Journal Issue


International Monetary Fund
Published Date:
September 2012
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I. Global Spillovers, Lending Conditions, and Monetary Policy in Indonesia1

Policy interest rates are expected to anchor money market rates and Treasury bill/bond yields that act as benchmarks for deposit and loan rates. Historically, retail deposit and lending rates have closely followed the policy rate (and the SBI auction rate before 2005). However, recently the SBI deposit facility and other money market rates have been allowed to fall below the policy rate, potentially reducing the effectiveness of monetary policy transmission by blunting the impact of policy rates on retail bank rates. The concern here is that lower market rates could translate to retail bank rates that are below levels consistent with the central bank’s policy stance and thus inflation objective. Therefore, this chapter evaluates the role of policy rates as well as lending conditions (proxied by the spread between the policy and market rates) when assessing monetary conditions.

Indonesia: Interest Rates

Source: Bloomberg LP; and CEIC Data Co.

Indonesia: Credit Conditions

Source: CEIC Data Co. Ltd.; and IMF staff estimates

1. To assess the impact of policy rates and lending conditions as well as the second-round effects of higher global commodity prices, the chapter estimates a small open economy version of the Global Projection Model (GPM) using Bayesian techniques. The Great Recession highlighted the importance of taking into account both domestic and global shocks (and uncertainty) as well as macrofinancial transmission mechanisms in the design of monetary policy in emerging markets. We estimate a GPM model using Bayesian techniques that incorporates global factors and lending conditions to consider the policy trade-offs under an inflation forecast targeting regime like in Indonesia.

2. The model estimates shed important insights for monetary policy making. First, a 1 percentage point increase in the spread leads to a 0.15 percent change in the output gap, compared to 0.13 percent for the policy rate. Taken together with the impact of the spread on the output gap, this suggests scope for currently reducing inflationary pressures by lowering the spread rather than hiking the policy rate itself. Second, there are significant second-round effects of headline inflation on core inflation, making core inflation susceptible to global commodity prices and domestic fuel price adjustments, potentially requiring a monetary policy response (see IMF 2011).

A. Small Open Economy GPM Model

3. The analysis is conducted with the use of a modified version of the small “New Keynesian” macroeconomic model of Berg, Karam, and Laxton (2006). The model is a stripped-down version of a stochastic general equilibrium (DSGE) model with rational expectations. By virtue of their relatively simple structure, small New Keynesian models have been used for forecasting and policy analysis purposes in central banks and by IMF country desks. A number of inflation forecast targeting (IFT) central banks have used similar models as integral parts of their forecasting and policy analysis system (see Laxton and others, 2009).

4. To capture the commodity dependence and importance of the banking system in Indonesia, the baseline model is extended to incorporate oil prices and macrofinancial linkages through a credit conditions variable (Carabenciov and others, 2008). The model features a small open economy including forward-looking aggregate supply and demand with microfoundations and with stylized (realistic) lags in the different monetary transmission channels. External shocks from the rest of the world are captured here by U.S. growth. Output developments in the rest of the world feed directly into the small economy as they influence foreign demand for Indonesian products. Changes in foreign inflation and interest rates affect the exchange rate and, subsequently, demand and inflation in Indonesia.

5. The model is estimated using Bayesian techniques based on prior distributions for the parameters from cross-country work and assumptions about the Indonesian economy. Bayesian estimation in a situation of a relatively small sample size helps ameliorate the problems of classical econometric estimation, which often gives macro model results that are inconsistent and faced with simultaneity challenges. This is a particularly important aspect for Indonesia where there was a structural change in 2005 when the Bank Indonesia (BI) switched to an IFT framework, necessitating a subsample estimation from 2005 to 2012 to confirm the robustness of the full sample estimates. The model is estimated based on quarterly data from 2000 to 2012 using prior empirical knowledge about the parameters of interest for Indonesia or cross-country studies on emerging markets (see Anand, Ding, and Peiris, 2011; and Bathaluddin and Waluyo 2010). All variables are seasonally adjusted using the X12 filter, with the exception of the interest rate and the exchange rate, and expressed in “gap” terms, defined as deviations from a Hodrick-Prescott time trend or a multivariate filter (see Benes and others, 2010) in the case of the output gap.

6. The parameter estimates shed new insight into the monetary transmission mechanism in Indonesia, the role of domestic and global shocks, and the weights placed on inflation, the output gap, and the exchange rate in an open-economy Taylor-rule. The model has four behavioral equations: (1) an aggregate demand or IS curve that relates the level of real activity to expected and past real activity, the real interest rate, the real exchange rate, foreign demand, and financial conditions; (2) a price setting or Phillips curve that relates inflation to past and expected inflation, the output gap, and the exchange rate; (3) an uncovered interest parity condition for the exchange rate, with some allowance for backward looking expectations; and (4) an open-economy Taylor-rule for setting the policy interest rate as a function of the output gap, expected inflation, and the exchange rate.

(1) The aggregate demand equation and results are as follows:


where ygap is the output gap, RRgap the real interest rate gap, zgap the real exchange rate gap, ygapRW the output gap in the United States, η a measure of lending conditions based on the spread between market and policy rates,2 and the, β a series of parameters attached to these variables, and εygap an error term that captures other temporary exogenous demand shocks (details of the extension to the model to include lending conditions is in Appendix 1).

  • Berg, Karam, and Laxton (2006) suggest that the value of βlag will lie between 0.5 and 0.9, with a lower value for less advanced economies more susceptible to volatility. The coefficient of 0.47 for βlag is comparable to other emerging markets. The lead of the output gap (βld) is typically small, between 0.05 and 0.2, and the estimated value for Indonesia is at the high end of that range. The parameter βRRgap indicates the effectiveness of the monetary transmission mechanism, while βzgap and βRWygap depend on the importance of the exchange rate channel and the degree of openness. The posterior estimates of βRRgap and βzgap suggest that the interest rate effect on aggregate demand is stronger than the exchange rate affect, possibly reflecting the importance of factors beyond the exchange rate for competitiveness (e.g., costs of doing business). However, lending conditions have a slightly (0.15) stronger influence on aggregate demand than policy do rates. The value for βRWygap is 0.15, in line with BI ARIMBI model of Bathaluddin and Waluyo 2010.


(2) The Philips curve equation and results are as follows:


where π4t+1 is the four-quarter ahead inflation rate (year/year), π4t-1

the four-quarter lagged inflation rate, ygap the output gap, zt −zt-1 the real depreciation, α the parameters, and εtπ
an error term.

  • The απld parameter in the headline inflation equation determines the forward-looking component of inflation (while its inverse 1 −απld determines the backward-looking component). The parameter value can be interpreted as depending in part on the credibility of the central bank and in part on institutional arrangements regarding wage indexation and other price-setting mechanisms. A high value of απld, close to 1, would suggest that small changes in monetary policy cause large changes in price expectations. The αygap parameter depends on the extent to which output responds to price changes and, conversely, how much inflation is influenced by real demand pressures, and is typically between 0.25–0.50. This parameter ultimately depends on the “sacrifice ratio” (the loss of output necessary to bring down inflation) and is estimated to be 0.25. The αzgap parameter represents the short-term passthrough of (real) exchange rate movements into prices, and depends on trade openness, price competition, and monetary policy credibility. The exchange rate passthrough coefficient is estimated to be relatively high in the Indonesia.


As an extension, the following equation for core inflation is added:


  • where the term (π4t-1-π4c,t-1) has been added to the simple canonical inflation equation to allow for the possibility of relative price and real wage resistance (second-round effect); or more precisely that workers and other price setters may try to partially keep their prices from rising in pace with past movements in headline CPI. If the parameter αc, is zero, commodity price shocks that raise headline inflation, for example, will have no effect on core inflation and may not necessitate an increase in interest rates. However, to the extent that higher commodity prices are an important input into the production costs of many consumer goods, or if workers resist the reduction in their real wages in response to an increase in headline inflation, there could be a role for headline inflation to play in monetary policymaking. The estimated coefficient of αc of 0.49 is significant and should be taken into account, although there are a number of reasons to target core inflation in an economy prone to commodity price swings like Indonesia (see IMF 2011).


(3) The uncovered interest parity equation and results are as follows:


where zt is the real exchange rate (an increase represents a depreciation), RRt the real interest rate, RRtRW the U.S. real interest rate, ρ* the historical average risk premium on the domestic currency, δz the smoothness parameter, and ɛtz an error term. This equation, an uncovered interest rate parity condition, posits that the real exchange rate is a function of the expected real exchange rate (the first two terms), the real interest rate differential (the currency risk premium), and a disturbance term.

  • The δ parameter in the real exchange rate equation determines the relative importance of forward- and backward-looking real exchange rate expectations. If δ is equal to 1, the exchange rate behaves as in the Dornbusch overshooting model (the real exchange rate is a function of the future sum of all real interest rate differentials). The estimated coefficient of 0.51 makes monetary policy potentially a more effective tool.


(4) The open-economy Taylor-rule and results are as follows:


  • The γ parameters in the monetary policy rule equation depend on the speed and extent to which the monetary authorities adjust the nominal interest rate, and the relative importance of the inflation target versus the real activity target. There is a significant degree of interest rate smoothing in Indonesia but BI does aggressively respond to inflation forecasts (expectations) above the targeted level. It is common for central banks to pay some attention to real activity even in a “pure” inflation targeting framework and, thus, for the γygap coefficient to be greater than zero. This is borne out in the Indonesia data where the weight on real activity is comparable to other emerging markets. γzgap reflects the weight on the real exchange rate, which has been observed to be quite significant in emerging markets (see Stone and others, 2009). The estimated coefficient is low and indicates that BI does not appear to place weight on exchange rate developments in conducting monetary policy, although it may reflect the use of foreign exchange intervention to manage the exchange rate instead.


7. The model-based forecast suggests that in order for inflation to remain within BI’s target range, lending conditions would need to be tightened over the next two years. The analysis forecasts inflation and other real economy factors (such as the output gap) conditional on the WEO forecast for the U.S. economy, global inflation, and estimated distributions for stochastic shocks including supply-side factors in Indonesia. The simulations based on no tightening of lending conditions would result in core inflation exceeding the authorities’ inflation target, while gradually moving market rates to the policy rate (i.e., closing the spread between them) would help keep core inflation within the target range with broadly unchanged policy rates. The exact magnitudes of the estimated impact have significant uncertainties around them, as they depend on exact model specification, but the direction of change is likely to be instructive.

Core Inflation Forecast (No Tightening)

(Quarter-on-quarter s.a.a.r.)

Core Inflation Forecast (With Tightening LC)

Quarter-on-quarter s.a.a.r.)

B. Conclusion

8. The model estimates shed other important insights for monetary policy making. The Bayesian estimates show that the impact of lending conditions on the output gap is 0.15 compared to a coefficient of 0.13 for the policy rate. Thus, evaluating the monetary stance and inflation forecast without taking into account lending conditions could potentially lead to overshooting the inflation target if lending conditions are accommodative. Importantly, however, the model forecasts suggest that inflation could be kept within target by tightening lending conditions (raising market rates to the policy rate) without hiking policy rates. In addition, there are significant second-round effects, with a 1 percentage point increase in the difference in headline from core inflation leading to a 0.45 percentage point increase in core inflation. Therefore, changes to administered fuel prices that have not kept up with global prices can be another source of inflation pressure that may require a policy response.


This chapter extends the workhorse model of Berg, Karam, and Laxton (2006) to better capture macrofinancial and global linkages:

  • The global financial crisis and great recession have highlighted how financial developments can affect the real economy, particularly through “financial accelerator” effects. Given the dominance of banks in Indonesia, the analysis focuses on bank lending conditions, as in Carabenciov and others (2008). Bank lending (BL) is a function of BL* (defined as the equilibrium level of BL), the real interest rate gap, and banks’ expectation of the economy four quarters ahead. The output gap is explained by the same variables as in equation (1) above as well as by a distributed lag of εtBL. The values of the coefficients imposed on the distributed lag of εtBL are intended to react to a pattern in which an increase of εtBL (a loosening of credit conditions) is expected to positively affect spending by firms and households in a hump-shaped fashion, with an initial buildup and then a gradual rundown of the effects as in Carabenciov and others (2008). The specification in this case is:

    where η is the distributed lag of εtBL and is calculated as:



  • Global linkages are modeled as a two-country small open economy version of the GPM (United States and Indonesia) instead treating the global factors as purely exogenous (AR process) in order to incorporate agents expectations about the global economy such as the large negative output gap in the United States and thus the Federal Reserves’ policy intention to maintain interest rates near-zero until 2014, that is likely to have an important bearing on emerging markets like Indonesia.


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Prepared by Shanaka J. Peiris.

The spread between the one-month SBI rate and the policy rate is used prior to June 2010 and the JIBOR one-month rate is used as a measure of market rates after June 2010.

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