18 Introducing a Semi-Structural Macroeconomic Model for Rwanda

Andrew Berg, and Rafael Portillo
Published Date:
April 2018
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Luisa Charry, Pranav Gupta and Vimal Thakoor 

1 Introduction

After facing some challenges in the conduct of monetary policy and to further legitimize their commitment to a low and stable inflation, central banks of the East African Community (EAC) have embarked on gradually updating their monetary policy frameworks. The main changes include allowing greater exchange rate flexibility, enhancing the role of policy rates in signalling the policy stance, announcing inflation targets, and introducing forward-looking elements in policy formulation and communication strategies. As part of this process, efforts have also been undertaken to better understand the transmission channels of monetary policy to real economic activity and prices. This chapter contributes to this effort.

The National Bank of Rwanda (NBR) has been working on strengthening its monetary policy framework. One of the dimensions through which understanding of the transmission mechanism can be enhanced is through the introduction of a semi-structural macroeconomic model that links the monetary policy stance to economic activity and inflation. Such a model can then be integrated into a wider set of processes and tools (a Forecasting and Policy Analysis System, FPAS) to prepare coherent macroeconomic forecasts, perform scenario analysis, and inform the monetary policy formulation process.

As in other chapters of this book, the model introduced here is a rational-expectations New Keynesian model, similar to models used in central banks around the world. The model consists of four basic behavioural equations: an IS curve (aggregate demand), which relates monetary policy and real economic activity; a set of Phillips curves (aggregate supply) that link economic activity and inflation; a monetary policy rule that describes the response of the central bank to deviations of inflation from the target and the phase of economic activity.

A key feature of the model in this chapter is the introduction of a modified uncovered interest parity condition (UIP), which describes exchange rate dynamics. This modification to the UIP condition seeks to capture key structural features of Rwanda’s economy and policy framework, such as the rather closed nature of the capital account, its shallow and underdeveloped financial system, and the existence of dual targets on both inflation and the nominal exchange rate.

The model is calibrated to reflect a set of stylized facts of the Rwandan economy, especially the heavy reliance of monetary policy on a stable exchange rate. A filtration of the last ten years of observed data through the model allows us to determine the contribution of various factors to inflation dynamics and its deviations from the inflation target of 5 per cent. In particular, we are able to dissect the contribution of food and oil prices to inflation. Our results, consistent with evidence for other countries in the region, suggest that food and oil price shocks have accounted for the bulk of inflation dynamics in Rwanda, particularly in 2008 and 2011. Fluctuations in food prices have the greatest impact on inflation, while the impact of inflation from international oil price changes is somewhat lower. This can be explained by the fact that there is only partial pass-through from international prices to the domestic pump price structure, which is administratively updated on a regular basis to mitigate their impact.

The filtration exercise also enables us to show that there have been periods when the monetary policy stance has been more accommodative than warranted, most significantly in 2008 and 2011, and this in turn has contributed to inflation deviating from its target. In 2008 monetary policy was significantly looser than required, given the inflationary developments. In 2011, while policy was looser for a longer duration, the magnitude was smaller compared to 2008. We also disentangle the contribution of the exchange rate to inflation. We find that exchange rate developments were a significant contributor to inflation in 2008 and the second half of 2012, when the exchange rate depreciated in response to a suspension of foreign aid flows and reduced reserve buffers. The impact was, however, mitigated by favourable food price developments. The model thus enables a clear identification of the factors contributing to inflation, both from domestic and external factors, as well as those that are policy induced. Further more, the exercise shows that properly tailored structural models can provide useful insights even when the data are noisy or scarce, financial markets underdeveloped, and regimes changing. The rest of this chapter is organized as follows. Section 2 presents an overview of the Rwandan economy and the implementation of monetary policy. The model, and the results of the filtering and forecasting exercises are presented in Section 3. Section 4 discusses the authorities’ conduct of monetary policy in light of the findings from the model. Section 5 concludes.

2 An Overview of Rwanda’s Economy and Monetary Policy Regime

Rwanda’s economy has come a long way over the past two decades. Judicious economic policies, coupled with ample donor support, have allowed the economy to sustain a real annual growth of around 8 per cent over the past decade. The sectors that have contributed most to growth are agriculture and services. Targeted policies and improving productivity have increased the contribution of the agricultural sector, which accounts for 30 per cent of GDP and 70 per cent of employment (National Institute of Statistics of Rwanda). The construction and services sectors have also sustained growth, reflecting high public investment and a deliberate policy to stimulate private sector credit growth. While achieved from an initial low base, this sustained growth has enabled the country to make significant inroads in the fight against poverty, as real GDP per capita increased from around US$200 in 2001 to US$660 in 2013. Foreign grants have traditionally been a major component of budgetary resources but are on the decline (about 10 per cent of GDP, or nearly 40 per cent of the budget). Since 2012, when major donors suspended aid flows, the authorities have been discussing options to further reduce Rwanda’s aid reliance and foster greater domestic revenue mobilization. The narrow export base is dominated by low-value products like coffee and tea. Mineral exports are increasing, although the sector is not yet operating at full potential. Despite exhibiting strong Doing Business indicators, FDI flows have yet to materialize on a significant scale. Debt relief coupled with prudent fiscal policies have contained external debt to under 25 per cent of GDP. In 2013, Rwanda tapped the international capital markets for the first time in its history, with the issuance of a US$400 million Eurobond.

The objectives of the NBR include maintaining inflation in single digits while supporting growth. Accordingly, the NBR targets an inflation rate of 5 per cent. Monetary policy has been formulated in the context of challenging, and at times difficult, domestic and external environments. In particular, food and oil price shocks have played an important role in inflation dynamics. Also, the economy has been subject to significant demand shocks stemming from the global financial crisis in 2009. The suspension of aid flows in 2012 and reduced reserve buffers have added an additional dimension to the policy formulation challenges.

Inflation, while volatile, has been contained in single digits (Figure 18.1). Food and fuel prices are substantial components of headline inflation—food accounts for 35 per cent of the CPI in Rwanda. About 85 per cent of the food basket is sourced locally, while the rest is imported. As a country that relies heavily on imported oil, Rwandan inflation is also exposed to changes in international fuel prices, albeit the impact is somewhat limited as local fuel prices are subject to administrative controls. In the CPI statistics, fuel is included in the transportation component, which accounts for 12 per cent of the basket.

Figure 18.1.Rwanda: Selected Economic Indicators, 2006–2013

Source: IMF staff based on authorities’ data.

The NBR conducts policy in the context of a flexible monetary targeting framework, with reserve money used as the operational target and broad money (M3) as an intermediate target. An array of instruments is used to manage liquidity, including reserve requirements, open market operations, standing facilities, and foreign exchange operations. The NBR has also increasingly relied on its policy rate—the Key Repo Rate (KRR)—to signal its monetary policy stance. Since the introduction of the KRR in 2008, the NBR conducts repo transactions with commercial banks to navigate interbank rates in a corridor around the KRR. However, the coexistence of both quantity and price targets has, on occasions, led to inconsistent signalling of the policy stance.1

Monetary policy implementation remains challenging. Reserve money targets often overshoot the target during quarters, even if end quarter targets are met.2 However, these target misses have not translated into higher inflation. A gradual shift to a quarterly average for reserve money, within a band, has recently provided the authorities greater flexibility in the conduct of their monetary policy operations. However, the slow or non-response of the KRR to changing monetary conditions and market developments has undermined its signalling role and its effectiveness in the transmission mechanism. In early 2013, the divergences between the KRR and the interbank rate widened. Hence, the authorities have had at times needed to have recourse to moral suasion to affect market rates, or take administrative measures—for example, by not fully passing through international oil price changes—to contain inflation.

The NBR has been taking measures to improve the transmission of monetary policy and ensure greater relevance for the policy rate. To better absorb liquidity, the NBR has started issuing longer-term instruments and has reactivated the secondary market to support the development of an active interbank market. The NBR is also bolstering its communication strategy with market participants to promote a better understanding of monetary policy decisions and to guide expectations formation. The recent publication of the quarterly inflation report goes in this direction.

The Rwandan authorities have traditionally favoured a stable exchange rate. The NBR has intervened regularly to maintain the currency within a narrow band of the official rate. However, the determination of the market exchange rate suffers from some structural issues. The interbank market remains shallow, dominated by the central bank. To foster greater exchange rate flexibility, the authorities introduced an exchange rate corridor system in March 2010 and committed to intervening in the market only to smoothen out temporary volatility. Following the aid shock in 2012, and in a bid to preserve reserve levels and contain pressures in the forex market, the NBR has allowed greater exchange rate flexibility. The currency has depreciated by about 12 per cent since then. Rwanda’s de facto exchange rate classification has since been revised from crawl-like to other managed arrangement. The de jure exchange rate arrangement is classified as floating (IMF, 2013).

3 The Model: Outline, Calibration, Filtering, and Forecast Exercise

3.1 The Model

The model consists of four blocks: aggregate demand, aggregate supply, links with the rest of the world through arbitrage conditions, and a monetary policy rule.

In terms of general notation, for any given variable x, a bar (x¯) denotes that variable’s trend or long-run value, and a gap term added to the variable (xgap) denotes deviations from trend. The model is specified for quarterly frequencies, a delta (Δ) in front of the variable indicates, except for inflation rates which are denoted by π and correspond to quarter over quarter annualized seasonally adjusted changes. Finally, an asterisk * denotes a foreign variable and ss sub-indexes stand for steady state values.3 Behavioural equations also include auto-regressive components to better match the properties of the data.

3.1.1 Aggregate Demand

Equation 1 describes the behaviour of deviations of output from trend (the output gap), where ygap is the output gap, rmci is the real monetary conditions index (an overall indicator of the monetary policy stance, which is a weighted average of the deviation of the real interest rate and real exchange rate from their trends), ygap* is the US output gap, rgap is the real interest rate gap, and zgap is the real exchange rate gap.4εygap represents a shock or innovation to domestic aggregate demand which picks up non-modelled effects. The real interest rate is the ex ante interbank rate deflated by headline inflation.

3.1.2 Aggregate Supply

To better capture the effects of supply shocks on inflation dynamics and the conduct of monetary policy, we introduce behavioural processes for core, food, and fuel inflation. Core inflation (πcore) dynamics evolve according to Equation 3. Here, the lagged term in the Phillips curve captures the backward-looking expectations of agents based on learning, imperfect credibility of the central banks, or indexation. Etπt stands for headline inflation expectations and is defined as a function of lagged and future inflation.5 rmc denotes the real marginal costs, given by a weighted average of the real exchange rate gap (zgap) and the output gap (ygap). The real exchange gap reflects the effect of imported goods’ prices on inflation while the output gap captures excess aggregate demand pressures. Once again, εcore stands for shocks coming from excluded factors.

Food inflation dynamics (πfood), in turn, are represented by Equation 6. Similar to core inflation, food inflation is explained by its past level, inflation expectations, and excess aggregate demand pressures. Additionally, the φ^food term captures price pressures arising from changes in international food prices (πfood*) relative to domestic food prices. Here Δs stands for changes in the nominal exchange rate and Δz¯t denotes changes in the trend value of the real exchange rate, and εfood is a perturbation term.

The specification for oil inflation (πoil) has a similar structure as the one for core and food inflation (Equation 8).

Finally, headline inflation is defined as the weighted average of core, food, and oil (Equation 9).

3.1.3 Exchange Rate Determination

The block that models the links with the rest of the world is comprised by a set of arbitrage conditions. We introduce a modified uncovered interest rate parity condition to simulate nominal exchange rate dynamics in Rwanda (Equation 10), where st is the nominal exchange rate, stT is the target exchange rate, i and i* are the Rwandan interbank rate and the US Federal Funds rate, and ρ is a risk premium. The parameter η controls the degree of flexibility of the nominal exchange rate and/or deviations from uncovered interest parity.6 We also assume that the rate of crawl (ΔstT) is such that in the long run the target exchange rate is determined by relative purchasing power parity adjusting for trends in the real exchange rate. This, in turn, implies that efforts by the central bank to manage the exchange rate have to be consistent with the inflation objective.7 This process is represented by Equation 12, where π¯ stands for the domestic inflation target, π¯* is the US inflation rate, and Δz¯ is the change in the equilibrium real exchange rate.

The modification to the UIP condition is very general, but by allowing for a parameter (σ1) to capture the degree of capital mobility and the response of the exchange rate to monetary policy, the model is better able to fit the data. It also permits us to better characterize the policy framework in place in Rwanda, typified by active exchange rate management through the use of unconventional instruments (interventions, moral suasion, etc.) and where dual nominal anchors coexist. This setting can be used to characterize the policy frameworks of other frontier markets in the region. Other issues that arise from this adaptation, such as the relationship between international reserve stocks and the risk premium, or the two-instrument/two-target problem more generally are not incorporated.8

3.1.4 Interest Rate Policy Rule

We close the model by introducing a monetary policy reaction function, according to which the central bank sets the interest rate in response to deviations of the one-year ahead inflation forecast from the inflation target and the output gap (Equation 13).9 Here i¯ is the long-run (neutral) nominal interest rate, π4 is year-on-year (YoY) inflation rate, r¯ is the neutral real interest rate, and σi is an error term that can be interpreted as a measure of the unsystematic component of monetary policy.

We also specify a stochastic process for the inflation target (Equation 15), which allows us to simulate different disinflation paths.

3.1.5 Long-Run Trends

The long-run values of the real interest rate, the change in potential output and the real exchange rate are assumed to follow a simple first order autoregressive process given by:

where Δy¯ss,Δz¯ssandr¯ss are the steady state values of potential output growth, the change in the real exchange rate, and the real interest rate, respectively.

3.1.6 Foreign Block

The dynamics of our model are completed by adding a simple rest of the world block, which we proxy with US variables. The block is comprised by a foreign output gap equation (ygap*), an autoregressive process for the foreign neutral real interest rate (r¯*) and headline inflation (π*), and a nominal interest rate policy rule (i*).

3.2 Data and Calibration

The complete dataset along with the sources is described in Table 18.1. The database spans from 2003Q1 to 2013Q3.10 The disaggregation of inflation into core, food, and oil follows the National Institute of Statistics all-urban consumer price index. The weight for food and non-alcoholic beverages in the overall CPI basket is 35.4 per cent, whereas the weight for oil (transport) is 11.9 per cent. Core CPI is calculated by excluding food and oil CPI from the overall CPI index. The international oil and food price indexes are those of the World Economic Outlook (WEO).

The GDP and CPI series are seasonally adjusted using the X12-ARIMA filter. The quarterly GDP data is also smoothened using the Hodrick-Prescott (HP) filter, using a smoothing parameter of 0.5. This de-trending of the series seeks to remove some of the volatility associated with supply shocks, which are difficult to model in structural terms.

Table 18.1.Data Series
sExchange rate (Franc/USD)NBR
iInterbank rateNBR
yQuarterly GDPIMF (IFS)
CPIQuarterly CPI (headline)NISR
CPIoilQuarterly CPI (oil)NISR
CPIfoodQuarterly CPI (food)NISR
ΔPoilworldInternational oil pricesIMF (WEO)
ΔPfoodworldInternational food prices indexIMF (WEO)
i*US Federal Funds rateIMF (IFS/WEO)
ygap*US output gapIMF (IFS/WEO)
Note: compilation of quarterly GDP: for 2006 and afterwards, the IMF’s annual GDP series is converted to a quarterly frequency using the authorities’ quarterly GDP estimates. For the earlier period, quarterly weights computed from the authorities’ quarterly GDP estimates for 2006–11 are applied to the IMF’s annual series.NISR: National Institute of Statistics for RwandaNBR: National Bank of RwandaWEO: World Economic OutlookIMF: International Monetary FundIFS: International Financial Statistics
Note: compilation of quarterly GDP: for 2006 and afterwards, the IMF’s annual GDP series is converted to a quarterly frequency using the authorities’ quarterly GDP estimates. For the earlier period, quarterly weights computed from the authorities’ quarterly GDP estimates for 2006–11 are applied to the IMF’s annual series.NISR: National Institute of Statistics for RwandaNBR: National Bank of RwandaWEO: World Economic OutlookIMF: International Monetary FundIFS: International Financial Statistics

The model parameters are calibrated to match the broad properties of the data, following basic economic principles and how sensible the properties of the resulting model look (Table 18.2).11 The steady state values of the real interest rate, output growth, and the real exchange rate change correspond to the average of the last six years. The inflation target is consistent with the target of the NBR of 5 per cent. To check the consistency of our choice of parameters, we estimate the sacrifice ratio obtained from the model and match it with the sacrifice ratio calculated from the observed data for the disinflationary period of 2008Q2–2010Q3, following Ball (1994). The observed sacrifice ratio (amount of output that must be forgone to achieve a given permanent reduction in inflation) turns out to be 2.0 for headline inflation, while the model’s sacrifice ratio stands at 1.8.

Table 18.2Calibration
Output Gap Equation
β1AR(1) parameter0.69
β2Coefficient on real monetary conditions (rmci)0.47
β3Coefficient on the foreign output gap0.05
β4Weight of the real exchange rate gap in rmci0.30
Core Inflation Equation
γ1AR(1) parameter0.65
γ2Coefficient on real marginal costs (rmc)0.51
θWeight of the real exchange rate gap in rmc0.20
αAR(1) in inflation expectations process0.50
Food Inflation Equation
λ3AR(1) parameter0.35
λ4Coefficient on international food price pressures0.17
λ5Coefficient on the output gap0.06
Oil Inflation Equation
λ6AR(1) parameter0.35
λ7Coefficient on inflation expectations0.57
Headline Inflation
w1Core inflation weight0.53
w2Food inflation weight0.36
Exchange Rate Rule
η1Coefficient on the target exchange rate0.95
σ1AR(1) parameter0.80
Monetary Policy Rule
τ1Smoothing parameter0.45
τ2Coefficient on inflation forecast deviation from target2.10
τ3Coefficient on the output gap0.90
τ4AR(1) parameter in the inflation target process0.50
ψ1Persistence, long-run real interest rate0.45
ψ2Persistence, long-run output growth0.38
ψ3Persistence, long-run real exchange rate0.55
Foreign Block
a1Persistence in output gap0.80
a2Persistence, real interest rate trend0.50
a3Smoothening parameter in US Taylor rule0.80
a4Coefficient on expected inflation deviation from target3.50
a5AR(1) parameter0.30
Steady state/long-run values
r¯ssLong-run real interest rate3.50
Δy¯ssLong-run output growth rate7.50
Δz¯ssLong-run real exchange rate change-1.00
π¯Inflation target5.00
r¯ss*Foreign long-run real interest rate0.50
π¯*Foreign inflation target2.00
Standard deviation of shocks
εygapOutput gap shock0.15
εcoreCore inflation shock0.50
εfoodFood inflation shock1.50
εoilOil inflation shock1.50
εiMonetary policy rule shock0.60
ϵπ¯Inflation target shock3.20
εsUncovered interest rate parity shock0.60
ϵΔsTExchange rate target shock1.50
ϵr¯Long-run real interest rate shock0.20
ϵΔy¯Long-run output growth shock0.27
ϵΔz¯Long-run real exchange rate shock0.36
ϵygap*Foreign output gap shock0.25
ϵr¯*Foreign long-run real interest rate shock0.75
εi*Foreign interest rate shock0.45
επ*Foreign inflation shock1.30
εoilWorld oil prices shock1.50
εfoodWorld food prices shock1.50

Figures 18.218.4 present a set of the model impulse response plots that illustrate its basic properties.12 A positive aggregate demand shock (εygap = 1) translates into increases in core, food, and oil inflation by 0.55 per cent, 0.4, and 0.35 per cent (all presented on a quarter on quarter basis), respectively. The central bank then responds by tightening monetary policy and increasing the interest rate. Inflation returns back to target as the exchange rate appreciates and a negative output gap opens up. Figure 18.3 presents the responses to supply shocks to core (εcore = 1), food (εfood = 1), and oil (εoil = 1) inflation. In all three cases, the central bank responds by tightening policy, but less so in the case of shocks to food and oil inflation. Accordingly, in all cases tighter policy leads to a negative (even though small) output gap and an appreciation of the exchange rate. Figure 18.4 presents responses to an interest rate shock.

Figure 18.2.Impulse Response Functions I (Demand Shocks)

Figure 18.3.Impulse Response Functions II. Supply Shocks: Core (solid line), Food (long-dashed line), and Fule (short-dashed line)

Figure 18.4.Impulse Response Functions III (Interest Rate Shock)

4 Filtering Rwandan Data Through the Model

Written in its state-space form, the model allows for the unobserved variables (state variables) to be estimated with the Kalman filter.13Figures 18.518.7 present the trend and gap components of the real exchange rate (zt), the real interest rate (rrt), and output (yt), respectively. The estimate of the output gap permits to identify a complete business cycle between 2008H1 and 2011H1, and a second one unfolding after 2011H2. The model captures well the negative effects of the global financial crisis on output, and the subsequent expansion on the back of more accommodative policies, as indicated by a negative real interest rate gap. The negative effect of the 2012 aid shock on economic activity is also evident, with the opening up of a negative output gap (of about 2 per cent of GDP in 2013). The real depreciation triggered by this episode and the consequent tightening of monetary policy (as signalled by higher real interest rates) is also well captured by the model.14

Figure 18.5.Real Exchange Rate Trend and Gap

Figure 18.6.Real Interest Rate Trend and Gap

Figure 18.7.Output Trend and Gap

The model also allows us to decompose the observed data into the different structural shocks hitting the economy. The results (Figures 18.818.12) indicate that exchange rate shocks play an important role in inflation dynamics all throughout the period under consideration. Likewise, supply shocks in the food sector (and less so in the oil sector) seem to play a large role. Core inflation dynamics, on the other hand, seem to be dominated by exchange rate shocks, monetary policy shocks, and supply shocks. The systematic nature of monetary policy shocks in the determination of core inflation, particularly since 2010 could either indicate that there is an additional element to include in the model’s monetary policy rule (such as the role played by monetary aggregates), or that there are in fact areas of improvement in the way monetary policy is currently conducted to better anchor inflation expectations.

Figure 18.8.Shock Decomposition of Headline Inflation (YoY)

Figure 18.9.Shock Decomposition of Core Inflation (YoY)

Figure 18.10.Shock Decomposition of Food Inflation (YoY)

Figure 18.11.Shock Decomposition of Oil Inflation (YoY)

Figure 18.12.Shock Decomposition of the Output Gap

4.1 Forecast

One way of assessing the reliability of the model is by evaluating its in-sample forecasting capabilities. We also present an out-of-sample forecast to showcase the usefulness of the tool to conduct policy analysis in a forward-looking context.

4.1.1 In-Sample Forecast

In-sample forecasts are generated on a quarterly basis, for the period 2007Q4–2013Q4. We assume an equilibrium real exchange rate appreciation of 1 per cent, with inflation converging to a target of 5 per cent. For this exercise, the in-sample variables of the rest of the world block and the world oil and food prices are exogenous and equal to their observed values (the trajectories of the external variables and world food and oil prices are shown in Figure 18.13).

Figure 18.13.Exogenous Variables

The forecasts are shown in Figure 18.14. The model predicts the disinflation of 2009 fairly well; however, it underestimates the magnitude of the inflationary pressures in 2010. The model does tend to closely track inflation in 2011 and after 2012, also helped by the fact that inflation was less volatile than in the previous years. All in all, the results suggest that the model is broadly satisfactory, particularly at short horizons. However, its performance is somewhat less reliable in the presence of large exogenous shocks, as in 2008. Nevertheless, a comparison of the in-sample model forecasts with those of a simple random walk model shows that the model outperforms the random walk model, especially at longer horizons

Figure 18.14.In-sample Forecast of the Main Variables

4.1.2 Out-of-Sample Forecast

The main outputs of the out-of-sample forecast, starting from 2014Q1l are presented in Figure 18.15. This is in a context where the aid situation has normalized with the return of donors, but near term growth has slowed down, with 2014 growth projected at 6 per cent, while we estimate potential GDP growth to stand at 7 per cent. The economy is thus operating below its potential, and the negative output gap (2 per cent of GDP at the start of the simulation) is not expected to close until the first half of 2015. The exchange rate pressures noticed at the peak of the aid shock have subsided significantly. The Rwandan franc depreciated by nearly 12 per cent between January 2012 and December 2013, but the pace of depreciation slowed in the first quarter of 2014, reflecting both the slowdown in economic activity and resulting decline in demand for imports as well as a return of donor flows. The premium between the official and market exchange rates is now below the ordinary 2 per cent. On the external front, the main change we anticipate over the forecast horizon is a normalization of US interest rates as after 2015. Commodity prices are expected to remain relatively stable over the period.

Bearing in mind these conditions, the baseline forecast suggests that headline inflation will remain within a range of 3 to 5 per cent in 2014, and the NBR’s current monetary policy stance can be considered as appropriate. However, should growth weaken further there may be room to ease monetary conditions to spur economic activity.

Figure 18.15.Out-of-sample Forecast of the Main Variables

5 Conclusions

The policy decision-making process by the NBR’s monetary policy committee is currently anchored on an analysis of mostly backward-looking indicators, which at times are available after a considerable lag. This has occasionally hindered the NBR’s response to economic developments, resulting in an expansionary policy being maintained despite rising inflationary pressures. Hence, the recent efforts by the NBR in developing tools to enhance its policy analysis apparatus, by introducing and FPAS, represent a step in the right direction. Models like the one presented here provide a framework within which to analyse monetary policy in a systematic and forward-looking way, which is the reason why they usually stand at the centre of such systems.

The use of the model allows us to show the key contributors to inflation in Rwanda, placing special emphasis on tracing the effects of food and oil price developments, as well as the nature of the exchange rate regime. For the period under consideration, exchange rate and supply shocks have played an important role in inflation dynamics. The results also suggest that monetary policy shocks have contributed to macroeconomic developments.

The NBR is considering adopting an inflation targeting regime and has been working to introduce the different elements of such a framework. Improving the policy decision-making processes by introducing forward-looking analytical tools so as to better gauge the response of the economy to policy changes is also likely to translate into superior policy and macroeconomic outcomes. This chapter is a contribution in that direction. However, in putting together the model in this chapter we have had to abstract from many of the complexities of the actual operating and policy framework in Rwanda, notably the complex and interrelated roles of money aggregates, the policy interest rate, and the exchange rate. Further progress in clarifying the instruments and objectives of the central bank will make this process more successful. It will also enable the execution of a clear and transparent communication strategy and more broadly enhance the credibility of the central bank.


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For example, in late 2008 and in response to a liquidity squeeze, the NBR lowered reserve requirements and introduced new credit facilities for commercial banks. However, at the same time, the NBR increased the KRR, to promote deposits.


However, ample flexibility has been observed, as when in June 2011, the NBR allowed some banks to miss the mandatory reserve requirement levels without the required penalties to meet the reserve money target. This is tantamount to implicitly relaxing the reserve requirement, implying a further loosening of monetary policy.


For simplicity, we use US variables to proxy for the rest of the world.


The nominal exchange rate is defined as units of domestic currency (Rwandan Franc) per US dollar. The real exchange rate is a bilateral rate vis-à-vis the US dollar.


This specification allows us to capture potential ‘second-round’ effects of supply shocks on core inflation.


See Benes, Hurnik, and Vavra (2008) for alternative ways to model exchange rate dynamics in the context of managed exchange rate regimes.


Modifications to the central bank’s exchange rate policy can be captured either through changes in parameter η, changes in σ1 or, changes in the rate of crawl.


We use the overnight interbank rate as a proxy for the stance of monetary policy in Rwanda. An increase of the interbank rate is interpreted as a tightening of monetary policy whereas a decrease reflects a loosening of policy.


Monthly series are averaged to quarterly frequencies.


See Berg, Karam, and Laxton (2006) and Chapter 15 for guidelines on calibrating this class of model in low-income economies.


In all cases, these correspond to responses to a temporary 1 per cent increase during one quarter in the shock term. The results are presented in deviations from steady state values.


In our case, the set of unobserved states includes the gap (deviations from trend) components. The filtering exercise covers the period from 2008 onwards.


The higher real depreciation after the aid shock was achieved through an upward adjustment in the rate of crawl by the central bank.

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