Pakistan: Selected Issues and Statistical Appendix

This Selected Issues and Statistical Appendix paper presents cross-country regression results that identify investment in physical capital and improvements in institutional quality as having the largest pay-off in terms of increased growth. The paper employs three approaches to forecast inflation in Pakistan. A leading indicator model outperforms a univariate autoregressive moving average model as well as a vector autoregressive model in terms of forecast quality. The paper presents three case studies of Pakistani public sector enterprises that have recently witnessed strong improvements in their financial performance.

Abstract

This Selected Issues and Statistical Appendix paper presents cross-country regression results that identify investment in physical capital and improvements in institutional quality as having the largest pay-off in terms of increased growth. The paper employs three approaches to forecast inflation in Pakistan. A leading indicator model outperforms a univariate autoregressive moving average model as well as a vector autoregressive model in terms of forecast quality. The paper presents three case studies of Pakistani public sector enterprises that have recently witnessed strong improvements in their financial performance.

III. Forecasting Inflation6

33. This section presents three empirical approaches to forecasting inflation in Pakistan. The preferred approach is a leading indicators model in which private sector credit growth leads inflation by 10 months. This model forecasts inflation to increase through the remainder of 2004/05, stabilizing by June 2005. A vector autoregressive model illustrates how monetary developments can be described by a Phillips-curve type relationship and also suggests that inflation will continue to accelerate in the near future. A univariate approach seems less suited to capturing turning points. This section also discusses some implications for monetary policy in Pakistan, including whether inflation targeting could be a feasible strategy.

A. Pakistan’s Monetary Policy Framework

34. Monetary policy in Pakistan is charged with three objectives. According to the State Bank of Pakistan’s (SBP) July 2004 monetary policy statement: Monetary policy “.... will have to ensure that the current growth and investment momentum in the country is not impaired in any significant manner, export competitiveness is maintained while inflation is kept under control.” At times, these objectives can be conflicting and thus difficult to achieve simultaneously using only monetary policy instruments.

35. The SBP has operationalized its objectives as quantitative targets. The inflation target is 5 percent at the moment. However, it seems likely that the target will be exceeded in 2004/05 by up to 2 percentage points, after undershooting the 4 percent target in 2002/03. The SBP tries to smooth excess exchange rate volatility, at times giving the impression of supporting certain psychological thresholds for the Pakistani rupee-U.S. dollar rate. More generally, the SBP looks at competitiveness when assessing the exchange rate. The SBP has also adopted the government’s growth targets of 6.5 percent in 2004/05, and increasing to 8 percent over the medium term.

36. The State Bank of Pakistan uses treasury bill (TB) auctions as the main monetary policy instrument. TB auctions are held every fortnight, with auctions for 6-month maturity alternating with a combined auction for 3- and 12-month maturity. The cut-off rate for 6-month TBs is the SBP’s main policy rate used to manage liquidity. The SBP operates a discount window, but the discount rate has remained unchanged since October 2002 while the 6-month TB rate had fallen by over 400 basispoints to its trough in July 2003. The discount rate has thus been somewhat defunct as a policy rate and has not been raised so far while TB rates have gone up again. Open market operations are scheduled as needed for liquidity management purposes and to support the general monetary policy direction.

37. The SBP does not publish a quantitative inflation forecast. The semiannual monetary policy statement includes an inflation target and discusses prospects for achieving the target. However, no inflation forecast itself is communicated to the public.

B. Toward a More Forward Looking Framework

38. Ongoing financial deepening changes the environment for monetary policy. The SBP has moved away from targeting monetary aggregates such as reserve money and net domestic assets (NDA). In the past few years, NDA targets agreed under the Fund program were not effective in controlling reserve money growth because of the strong net foreign asset accumulation that continued to outperform projections. Instead, the SBP has relied increasingly on short-term interest rates to achieve its objectives. With steady improvements in financial intermediation and continued financial deepening, the credit channel should become more effective, strengthening short-term interest rates as the main policy instruments. Our finding below that private sector credit growth is a good leading indicator for inflation is evidence that the credit channel is part of the monetary transmission mechanism in Pakistan.

39. Ideally, a quantitative forecasting framework is needed to support policy setting. Forecasts of major economic aggregates, in particular inflation and growth, can provide a sense of whether the SBP is set to achieve its objectives. This information could then feed into the policy setting process to ensure that objectives are indeed met. Given the typical time-lags of monetary policy, forecasts are valuable sources of information to adjust policies early on. In light of the SBP’s possibly conflicting objectives, forecasts can also illustrate possible trade-offs. Of course, the SBP could also decide to adopt formal inflation targeting, and use an inflation forecast as an intermediate target.

40. There are three main challenges that a forecasting model has to address:

  • Ongoing changes in Pakistan’s financial system such as financial deepening imply that simple standard relationships such as money demand functions may not be stable at the end of the sample period. Thus, small models may suffer from nonconstant parameters, which affects the model’s forecast quality, or they may even result in estimated coefficients that are contrary to economic reasoning.7

  • Only a few, mostly monetary, variables are available on a monthly or quarterly basis. GDP, for example, is available only annually, though quarterly national accounts are under construction.

    Pakistan’s data is not only subject to Gregorian calendar, but also to Islamic calendar effects.8 Several standard techniques are available to address Gregorian calendar seasonality. However, only little work has been done to address Islamic calendar effects that cannot be controlled for by standard techniques which are calendar year-based because the Islamic year is shorter than the calendar year.9

Figure III.1
Figure III.1

Monetary Developments

Citation: IMF Staff Country Reports 2004, 415; 10.5089/9781451830644.002.A003

Source: National authorities; and Fund staff calculations.

C. Related Literature

41. A large number of empirical studies is available that look at inflation and monetary policy relationships in Pakistan. Some studies are based on samples going back as far as the 1950s, but most start in 1972, using either annual or constructed quarterly data. Most studies use either cointegration techniques or estimate vector autoregressive models (often in first differences). All studies are in the business of model building and none attempts to use their results for forecasting. Table III.1 provides a selective survey of the literature.

Table III.1.

Pakistan: Empirical Studies of Inflation and Monetary Policy

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42. Most empirical studies find standard economic relationships to hold. Estimates of money demand functions mostly find money demand to be determined by measures of opportunity costs and activity (e.g., Tariq and others, 1997). Likewise, inflation is influenced by changes in money supply, interest rates, measures of aggregate demand or output, and import prices (e.g., Ahmad and Ali, 1999a). While most studies find such relationships to hold in a cointegration framework, a few fail to find cointegration which could suggest structural breaks in particular samples (e.g., Shamsuddin and Holmes, 1997). There seems to be no or only little exchange rate pass-through to domestic prices (e.g., Choudhri and Khan, 2002).

D. Three Quantitative Approaches

43. We use three empirical approaches to forecasting inflation. As a benchmark, we estimate a univariate autoregressive moving-average model (ARMA). Next, we use a vector-autoregressive (VAR) model that includes several variables based on an economic model. And finally, we use a leading indicators model (LIM), also based on several explanatory variables, but less concerned with mirroring an economic model. We find the LIM to be best suited for forecasting in terms of statistical properties and measures of forecast accuracy. However, once longer time-series become available, we believe that an economic model-based VAR could allow more in-depth policy analysis.

The data

44. The database includes mostly monetary and financial data available at monthly frequency. We restrict the analysis to monthly data because this is available with much shorter lags and thus more suitable for a continuous forecasting exercise. However, this implies that we cannot use variables such as GDP because national accounts are compiled only on a fiscal year basis. As such, data is restricted to monetary aggregates, interest rates, the exchange rate, and inflation. In addition, we use the monthly large-scale manufacturing index to proxy activity. Table III.2 presents descriptive statistics for the core variables in our database.

Table III.2.

Pakistan: Descriptive Statistics of Core Variables Used

(Average annual change in percent, unless otherwise indicated)

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Sources: Pakistani authorities; and Fund staff calculations.

Defined as 12-month TB rate less 3-month TB rate.

45. The sample is restricted to July 1998 onwards. This starting point was chosen to exclude observations before the 1998/99 crisis after which the exchange rate was liberalized substantially. A casual look at the data supports this cut-off date as inflation appears to be much more stable since the crisis. Truncating the sample in 1998 has the added advantage that the recent fundamental changes in the financial system would be better reflected in the estimated coefficients which should contribute to better forecasts. However, nonconstant coefficients remain a problem for at least one of our approaches, though we did not try techniques that allowed for time-varying coefficients because this would further strain the already small sample size.10 At the time of estimation, the latest available observation was June 2004 for most variables which leaves a fairly short sample.

46. We address seasonality by using 12-month moving averages except in the ARMA approach. Using average annual inflation as well as 12-month averages for possible regressors in the VAR and the LIM smoothes out calendar year effects. In addition, averaging should also smooth out Islamic calendar effects, except for the rare case where, for example, two Eids would fall into one calendar year. For the ARMA, we find that taking 12-month moving averages filters out too much of the variation in the data, resulting in a poorly specified model that does not fit the data well. However, using unfiltered monthly data yields a satisfactory ARMA specification.

Figure III.2.
Figure III.2.

CPI Inflation

Citation: IMF Staff Country Reports 2004, 415; 10.5089/9781451830644.002.A003

Source: National authorities; and Fund staff calculations.

47. Most core variables in the database are nonstationary in levels (Table III.3).11 In our sample range, the consumer price index (CPI), broad money, credit to the private sector, the six-month TB rate and the output gap12 are integrated of order one based on augmented Dickey-Fuller tests. However, reserve money and the large scale manufacturing index are stationary. Inflation is found to be integrated of order two. While this is not unusual, it seems somewhat at odds with the finding that the CPI is integrated of order one. Moreover, a graphical inspection of the inflation series casts some doubt on this result which may be driven by the fact that nonstationarity tests are biased toward nonrejection in small samples.

Table III.3.

Pakistan: Test for Nonstationarity of Core Variables 1/

article image
Sources: Pakistani authorities; and Fund staff calculations.

Augmented Dickey Fuller test. Model includes intercept and trend. Sample range is 1998:7 through 2004:6 where available.

The critical value at the 5 percent significance level is -3.5.

A Univariate Model

48. In the simplest form, inflation can be modeled as an ARMA process. We determine the optimal lag length according to the Box-Jenkins methodology, significance tests, and statistics measuring the forecast quality such as the root square mean error. Since inflation is integrated of order one according to unit root tests, we should difference inflation as part of the ARMA approach. However, the estimated model does poorly. Therefore, we estimate the model without differencing inflation—based on the finding that the CPI is integrated of order one and a visual inspection of the data which suggests that inflation may be stationary—and achieve better results.13

49. The preferred specification is an ARMA (5,3) model that replicate the sine-type trajectory inflation has followed in the past (Table III.4). We reestimate the model for a reduced sample through June 2003 and use this reestimated model to predict inflation for the period July 2003 through June 2004 for which we also have actual data, allowing an assessment of the models predictive power. The in-sample forecast fails to fully anticipate the acceleration of inflation in 2003/04. However, none of the other ARMA specifications yielded a better in-sample forecast accuracy.

Table III.4.

Pakistan: Econometric Results of the ARMA Model

article image
Sources: Pakistani authorities; and Fund staff estimates.

50. The ARMA (5,3) predicts a sharp slowdown in 12-month inflation starting in July 2004. However, ARMA forecasts have difficulties capturing turning points because they use only information on past values of inflation and do not use any information on shocks that would trigger turning points. Instead, the ARMA model extrapolates the sine-type trajectory which inflation has followed in the past. The out-of-sample forecast then suggests that typically, inflation would be expected to come down soon if the average cycle length realized in the past continues to hold. However, more information on what is driving this sine-type behavior is needed to firm up such a forecast.

Figure III.3
Figure III.3

In-Sample and Out-of-Sample Forecasts with the ARMA

Citation: IMF Staff Country Reports 2004, 415; 10.5089/9781451830644.002.A003

Source: National authorities; and Fund staff projections.1/ Forecast based on model for reduced sample through March 2004.Source: National authorities; and Fund staff projections.

An Unrestricted Vector Autoregressive Model

51. A VAR allows a more model-based approach that should be better able to identify shocks that may trigger turning points in inflation. With nonstationary variables, the VAR can be specified as a vector error correction model in levels that separates long-run and short-run relationships. However, we failed to find cointegration in various specifications which is likely to reflect the fairly short sample span that does not provide sufficient information on long-run relations as well as the structural changes taking place in the financial system. Alternatively, we specify a VAR in first differences that describes short-run relationships.14 The widely used Phillips curve provides the theoretical starting point. Parameter restrictions would be required to make the VAR truly model-based. However, for now, we have only estimated an unrestricted VAR. The VAR’s lag length is selected based on standard information criteria and tests for normality of the error terms; the information criteria suggest a lag length of one, but we set the lag length at three to ensure that the residuals are white noise.

52. The estimation results are shaky, but provide some insights. Our preferred specification is a VAR including inflation, a real interest rate (defined as the 3-month TB rate less expected inflation from an ARMA model), and the output gap. In this specification, inflation is low when the output gap is large (i.e., the economy is below potential) or when the real interest rate is high (see Table III.5). However, there is no feedback between output gap or real interest rate in either direction. The estimated output gap equation does not fit the data well. Reestimating the model for a reduced sample through March 2004 and comparing the forecast with actual data, shows that the VAR forecast does not capture the accelerating trend of inflation, though actual inflation is within the wide 2-sigma band.

Table III.5.

Pakistan: Econometric Results of the VAR Model

article image
Sources: Pakistani authorities; and Fund staff estimates.

Annual average inflation.

The real interest rate is defined as the nominal 3-month TB rate less expected inflation, where inflation expectations are based on an ARMA model.

53. The VAR predicts an acceleration of average annual inflation to 6½ percent by March 2005. At the same time, the VAR predicts the output gap to remain almost constant while the real interest rate increases notably. One interpretation of this forecast could be that a tightening of monetary policy successfully reins in inflation without affecting the output gap. More technically, the unchanged output gap is the result of the poor fit in that equation.

Figure III.4
Figure III.4

In-Sample and Out-of-Sample Forecasts with the VAR

Citation: IMF Staff Country Reports 2004, 415; 10.5089/9781451830644.002.A003

Source: National authorities; and Fund staff calculations.1/ Forecast based on model for reduced sample through March 2004.Source: National authorities; and Fund staff projections.

54. As typical economic relationships are firming up in the data, the VAR approach should become a useful tool to forecast and analyze inflation trends. At present, not enough data is available to estimate a structural VAR with sufficient precision. Moreover, structural changes in the financial system result in nonconstant coefficients which make forecasting problematic. However, after 2001, Phillips curve-type relations are found in the data. If these relations were to firm up going forward, a structural VAR that reflects an economic model should provide a powerful tool for forecasting inflation and analyzing monetary policy.

A Leading Indicators Model

55. The leading indicators approach searches for variables that co-move with the variable to be forecasted without imposing a model structure. Leading indicators do not necessarily need to be causal factors of the target variable as part of an economic model, though this would presumably strengthen one’s confidence in a forecasting model (e.g., Marcellino, 2004, and Stock and Watson, 1989 and 1999). We use the general-to-specific algorithm in PcGets to narrow down the set of possible leading indicators from our full dataset and then use the same criteria of forecast accuracy as for the ARMA to arrive at a final specification (see Hendry and Krolzig, 2004). We require indicators to lead inflation by at least 6 months and allow for leads up to 12 months.

56. Private sector credit growth is a good leading indicator of inflation. Higher private sector credit leads an acceleration of inflation by 10 months (see Table III.6) which is consistent with a monetary transmission mechanism that works through the credit channel. All other variables drop out of the specification process, including broad money growth, reserve money growth or the output gap which did not add to the model’s forecast quality. The ex-post in-sample forecast based on a model reestimated for a reduced sample through June 2003 and forecasted through June 2004 tracks the actual inflation development quite well, with inflation staying within a fairly narrow two-sigma band. Using the model estimated through June 2004 to forecast out-of-sample shows average annual inflation to increase steadily though with a slightly declining trend up to 9 percent in March 2005.

Table III.6.

Pakistan: Econometric Results of the Leading Indicators Model

article image
Sources: Pakistani authorities; and Fund staff estimates.
Figure III.5
Figure III.5

In-Sample and Out-of-Sample Forecasts with the LIM

Citation: IMF Staff Country Reports 2004, 415; 10.5089/9781451830644.002.A003

Source: National authorities; and Fund staff projections.1/ Forecast based on model for reduced sample through December 2003.Source: National authorities; and Fund staff projections.

57. The leading indicators model yields a fairly accurate forecast, but is not grounded in an economic model. By construction, the approach picks a leading indicator that yields a high forecast accuracy at the current juncture. Moreover, higher private sector credit growth being associated with higher inflation seems plausible from an economic point of view (credit channel). However, the choice of leading indicators may change over time, so that the forecasting model may not be stable. As such, periodic respecification and reestimating would be required. For example, since we specified the model, two additional months of data have become available. Reestimating the model for the extended sample leaves the coefficient estimates fairly unchanged. The projection from this reestimated model has inflation stabilize about 10 percent by June 2005.

Figure III.6
Figure III.6

Updated Out-of-Sample Forecast with the LIM

Citation: IMF Staff Country Reports 2004, 415; 10.5089/9781451830644.002.A003

Source: National authorities; and Fund staff projections.1/ LIM re-estimated through August 2004; projections through June 2005.

E. Inflation Targeting

58. Inflation targeting is a monetary policy strategy based on five elements (e.g., Carare and others, 2002, Croce/Khan, 2000, and Mishkin, 2000): (a) the public announcement of a quantitative inflation target for the medium- to long-term; (b) an institutional commitment to price stability as the primary objective of monetary policy to which all other objectives are subordinated; (c) use of a wide set of variables and information to set monetary policy instruments; (d) transparent communication with the public and markets, explaining monetary policy objectives and decisions; and (e) accountability of the central bank for achieving the inflation target.

59. Pakistan’s monetary policy contains some of these elements. An annual inflation target is publicly announced, and the SBP explains its past and future actions in the semiannual monetary policy statement, as well as quarterly and annual reports. However, price stability is not the SBP’s only objective, and growth and exchange rate stability are not always subordinated to the inflation target.

60. If Pakistan wanted to adopt inflation targeting, the medium-term inflation target would need to be made the primary objective of monetary policy. While the SBP has an inflation target even now, it cannot pursue inflation targeting as long as there are two other possibly conflicting objectives, growth and the exchange rate. This is a political decision that needs to weigh the advantages of inflation targeting against the question of what role monetary policy can play to support growth and the rationale behind a fear of floating’ (e.g., Calvo and Reinhart, 2000, and Hausman and others, 2000).15

61. Using an inflation forecast as an intermediate target is, however, not yet possible. As illustrated above, empirical relationships do not appear to be firm enough, yet, to establish a forecast as the intermediate target and fine-tune monetary policy in response to an inflation forecast that deviates from the inflation target. However, inflation forecasts can inform monetary policy and give an indication whether a particular target is likely to be achieved. The forecasting models presented above can serve such a purpose and provide at least qualitative input for setting monetary policy instruments.

F. Conclusions

62. Pakistan’s economic data permits quantitative forecasts of inflation. Using monthly data since mid-1998, we have presented three approaches to forecasting inflation. At present, we consider the LIM most appropriate to arrive at a quantitative inflation forecast. However, over time as economic relationships firm up, a structural VAR approach should yield a richer forecast that will also allow an analysis of the impact of monetary policy instruments.

63. Inflation forecasts with the LIM as of August 2004 suggest that monetary policy needs to be tightened. The LIM estimated through August 2004 predicts annual average inflation to stabilize about 10 percent by June 2005, significantly higher than the SBP’s 5 percent target for 2004/05. In fact, on current trends, this target is not likely to be achieved in 2004/05. Thus, monetary policy should be geared toward reversing the acceleration of inflation, and possibly returning 12-month inflation to 5 percent by the end of 2004/05.

64. The LIM can also give some guidance on an intermediate target. The LIM is not a structural model. Therefore, strictly speaking, the LIM does not allow inference on what needs to be done to achieve the SBP’s inflation target. Nonetheless, stretching the limits of the model, the LIM can be inverted to show that slowing down credit growth to about 18–20 percent (from 31 percent in August 2004) over the next six months, would possibly lead to a decline of average annual inflation to 5 percent by December 2005. Such an exercise should be treated with caution, but may give some guidance for monetary policy.

65. Inflation targeting is a policy option for the SBP. However, if the SBP were to adopt inflation targeting, inflation would need to be made the primary objective of monetary policy. This could facilitate the conduct of monetary policy compared to the current regime in which the SBP has three potentially conflicting objectives—inflation, growth, and the exchange rate. Empirical relationships do not appear firm enough to allow using an inflation forecast as an intermediate target of monetary policy. Still, quantitative inflation forecasts would provide important information in an inflation targeting policy framework.

66. The models presented here can be developed further. In part, this will require longer time series, but also some stabilization in the rapidly developing financial system to ensure parameter stability. Given the data limitations, our econometric techniques were also constrained, and we look forward to future refinements. In the meantime, we put our LIM forecasts to the test of time.

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6

Prepared by Madhavi Bokil and Axel Schimmelpfennig.

7

We explore this in Bokil/Schimmelpfennig (2004).

8

Prices tend to increase during Ramadan and the Eids (religious holidays).

9

A notable exceptions is Riazuddin and Khan, 2002.

10

In principle, it would be possible to account for the structural break in 1989/99 by including intercept and slope dummies for observations after 1989/99. When allowing different slopes for all variables in the period after 1998/99, this is approach is almost equivalent to simply restricting the sample as we do. Moreover, attempts at estimating a vector error correction model for a longer sample failed to find cointegration, suggesting that there is no stable long run relationship over the 1990-2004 period. As such, we believe that the more recent observations contain more relevant information for the purpose of forecasting and restrict ourselves to the 1998/99 and beyond sample.

11

The detailed test results can be found in Bokil and Schimmelpfennig, 2004.

12

We calculate the output gap as the difference of the large scale manufacturing index from its long-run HP-filtered trend in percent of the trend.

13

The results for an ARIMA based on first-differenced data can be found in Bokil and Schimmelpfennig, 2004. While they are not fundamentally different, the forecast quality of the model for inflation is somewhat worse.

14

Thus, we either deviate from our assumption that inflation is stationary and assume that it is indeed integrated of order one, or—in terms of long-run relationship—we assume that the other variables in the VAR (which are integrated of order one) are cointegrated and that cointegrating vector is related to inflation. By differencing inflation, we, of course, run the risk of over-differencing the model.

15

Fatas and others, 2004, find strong empirical support that having a quantitative target for monetary policy significantly lowers inflation.

Pakistan: Selected Issues and Statistical Appendix
Author: International Monetary Fund