The paper discusses potential output, the output gap, and inflation in Korea. The paper explores the information content of potential leading indicators of inflation. A broadly balanced current account has been the suggested norm for Korea over the medium term. The challenge is to help build a more robust bond market that prices risk appropriately. The features of pension schemes in Korea and the problems they face are outlined. The paper reviews pension reform, banking sector, corporate sector, and foreign exchange crises with respect to Korea.

Abstract

The paper discusses potential output, the output gap, and inflation in Korea. The paper explores the information content of potential leading indicators of inflation. A broadly balanced current account has been the suggested norm for Korea over the medium term. The challenge is to help build a more robust bond market that prices risk appropriately. The features of pension schemes in Korea and the problems they face are outlined. The paper reviews pension reform, banking sector, corporate sector, and foreign exchange crises with respect to Korea.

II. The Information Content of Inflation Indicators in Korea1

The Bank of Korea adopted inflation targeting in 1998, and the monetary authorities use a wide array of information to guide monetary policy. This chapter explores the information content of potential leading indicators of inflation, using Granger causality tests and bivariate and multivariate vector autoregressions. M3 appears consistently useful in the various tests. Other promising indicators are measures of economic slack (based on industrial production and unemployment) and measures of cost-push pressures (the nominal effective exchange rate and unit labor costs).

A. Introduction

1. With the revised Bank of Korea Act (1997), Korea joins the growing list of countries that have adopted inflation targeting (IT). The revised act enshrines price stability as the “primary goal” of monetary policy, and accordingly:

  • requires the Bank of Korea (BOK) each year to set an inflation target in consultation with the government and to formulate and publish an operational plan for monetary and credit policies to meet this target;2

  • guarantees the independence of the BOK, neutrality in the establishment of monetary policy, and autonomy in implementation; and

  • requires the BOK to publish the minutes of the Monetary Policy Committee meetings, and prepare a detailed report on monetary policy for the National Assembly.

2. The move to inflation targeting marks a major switch in Korea’s monetarypolicy framework. (Box II.1 surveys the BOK’s inflation targeting framework and some of the recent research on inflation and monetary policy in Korea.) The central feature of a monetary regime is a nominal anchor. Some countries (most prominently the United States) have gone without an explicit nominal anchor but have nevertheless achieved excellent macroeconomic performance (Mishkin, 1999). Those that have explicitly adopted nominal anchors have usually targeted exchange rates, monetary aggregates, or the inflation rate itself. In Korea’s case, from 1957 to 1998, the BOK targeted a succession of monetary aggregates, but as the movements of a particular designated aggregate grew harder to interpret, the BOK had to keep switching its target to another aggregate (Kim and Kim, 1999).

Inflation Targeting in Korea

Since the introduction of the revised Bank of Korea Act (1997), Korea has made significant progress in operating an inflation targeting framework. This box reviews the key features of the current monetary policy framework and several research findings.

Institutional framework

  • Legal framework. The revised Act establishes price stability as a central objective and ensures the BOK’s instrument independence.

  • Definition of the inflation target. The headline CPI can be seriously affected by temporary supply-side shocks, such as oil price increases or natural disasters. Hence, the BOK targets the core inflation rate, which it has defined as headline inflation excluding the price changes of selected petroleum products and non-cereal agricultural products. These items were found to have exceptionally volatile price movements (BOK, 2000).

  • Target range. In both 2000 and 2001, a range of 1 percent above and below the announced target was established. This practice is also preferred by most countries that practice inflation targeting, given uncertainties associated with hitting targets.

  • Target horizon. As is typical for countries with low inflation, Korea has announced a medium-term target (2.5 percent) for core inflation. However, the revised Act also provides for the announcement of an annual target. In view of Korea’s circumstances, there should ideally be only a medium-term target with an indefinite horizon.

Operational issues

  • Inflation forecasting. Hoffmaister (1999) had concluded that inflation forecasting was highly feasible in Korea. The BOK has good technical capacity to implement inflation forecasting. Currently, the BOK’s inflation forecasting framework relies on a suite of macroeconometric models of varying sizes and complexity. In June, the Monetary Policy Committee (MPC) receives a forecast for the second semester, and an informal projection for the following year. Forecasts are updated four times a year, but also more frequently if needed, and are supplemented with information from other economic indicators and informal surveys of inflation expectations.

  • Policy implementation. The BOK implements monetary policy through changes in the overnight call rate. In turn, the call rate is set through open market transactions involving repos or monetary stabilization bonds. These practices are consistent with empirical research (Oh, 2000) which finds that: (1) the call rate has unilateral causality over long-term interest rates and real economic variables; and (2) the repo rate has unilateral causality over the call rate.

  • Transmission mechanism. Kim (2000) and Oh (2000) concur that an increase in the overnight call rate begins to affect the rate of inflation in the third-to-fourth quarter after the increase, reaching maximum impact in eight to nine quarters. However, there are concerns that the interest rate channel is still weak, due to the narrowness and current fragility of financial markets. Most recently, financial instability arising from the problems of the ITCs and troubled conglomerates has muted the signaling effect of interest rates in the bond markets.

Organizational issues

  • Accountability and transparency. Decisions reached in the monthly meetings of the MPC are widely disseminated, and an inflation report is submitted twice yearly to the National Assembly. The MPC’s minutes are published with a 3-month lag. However, coverage in the press of inflation developments seems to indicate that the concept of core inflation and the definition of the target (period coverage and target horizon) are not yet well understood by the public.

3. Under IT, monetary authorities use all available information to determine what policy actions are required to achieve the inflation target (Mishkin, 1999; Svenson, 2000). By contrast, monetary targeting and exchange rate targeting can be seen as “rule-like” strategies that rely on targeting a variable (e.g., monetary aggregates or exchange rates) that has a causal relationship with inflation, and which the monetary authorities can influence. Baumgartner and Ramaswamy (1996) argue that the reliance on many indicators under IT, which they call an “information variable approach,” is an advantage, especially when (a) an economy is subject to large structural changes, and variables that used to predict inflation become less informative, or (b) there is no consensus on the structural properties of the monetary transmission mechanism. Furthermore, as opposed to an intermediate targeting approach, the monetary authorities under information variable approach would aim to extract information even from nonfinancial indicators. A full suite of analytical and forecasting tools, such as models of the monetary transmission mechanism and short-term forecasting models would be helpful, but it can be argued that the minimum technical framework for operating an IT regime is somewhat less demanding. Or, as Baumgartner and Ramaswamy (1996) put it, “a full understanding of the structural features of the transmission mechanism is not a necessary condition for targeting inflation successfully. Instead the need is for identifying a set of indicators that contain information on future inflation.”3

4. This chapter explores the information content on future inflation of several potential indicators, for the period January 1990-June 2000, using methods from existing empirical work on the subject.4 First, Granger causality tests are conducted as an initial test of promising leading indicators. Second, nonstructural bivariate vector autoregressions (VARs) are estimated to gauge the predictive power of the variables. Finally, multivariate VARs are estimated, which include more than one of the candidate indicators, to test the robustness of the bivariate tests and to allow for feedbacks among the different sets of indicators.

B. Inflation Indicators

5. This study employs the following indicators:5

  • Monetary and credit aggregates: M1, M3, and credit to the private sector.

  • Measures of economic activity and/or slack: real GDP growth, the output gap6, capacity utilization, and the unemployment gap (i.e., the difference between unemployment and the NAIRU, or nonaccelerating inflation rate of unemployment).

  • Domestic cost-push factors: wages and unit labor cost (ULC).

  • External cost-push factors: the nominal effective exchange rate and import prices.

  • Asset prices: stock market prices and the slope of the yield curve.7

The above list has much in common with the set of variables that the BOK currently follows, namely: the output gap, capacity utilization ratios, unemployment, yield curve, M3, stock prices, real estate prices, exchange rates, and import prices.8

C. Granger Causality Tests

6. Granger causality tests essentially use F-statistics to test whether lags of the candidate variable are significant in explaining inflation. Although they do not provide information on the structural relationships between inflation and the variables tested, they provide information on the leading indicator properties of those variables. The results, shown in Table II.1, indicate that:

  • the growth rate of credit to the private sector, industrial production, M3, the nominal effective exchange rate, and unit labor costs “Granger-cause” inflation;

  • there is bidirectional Granger causality between credit and inflation; and

  • inflation “Granger-causes” Ml and the unemployment gap.

Table II.1:

Korea: Granger Causality Tests

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indicates rejection of the null hypothesis at the 5 percent confidence level.

indicates rejection of the null hypothesis at the 1 percent confidence level.

All equations were estimated with 6 lags.

D. Bivariate VARs

7. Turning to the results of VAR estimates, the general strategy followed in identifying promising leading indicators of inflation consists of: (a) examining the variance decomposition of inflation to spot the variables that have substantial predictive power; and (b) checking that the predictive power of the variable in question is strong at least four quarters after the start of the simulation period. This cutoff was chosen because existing studies of the lag of monetary policy in Korea (Kim, 2000; Oh, 2000) concur that an increase in the overnight call rate begins to affect the rate of inflation in the third-to-fourth quarter after the increase, reaching maximum impact in eight to nine quarters.

8. Simple bivariate systems were estimated, with each system consisting of the year-on-year rate of CPI inflation and the year-on-year-rate of change of a potential leading indicator—except for the output and unemployment gap measures and the slope of the yield curve, where levels were used. Each system also included a dummy variable that takes the value of 1 from November 1997 onward, to incorporate the impact of structural changes that may have taken place since the outbreak of the financial crisis. Lag lengths were chosen based on the Akaike information criterion. The results are shown in Table II.2.

Table II.2

Korea: Bivariatc VARs 1/2/ Variance Decomposition: Year-on-year Inflation

Monthly Data

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Quarterly Data

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All VARs include a dummy variable which lakes the value I for the period November 1997 (or 1997 Q4) onward.

Numbers in parentheses denote the lag length of llic VAR, determined on the basis of the Akaike information criterion.

1/11/01 15:28U;Korea\VARs\|Var_Becom_diim.xl5|S102

9. The money and credit variables perform well. Early in the simulation period, Ml and credit explain more than 50 percent of the variance of inflation, and by the end of the simulation, they explain 70-85 percent. In contrast, M3’s predictive power reaches more than 50 percent only by the seventh quarter. Nevertheless, because most of M3’s impact is felt from one year onward, it would appear to be a suitable leading indicator of inflation. Conversely, although the impact of credit is felt relatively quickly, it retains significant explanatory power throughout the simulation period and hence would also appear to be a suitable indicator. In sum, although the BOK has moved away from monetary targeting, it would seem that monetary and credit aggregates are still helpful information variables.

10. Of the two external cost-push variables, NEER seems to be a more promising indicator. The explanatory power of import prices never rises above 50 percent, whereas NEER explains 50 percent of the variance of inflation by the fifth quarter, and its contribution steadily rises thereafter. Two possible reasons for the relative unimportance of import prices compared to the NEER are: (a) the relative closedness of and low degree of competition in the Korean economy prior to the reforms implemented after the 1997-99 financial crisis; and (b) domestic policy changes to offset the impact of import prices. Most recently, for instance, despite the sharp increase in international oil prices, domestic fuel prices were kept steady until mid-2000 through cuts in fuel taxes.

11. Of the two asset price variables, stock market prices appear to be a more useful indicator. The contribution of the yield curve remains below 5 percent, whereas the contribution of stock prices reaches almost 40 percent by the end of the forecasting horizon. The low predictive power of the yield curve would indicate that the risk premium associated with corporate bonds obscures the information provided by the yield curve regarding inflationary expectations.

12. Turning to domestic cost-push variables, wages and ULC show only modest predictive power. Although the contribution of wages to the variance of inflation jumps to 54 percent in the second quarter, it drops thereafter to around 30 percent for the rest of the simulation period, ULC’s contribution remains low up to the eighth quarter, and rises to 40 percent only toward the twelfth quarter.

13. Measures of economic slack do better than measures of economic activity in explaining the variance of inflation. This is evident in the contrasting performance of IP (industrial production) and IPGAP (the difference between industrial production and its trend, taken to be a measure of monthly potential output). Whereas the contribution of IP rises to only 36 percent by the end of the simulation period, the contribution of IPGAP rises to 45 percent by the fourth quarter and to 70 percent by the twelfth. Similarly, the contributions of the unemployment gap and capacity utilization (both monthly data) are substantially higher than the contribution of real GDP growth. The three measures of the output gap perform about as well as real GDP growth.

E. Multivariate VARs

14. Several small multivariate VARs were estimated to take into account the comovement of certain groups of indicators. In this way, two objectives are accomplished: (1) hypotheses regarding the mechanism through which, to take one example, M3 growth affects the exchange rate and in turn inflation, can be explored; and (2) the “true impact” of a nonmonetary variable (say, the output gap) can be better identified, by controlling for the effect of monetary variables.

15. Because there are a vast number of possible combinations of variables, only a few systems were tried, using the promising indicators identified from the results of the bivariate VARs described previously, and combined in systems reasonably based on economic theory. Halikias (1999) argues that the small systems thus estimated can be regarded as reduced-form subsystems embodying relations that can be traced back to standard theoretical models. For each system estimated, a schema of the transmission process is briefly described in Annex II.2, together with a detailed discussions of the results. Briefly, the results indicate that:

  • Of the money and credit variables considered, M3 almost consistently has strong predictive power for inflation, while credit to the private sector also performs well.

  • Among the slack indicators, the industrial production and unemployment gaps also provide useful information on future inflation. The three estimates of the output gap are somewhat informative, but the patterns of their impulse response functions are problematic and would bear further, more rigorous, investigation.

  • Consistent with their promising performance in the bivariate VARs, the cost variables NEER and ULC can also be good leading indicators of inflation.

F. Overall Assessment

16. An inflation targeting regime can be seen as being more demanding than a monetary or exchange rate targeting regime. First, the link between the monetary authorities’ actions and the results is highlighted both under the statutes establishing the regime, and through the process of publication and discussion of the monetary authorities’ actions. Second, under IT, the monetary authorities will have to consider all available information to guide their actions.

17. In turn, the expanded use of information under IT has two implications. On one hand, it enhances the flexibility of the central bank because it is no longer limited to the information extracted from the movements of exchange rates or monetary aggregates. As noted earlier, a full-blown analytical and forecasting framework is not imperative, but can be helpful. On the other hand, it becomes all the more important for the central bank to gather as much information as it can, and to have reliable indicators of inflation as an input to monetary policy monitoring and deliberations.

18. This paper has shown that a number of potential leading indicators of inflation in Korea have very helpful explanatory power. One striking result is that M3 comes out wellin the various tests. Currently, along with the inflation targets, the BOK publishes targets for M3, probably as a holdover from the previous practice of targeting monetary aggregates. However, the M3 targets do not gain much attention, and the BOK is considering relegating M3 to be just another information variable. Making inflation the sole target would be more consistent with an IT regime, but the results of this study show that M3 should still be in the set of information variables. In addition, nonmonetary variables also contain significant information about future inflation. In particular, two indicators of economic slack (the industrial production and unemployment gaps) and two indicators of cost-push pressures (the nominal effective exchange rate and unit labor costs) would warrant inclusion in the set of information variables.

Annex II.1

List of Variables

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Annex II.2: Results from Estimated Multivariate VARs

Money, the exchange rate, and inflation

19. Trivariate VAR systems are estimated, consisting of Ml (or M3), the nominal effective exchange rate, and inflation, in that order. A priori, the impact of a positive shock to money is ambiguous. To the extent that it represents monetary easing, the NEER would depreciate. However, to the extent that it reflects stronger economic growth or an increase in the demand for money, the NEER would appreciate. In either case, a depreciation should lead to higher inflation.

20. The variance decomposition of inflation for this system is shown in Table II.3. Not surprisingly, the inclusion of NEER reduces the predictive power of both monetary variables. However, M3’s contribution to the variance of inflation remains substantial and its impact is felt most visibly during the second half of the simulation period, making it a suitable leading indicator. With either system, the predictive power of NEER is substantial throughout the simulation period.

Table II.3.

Trivariate VARs: Money-Exchange Rate-Inflation

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21. The impulse response functions of the system that includes M3 are shown in Figure II.1. A positive shock to M3 causes the NEER to fall below its baseline (i.e, a depreciation), while a positive shock to NEER leads to a statistically significant fall in inflation around a year after the shock. Figure II.1 also shows that a positive shock to M3 leads to significantly higher inflation, which persists even three years after the shock. In sum, the results for this section show that M3 has more explanatory power than Ml and that expansionary monetary policy leads to depreciation and higher inflation.

Figure II.1.
Figure II.1.

Korea: M3-NEER-Inflation

Response to One S.D. Innovations ± 2 S.E.

Citation: IMF Staff Country Reports 2001, 101; 10.5089/9781451822052.002.A002

Money, credit, and inflation

22. Table II.4 shows the results for two systems consisting of M1 or M3, credit to the private sector, and inflation. These systems portray monetary easing leading to higher credit and, implicitly through increased economic activity, to higher inflation.

Table II.4.

Trivariate VARs: Money-Credit-Inflation

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23. The two sets of results are contrasting. When credit is included, M1’s predictive power sharply decreases, whereas M3’s remains significant. Based on the variance decomposition of inflation, therefore, M3 and credit are promising indicators. This impression is confirmed by the impulse response functions, shown in Figures II.2. Positive shocks to M3 significantly raise credit and inflation above their baseline even after a year, with a similar result for the effect of shocks to credit on inflation.

Figure II.2.
Figure II.2.

Korea: M3-Credit-Inflation

Citation: IMF Staff Country Reports 2001, 101; 10.5089/9781451822052.002.A002

Response to One S.D. Innovations ± 2 S.E.

Money, slack, and inflation

24. The third set of trivariate VARs estimated are systems consisting of money, three different measures of economic slack (the unemployment gap, unit labor cost, and the output gap), and inflation, in that order. These systems aim to portray the impact of expansionary monetary policy on economic activity and production costs. It is postulated that a positive shock to money reduces the unemployment and output gaps (in turn reducing inflation) or raises unit labor cost (in turn raising inflation).

25. Table II.5 shows that M3 and the unemployment gap are useful indicators of inflation, but that Ml and the unemployment gap together do not constitute an informative system. The predictive power of both M3 and the unemployment gap remain significant throughout the simulation period, with most of the effect being felt around the eighth quarter. Focusing on M3 and the unemployment gap, the impulse response function in Figure II.3 show that a positive shock to M3 does not have much effect on the unemployment gap As expected, an increase in the unemployment gap causes inflation to fall below its baseline path, and this effect peaks around one year after the shock.

Table II.5.

Trivariate VARs: Money-Unemployment Gap-Inflation

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Figure II.3.
Figure II.3.

Korea: M3-UNEMGAP-Inflation

Response to One S.D. Innovations ± 2 3.E.

Citation: IMF Staff Country Reports 2001, 101; 10.5089/9781451822052.002.A002

UNEMGAP: Actual unemployment less the NAIRU.

26. Table II.6 (the VARs incorporating ULC) shows that M3 remains useful, but M1 is less so. ULC also seems to be a helpful leading indicator, as its predictive power remains above 30 percent all through the simulation period. Hence, focusing on the impulse response functions for M3 and ULC, Figure II.4 shows that a positive shock to M3 raises ULC above its baseline and that this effect peaks at a year after the shock, but that the impact of ULC on inflation is not statistically significant throughout the simulation.

Table II.6.

Trivariate VARs: Money-Unit Labor Cost-Inflation 1/

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Quarterly data.

Figure II.4.
Figure II.4.

Korea: M3-ULC-Inflation

Citation: IMF Staff Country Reports 2001, 101; 10.5089/9781451822052.002.A002

Response to One S.D. .nnovations ± 2 S.E.ULC: Unit labor cost based on value-added.

27. Tables II.7a and II.7b show the variance decomposion of systems consisting of money, measures of the output gap, and inflation. In all six cases considered, money and the output gap have moderate to high predictive power and have lags that make them suitable as leading indicators. In the case of systems with Ml, the contribution of Ml peaks at around the eighth quarter, while the contribution of the output gap steadily rises until the end of the simulation period. In the case of systems with M3, the respective contributions of M3 and the output gap remain the same in all combinations. However, turning to the feedbacks among the indicators (Figures II.5-II.7), the relationship between M3 and the output gap, and the output gap and inflation are contrary to theory and expectations. That is, in all cases, a positive shock to money is associated with a lower output gap (i.e., actual output falls below potential output). Also in all cases, a higher output gap is associated with a decrease in inflation, rather than an increase.

Table II.7a.

Trivariate VARs: Mi-Output Gap-Inflation 1/

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Quarterly data

GAPCS: Output gap estimated using a cubic spline smoothing filter.GAPHP: Output gap estimated using a Hodrick Prescott filter.GAPPF: Output gap estimated using the production function approach.
Table II.7b.

Trivariate VARs: M3-Output Gap-Inflation 1/

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Quarterly data

GAPCS: Output gap estimated using a cubic spline smoothing filter.GAPHP: Output gap estimated using a Hodrick Prescott filter.GAPPF: Output gap estimated using the production function approach.
Figure II.5.
Figure II.5.

Korea: M3-GAPCS-Inflation

Response to One S.D. Innovations ± 2 S.E.

Citation: IMF Staff Country Reports 2001, 101; 10.5089/9781451822052.002.A002

GAPCS: Output gap estimated using the cubic spline smoothing method.
Figure II.6.
Figure II.6.

Korea: M3-GAPHP-Inflation

Response to One S.D. Innovations = 2 S.E.

Citation: IMF Staff Country Reports 2001, 101; 10.5089/9781451822052.002.A002

GAPHP: Output gap estimated using the Hodrick Prescott filter.
Figure II.7.
Figure II.7.

Korea: M3-GAPPF-Inflation

Response to One S.D. Innovations ± 2 S.E.

Citation: IMF Staff Country Reports 2001, 101; 10.5089/9781451822052.002.A002

GAPPF: Output gap estimated using the production function approach.

28. In sum, the results bolster the earlier findings that M3 is a promising leading indicator. They also indicate that the unemployment gap and unit labor costs provide significant information.

Output, slack/production costs, and inflation

29. The final set of trivariate VARs estimated portray the relationship between economic activity, slack (or production costs), and inflation. It is postulated that increased economic activity (as measured by the excess of industrial production over its trend) leads to higher capacity utilization and unit labor costs, or a lower unemployment gap, and hence higher inflation.

30. Industrial production emerges as relatively useful in predicting inflation, except when interacted with the unemployment gap. The unemployment gap and ULC are also useful indicators, while capacity utilization’s predictive power is rather low. The impulse response functions (results not shown) indicate that, as expected, a positive shock to industrial production is associated with higher capacity utilization and a lower unemployment gap. However, the relationship with ULC is not as expected, with ULC basically falling below its baseline path for much of the simulation period. With regard to inflation, a positive shock to capacity utilization is weakly associated with higher inflation, but a higher unemployment gap and higher ULC are associated with inflation falling below its baseline path.

Table II.8.

Trivariate VARs: Industrial Production-Slack/Unit Labor Cost-Inflation 1/

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The trivariate VAR with unit labor cost usese quarterly data.

IPGAP: Output gap estimated using industrial production and the Hodrick Prescott filter.UNEMGAP: Difference between unemployment and NAIRU.

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1

This chapter was prepared by Henry Ma and Aung Thurein Win.

2

For the medium term (2001 onward), the BOK has set a target of 2.5 percent core inflation (on an annual average basis), but for 2001 itself, the target has been set at 3.0± 1 percent.

3

In any case, Brazil’s experience illustrates that establishing an inflation forecasting framework can be done in a relatively short period of time. Bogdanski et al (2000) describe the Brazilian central bank’s experience of setting up from scratch an inflation forecasting framework to aid monetary policy formulation.

4

Baumgartner and Ramaswamy (1996) on the United Kingdom, Baumgartner et al (1997) on Sweden, and Halikias (1999) on the Euro zone; Trecroci and Vega (2000) on M3; and Emery and Chang (1996, 1997) on wages and capacity utilization as leading indicators. This study is not as exhaustive as Chauvet’s (2000) work on Brazil, which examines 200 potential leading indicators, or Stock and Watson’s (2000) exploration of alternative economic indicators as inputs into Phillips curve equations for the United States.

5

Annex II. 1 describes the variables in more detail.

6

For more details, refer to Chapter I, “Potential Output, the Output Gap, and Inflation in Korea.”

7

The slope of yield curve is defined as the difference between the three-year corporate bond rate and the overnight call rate. Although ideally, the yield on risk-free government debt should be used, this may not be suitable in the Korean context because until recently, the market for government bonds was not very active or liquid.

8

Response by the BOK to a query by the National Assembly.

9

Credit to the private sector on a monetary survey basis comprises only credit extended by deposit money banks. Credit on a financial survey basis is broader, since it includes nonbank financial institutions, such as investment trust companies. The importance of nonbank institutions has grown apace with financial development in Korea. Recently, the growth rates of the two credit aggregates have sharply diverged, due to portfolio shifts by savers, occasioned by the crisis in the investment trust sector in 1999.

10

A. time-varying NAIRU series for Korea was estimated for the period January 1999-May 2000 by LEE Woosung of the LG Economic Research Institute, using the method described in Gordon (1997, 1998) and Staiger et al (1997). According to Lee’s estimates, as of May 2000, the NAIRU stood at 2.5 percent, compared to the actual unemployment rate of 3.9 percent.

Republic of Korea: Selected Issues
Author: International Monetary Fund