantly detrimental to the real growth in the long-run. The higher the average inflation bias the higher are its adverse effects on the real growth. These results are consistent with Kydland and Prescott (1977) and Barro and Gordon (1983) as it confirms that in the long-run, the inflation bias does not help boost the real activity. It is also unveiled that not only inflation bias is ineffective in boosting the real growth in the long-run but it is significantly detrimental to it. In the short-run the inflation bias may or may not have a positive effect on the real growth, however, to be certain about it and to ascertain its magnitude requires separate research, which is beyond the scope of this study.
These results imply that the scope of discretion in enhancing the real growth in the long-run is not only limited but counterproductive, particularly, in terms of its very
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objectives to stabilize inflation and real growth. Therefore, discretion may not be preferred over commitment as an inflation or growth-stabilizer because it accommodates inflation bias to stabilize the real growth, but inflation bias instead destabilizes it.
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