A. Households

Our modeling of households has the following features. First, households’ intertemporal decisions are influenced by the domestic lending rate ($R_t^L$), reflecting the dominance of banks in financial systems in LICs. Second, consumers may be constrained in their ability to borrow at the lending rate offered by banks. These features are reflected in our Euler equation for consumption:

where π_c,t is CPI inflation, λ_t is the marginal value of wealth, and u_F,1,t is the value of the multiplier associated with the borrowing constraint.¹² The marginal value of consumption is given by:

The parameter χ measures the degree of backward–looking behavior (or habit formation). We assume total consumption is spent on domestic goods and imports following a Leontieff specification, which implies the following demand for domestic goods: C_d,t = ωC_t. This specification captures the view that in low–income countries imports are not close substitutes with domestically–produced goods. The CPI is a weighted sum of import and domestic prices: P_c,t = ωP_d,t + (1 – ω)P_M,_t. The demand for imports is also potentially affected by a borrowing constraint:

with u_F,2,t denoting the marginal value of the constraint.¹³ This restriction allows us to emphasize the impact of a financial shock on the demand for imports rather than on overall consumption (more on this below). Financing for import consumption requires lenders’ acceptance of additional foreign currency exposure. It is plausible that banks may be especially unwilling to finance such exposure during the crisis.¹⁴

Consumers demand deposits from banks, which earn interest at the rate R_D,t. The demand for real deposits is given implicitly by the following function:

where D (*,*,*) is continuously differentiable and homogeneous of order zero, with D_i > 0 for i = 1, 2 and D₃ < 0. Demand for real deposits depends on consumption and the ratio of lending rates and deposit rates; lagged consumption is introduced to generate sluggishness in the demand for deposits.

The supply of labor by consumers is subject to nominal wage rigidities. This results in a Phillips curve for nominal wage inflation $({\pi }_{W,t}=\frac{W_t}{W_{t-1}})$, which depends on future and past wage growth and deviations between the marginal disutility of labor—which is constant—and the marginal value of real wages:

B. Firms

There are two types of firms in the economy: those that produce for domestic consumption and firms that produce export goods for the world market.

C. The Banking Sector

We assume financial intermediation is carried out by a perfectly competitive banking system, which consists of wholesale and retail branches. At the wholesale level the representative bank’s balance sheet is the following:

Banks’ liabilities consist of deposits by residents D_t and foreign debt F_t—denominated in foreign currency but measured here in local currency. Assets consist of loans L_t, government bonds B_bk,_t, and reserves at the central bank H_t, which earn no interest but help banks manage liquidity needs associated with deposits.

Profit maximization by banks lead to several arbitrage conditions. First, arbitrage between local currency returns on domestic and foreign bonds, R_B,t and R_t, respectively, lead to the following relation:

where R_t is given by the uncovered interest parity with world interest rates plus a potential shock to the country risk premium:

Arbitrage between (net) returns on loans and other assets lead to the following relation between wholesale lending rates $R_{L,t}^{*}$ and interest rates on government bonds:

where we have included an exogenous component to the risk premium on loans (u_F,3,t). Note that wholesale lending rates are not directly relevant for private sector decisions.

Finally, liquidity needs to manage deposit results in the following implicit demand for H:

where H (*,*) is continuously differentiable and homogeneous of degree zero, with H₁ > 0 and H₂ < 0. Banks demand for liquidity is also subject to a shock u_F,4,t. As a result of these liquidity needs there is a negative premium on the interest rate on deposits:

with Λ₁ < 0.

At the retail level, branches receive funding from wholesale branches and extend credit to households with some degree of monopoly power. Retail lending is risky and rates are subject to adjustment costs, all of which results in the following pricing equation for loans:

where g_t is given by:

and a (*) on top of a variable denotes its steady state value. Three factors affect the risk premium on lending rates. The first factor is the external finance premium g_t. It is usually micro-founded by assuming that returns on loans are risky, reflecting idiosyncratic risk on the borrowers part, which is costly for banks to verify and requires a compensating premium. This informational asymmetry is greatly reduced if borrowers can provide their own funds (capital in this case) to finance part of their project, which is why lowering the ratio of gross repayments to the value of capital reduces the premium. The second factor is the exogenous component u_F,3,t in equation (16). Finally the dynamic path of the lending rate is also affected by the adjustment costs at the retail level.

Beyond the arbitrage conditions between different interest rates, we also allow for the possibility that banks may ration borrowers at the prevailing lending rate. The rationing is captured by the shocks u_F,1,t and u_F,2,t. While we do not model the rationing formally, we believe there are reasons why banks may be reluctant to raise interest rates sufficiently to eliminate excess demand for loans, either because of adverse selection (as in Stiglitz and Weiss (1983), costly state verification (as in Williamson (1987)) or moral hazard (as in Bester and Hellwig (1987)).

We model a decrease in banks’ appetite for risk as a simultaneous increase in shocks u_F,i,t, for i=1…,4. As a result of higher aversion, banks simultaneously increase the premium on lending rates (u_F,3,t in equation (16)), ration their lending to the domestic private sector, including import finance (shocks u_F,1,t and u_F,2,t in equations (1) and (2), respectively) and increase their demand for liquidity (u_F,4,t in equation (17)). This simultaneity justifies treating these proximate shocks as coming from one single shock—the increase in banks’ appetite for risk, which we denote u_F,t. We impose the following normalization:

where the μ_iS are chosen to improve the fit of the model.

D. Monetary Authority

We allow for different options regarding how the monetary authority operates, i.e., what variables are targeted by the central bank, and what instruments—or combinations of instruments—are used. We allow for such flexibility in this block of the model in order to help account for systematic differences between policy choices in LICs and advanced economies, and to compare among various policy rules.

Here are the policy rules we model:

• A reserve money growth rule:

  $\frac{H_t}{H_{t-1}}=1-K_{\pi ,H}({\pi }_{c,t+1}-1)-K_{D\mathrm{.}H}(\frac{D_t}{D_{t-1}}-1)-K_{L,H}(\frac{L_t}{L_{t-1}}-1)-u_{M,t}$

  The reserve money growth rule nests various specifications: (i) an inflation targeting regime implemented using reserve money growth as the policy instrument (K_D,H = K_L,H = 0, K_π,h > 0); (ii) a constant money growth rule (K_D,H = K_L,H = K_π,H = 0); (iii) a rule that combines inflation targeting with broad money targeting (K_D,H > 0, k_π,h > 0); (iv) a rule that targets credit growth K_L,H> 0). Note that rule (iii) is consistent with current practice in some LICs, where broad money is often an intermediate target whereas reserve money serves as an operational target.

• Standard Taylor rule with the interest rate on government bonds being the main policy instrument:

Note that in both of types of rules we abstract from targeting the output gap since this variable is difficult to assess in low–income countries, with quarterly GDP often unavailable. Without loss of generality, our policy rules imply a zero inflation target: when comparing the model with the data we add a constant term—reflecting the authority’s implicit inflation target—to the inflation dynamics of the model. Depending on the rule, a tightening of monetary policy can be modeled as a positive shock u_M,t to the policy rate/short term T–bill rate R_B,t or a liquidity withdrawal –u_M,t on H_t.

We also keep track of the central bank balance sheet:

where B_cb,t denotes the central bank’s holdings of government debt and F_t are the central bank’s international reserves (measured in local currency). In this paper we set F_t to zero, since intervention in the foreign exchange market did not play an important role in Zambia during the global crisis. Regardless of the policy regime, we assume the central bank implements policy by varying B_cb,t. Finally we define a measure of relative or “excess” liquidity in the banking system, which is given by the inverse of the money multiplier:

E. The Government

The government taxes economic agents, spends on a basket of goods similar to consumers’, issues debt, and pays interest. Its budget constraint is given by:

where G_t is real government spending, T_t is the nominal tax revenue and B_t is the total stock of government debt (B_t = B_bk,t + B_cb,t). We assume the government only pays interest on debt held by commercial banks. Consistent with the tax structure in many low–income countries, where import duties make a large share of government revenue, we assume that tax revenue (in percent of nominal GDP) is sensitive to the value of imports:

$Y_t^N$ is the level of nominal GDP and Φ measures the sensitivity of tax revenues to imports.

Government debt is anchored by the following spending rule:

This rule ensures that government debt outstanding converges to a given long run level $\frac{B^{bk}}{Y^N}$. The parameter ρ_G measures the sluggishness in real government spending (in percent of GDP), while τ_G—together with ρ_G—measures the speed of adjustment to reduce debt levels.¹⁵

Note that the country’s resource constraint requires Y_t = X_t + C_d,t + ωG_t, while nominal GDP is defined as $Y_t^N=P_{c,t}(C_{d,t}+G_t)+P_{X,t}{X}_t-P_{M,t}{M}_t$ (with M_t = C_M,t + M_Y, + (1 - ω)G_t).

F. Relationship with the Rest of the World

We close our model by keeping track of the country’s balance of payments:

A. The Zambia data set

We collected data for 15 quarterly macroeconomic and financial variables.¹⁶ On the external sector the data includes the terms of trade, imports, the current account, and the nominal exchange rate. Data on the banking/monetary sector includes reserve and broad money, credit to the private sector by the banking system, interest rates on treasury bills, and lending rates. As in our model, we use the ratio of reserve money to broad money to assess liquidity in the banking sector, rather than the measure of liquidity used by the authorities—banks’ reserves in excess of those needed to satisfy regulatory requirements.¹⁷ On the fiscal side, we collected data on total revenues, spending and the stock of government debt. On the real sector side, we have quarterly data on GDP, interpolated from annual data.

We present the data as follows. About half of the variables (terms of trade, real credit to the private and public sector, real imports, real GDP growth, and excess liquidity) are expressed as percentage deviations from a deterministic trend or constant, which we calculate using pre-crisis data for each variable. Nominal variables are expressed in percentage points—Zambia’s inflation target during this period is assumed to be 10 percent. Finally, to help understand the magnitude of the macroeconomic adjustment, government revenues, government spending, and the current account are measured in percent of GDP.

B. Calibration and functional forms

Simulating the model requires specifying functional forms for functions D, F, H and Λ. Consistent with the optimization in Appendix A, the functional forms are as follows:

Regarding calibration, Table 1 contains all parameter values and key steady state ratios, organized by economic agent. Choosing parameter values for Zambia was a difficult exercise, and our calibration is tentative. To our knowledge, there has been little empirical work—either micro–level studies or econometric estimates of macro models—that would help inform the calibration; more work is clearly needed in this area. Our approach was the following:

Table 1:

Calibration of model parameters and steady-state ratios

Table 1:

Calibration of model parameters and steady-state ratios

Households
Parameter	Value	Parameter	Value
β	0.97¼	χ	0.3
ψc	0	ω	0.98
ξw	0.02	c1	1
ϱ1	0.2	ϱ2	0.2
$P_c^{-}C/\overline{D}$	1
Firms
γN	0.50	γM	0.20
δ	0.02	α	0.50
ξc	0.03	σK	1
ΨX	60	Ψy	3
Banks
g1	0.005	h1	0.02
D/H	3.49	F /Y N	0.60
μ2	0.25	μ3	0.12
μ4	0.045	ξR	0.06
Monetary Authority
Kπ,H	1.5	KD,H	0
ρH	0
The Government
ρ	0.12	τG	0.1
ρG	0.95	T /Y N	0.23
$P_c^{-}G/{\overline{Y}}^N$	0.22	B bk Y N	0.60

View Table

Table 1:

Calibration of model parameters and steady-state ratios

Households
Parameter	Value	Parameter	Value
β	0.97¼	χ	0.3
ψc	0	ω	0.98
ξw	0.02	c1	1
ϱ1	0.2	ϱ2	0.2
$P_c^{-}C/\overline{D}$	1
Firms
γN	0.50	γM	0.20
δ	0.02	α	0.50
ξc	0.03	σK	1
ΨX	60	Ψy	3
Banks
g1	0.005	h1	0.02
D/H	3.49	F /Y N	0.60
μ2	0.25	μ3	0.12
μ4	0.045	ξR	0.06
Monetary Authority
Kπ,H	1.5	KD,H	0
ρH	0
The Government
ρ	0.12	τG	0.1
ρG	0.95	T /Y N	0.23
$P_c^{-}G/{\overline{Y}}^N$	0.22	B bk Y N	0.60

• All the relevant steady state ratios ($P_C^{-}C/\overline{D},\overline{D}/\overline{H},\overline{F}/{\overline{Y}}^N,\overline{T}/{\overline{Y}}^N,P_C^{-}G/{\overline{Y}}^N,{\overline{B}}^{bk}/{\overline{Y}}^N$) are calibrated to the Zambian economy. Parameters γ_M, ω and α are also chosen to replicate the degree of openness of the economy.

• Several parameters (X,ξ_w, ζ, γ_M and ξ_c) are set in accordance with recent empirical work on African countries (Berg, Portillo and Unsal (2010), Berg and others (2010)).

• Some parameters (β, δ, σ_K, Ψ_y and ξ_R), for which there is no Zambia—or LIC—specific data, are set to standard values in the literature.

• We set g₁ to zero to explicitly remove the financial accelerator from the analysis. This transmission channel was not helping match the dynamics of the crisis. Since we believe it may be relevant in other situations and for other countries, we leave it in the model for future use.¹⁸

• The value of ψ_X reflects our prior that real exports in Zambia are likely to be fairly unresponsive to movements in the real exchange rate, mainly because copper exports represent 75 percent of total exports.

• We choose the money growth specification for monetary policy. In our baseline we assume monetary authorities do not respond to broad money or loan growth, which is consistent with the response during the crisis. We relax that assumption in our sensitivity analysis.

• On the government side, we choose Φ, τ_G, and p_G to broadly reproduce the path of fiscal variables during the crisis.

• The remaining parameters are chosen to improve the fit of the model. On the consumers’ side this applies to ϱ₁ and ϱ₂ (parameters that affect the income elasticity of broad money demand). On the banks side, the parameters chosen this way are t (related to the interest elasticity of reserve money demand), μ₂, μ₃ and μ₄ (related to the shock to banks’ appetite for risk). Note that ϱ₁, ϱ₂ and t help shape the path of broad and narrow money during the crisis but do not alter the sign of the response. We discuss the choice of μ_i’s in subsection F.

As can be seen from our calibration of the last set of parameters, our approach differs somewhat from a pure calibration exercise. In particular, we use the information derived from fitting the model to the data to improve our choice of the last group of parameters, some of which do not have a specific micro-foundation to support them. This approach could be defined as informal/partial estimation. We believe our approach is justified by the the purpose of our model, which is to serve as a data-consistent story-telling device.

C. Overview of shocks and the transmission mechanism

Before analyzing Zambia’s experience during the crisis, we briefly present impulse responses of key model variables to each of the four shocks in our model. This will help illustrate the underlying transmission channels. Figure 2 summarizes the model’s response to a terms of trade improvement of 20 percent and to a loosening of monetary policy expressed as an increase in the growth rate of reserve money by 10 percent, respectively. Both shocks lead to a temporary increase in domestic demand and output, as is shown in the path of real imports, money demand and GDP growth. They differ in terms of their effect on inflation: the terms of trade improvement appreciates the exchange rate and lowers inflation, while the monetary loosening leads to higher inflation and nominal depreciation. The effects of the two shocks are also qualitatively different for the volume of credit to the private sector. While the policy loosening encourages higher borrowing and an increase in real credit, the terms of trade improvement results in a decrease in the volume of private credit, reflecting consumption smoothing.

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Impulse response functions of key variables to a terms of trade shock and a monetary policy shock.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

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Figure 3 summarizes the impulse response functions to the two risk shocks in the model: a 10 percentage-point increase in the shock to the country-wide risk premium u_R, and a shock to the banking sector’s risk appetite u_F, such that—all else equal—the premium on the “notional” lending rate would increase by 5 percentage points. The increase in the country premium leads to a nominal depreciation, with upward pressure on inflation and an ensuing increase in policy and lending rates, which in turn leads to a contraction in domestic demand and output. The banking shock, on the other hand, leads to a squeeze in credit and a sharp fall in real imports. The fall in domestic demand triggers disinflation, lowers marginal costs for exporters and together with the drop in imports, improves the current account and contributes to higher GDP.¹⁹ Finally, the shock to banks’ risk appetite would—by itself—generate a nominal and real appreciation, as the demand for imports would fall.

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Impulse response functions of key variables to a risk premium shock and a banking shock.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

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Overall, the transmission channels operate as one would expect from a model of this type, although it must be emphasized that some of the shocks are pushing key variables (such as exchange rates and the current account) in opposite directions. The interesting question for the remainder of the paper is whether the model can provide us with explanations to our case study of Zambia, given the particular constellation of shocks and policy responses the country faced during the crisis.

D. Replicating the crisis

Having analyzed the impact of each shock separately, we now combine them together to mimic the impact of the crisis. As mentioned earlier, the aim here is to replicate Zambia’s external environment during this period. Our approach is as follows. We set the path of the terms of trade shock u_T,t and the risk premium shock u_R,f,t such that the model’s terms of trade and nominal exchange rate exactly replicate their counterparts in the data from 2008:Q4 to 2010:Q2.

Regarding the shock to the banks’ risk appetite u_F,t, we set their path so as to match the current account from 2008:Q4 to 2009:Q4. We focus on the mapping between the current account and banks’ appetite for risk for the following reasons. Given the structure of the banking sector in the model (all of the country’s financing including foreign borrowing goes through the banking sector), this shock has direct implications for the current account behavior. This linkage is consistent with the literature on sudden stops, where the external shock enters the model in the same way as our shock μ_F,1,t in equation (1). ²⁰ In addition, the mapping between the banking shock and the current account reversal is also consistent with the fact that Zambia’s banking system is largely foreign-owned, so that a change in banks’s attitude toward domestic loans would likely be reflected in capital flight.²¹

Regarding monetary policy, we set the path of shocks u_M,t, such that the model replicates the observed path of the 90-day T-bill rate in Zambia during the same period. As will become evident later, we believe the behavior of monetary authorities cannot be characterized by a systematic rule but rather as a sequence of discretionary policy measures. The use of shocks to mimic the policy response is therefore more appropriate. Finally, to ensure consistency with the standard analysis of impulse responses in this type of models, we assume the path of shocks is fully anticipated at the beginning of our simulations (which corresponds to 2008Q3 in the data).

The mapping between shocks and selected variables warrants some discussion. We use the IRIS toolbox, developed by one of our coauthors, to implement this mapping. The procedure requires (i) solving the linear approximation of the model using standard rational-expectations techniques, under the assumption that all shocks are anticipated at the beginning of the simulation; (ii) inverting the VAR representation of the model’s solution to recast the shocks as linear functions of the model variables (including leads and lags); and (iii) backing out the sequence of shocks that is necessary to reproduce the path of selected variables. The IRIS toolbox contains built-in functions to carry out this procedure.

E. Baseline results

Conditional on the four “hard-tuned” variables above, we simulate the model’s response and compare the remaining model variables with their counterparts in the data. The starting point of the data and the model is the same in most cases, except for inflation and the growth rate of reserve money, for which the model’s starting point is the pre-crisis average. By doing so, we are assuming that the economy was broadly at trend before the crisis hit. We return to the pre-crisis behavior of inflation in the last section.

First, we characterize the evolution of the key observed variables during the crisis, starting in 2008-IV (Figure 2). Along with the terms of trade deterioration we observe an immediate depreciation of the nominal exchange rate S. The exchange rate depreciation feeds initially into higher inflation. The current account reversal is in the order of 20 percentage points of quarterly GDP, reflecting the exit of foreign investors.²² The capital flight is associated with a large contraction in the volume of credit issued by the domestic banking system and an increase in lending rates, which prevents private agents from borrowing abroad to smooth out the effects of the crisis. Aggregate demand contracts significantly as a result of the credit crunch and inflation subsequently declines, while real deposits in the banking sector decrease by over 15 percent.²³ Note that the behavior of GDP, which has been interpolated to generate quarterly series, is not consistent with the overall macroeconomic picture. The fiscal outcome worsens—especially revenues—and the outstanding stock of government debt increases by about 20 percent.

In this context the initial monetary policy response can be characterized as contractionary. Interest rates on treasury bills increase by about 400 basis points between July 2008 and July 2009. This is associated with a decrease in the growth rate of the monetary base and contributes to the contraction of broad money and the increase in lending rates.

Starting in July 2009 however, there is a reversal in the monetary stance in response to the slowdown. Liquidity is injected into the banking system (H increases) above the pre-crisis level and the T-bill rate drops sharply, by about 1300 basis points by July 2010. This loosening policy drives down the lending rate and brings aggregate demand slowly back towards the baseline level. The monetary loosening coincides with an recovery of the terms of trade which appreciates the exchange rate, lowers inflation, and supports the recovery in demand.

How does the model predictions compare with the data? In general, the model performs well qualitatively and comes close to the data for the lending rate, inflation rate and import demand. For the other variables, the model predicts correctly the direction although the magnitudes are, as can be expected, not always closely matched. For example, in the model, credit to the private sector contracts faster and stronger than in the data, as is also the case with real money demand, while credit to the government is predicted to surge faster than in the data. The fiscal variables (revenues and spending as share of GDP) are slightly more volatile in the data than the model predicts. Clearly, the model cannot account for all sources of rigidities or policies that may shape the path of the economy. There are more sources of government and private credit funding, such as aid donors, non-banks etc. which may generate either additional volatility or delays in some of the macro responses that the model does not capture.

Regarding GDP growth however, the model’s prediction are completely at odds with the data: the model predicts a large contraction in GDP while the data indicates no such contraction. One interpretation is that in reality, unlike the model, the decline in external financing is not contractionary, perhaps because output is not demand determined. However, part of the divergence may be explained by positive shocks to the supply side of the economy.²⁴ Mismeasurement of economic activity may also account for some of the gap.

F. Shock decomposition

It is helpful to analyze how the different shocks contributed to each variable’s dynamics.²⁵ Figure 5 presents the path of all three external shocks. The initial path of all three shocks is consistent with the above narrative: there is an increase in banks’ risk aversion (positive u_F) through the first five quarters, a deterioration in the terms of trade (negative u_T) that recovers by the end of 2009, and an increase in the country-risk premium from 2009 to 2010Q2 (positive u_R).²⁶

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Overview of the baseline simulation Baseline simulation (green) versus data (red with cross). Units are percentage or percentage point deviations from pre-crisis trend at quarterly frequency.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

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Tuned paths of external shocks.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

Tuned path of the banking/financial shock (uf), the terms of trade shock (ut) and the country risk premium shock (ur). Units are percentage deviations from pre-crisis steady state at quarterly frequency.

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Figure 6 presents the shock decomposition for three nominal variables: the nominal exchange rate, the lending rate and the inflation rate. One striking observation is that the shocks are generating opposing effects on the dynamics of these variables. The terms of trade and external risk premium shocks (u_T, u_R) are generating pressures for nominal exchange rates to depreciate and lending rates and inflation to increase, while the banking sector shock (u_F) is having the opposite effect.

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Structural shock decomposition 1.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

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More importantly, the banking shock plays an important role in the transmission of the crisis. This reflects the dominant role of the banking sector in our model. The decrease in banks’ risk appetite through the end of 2009 exerts a downward pressure on inflation and the exchange rate, as it generates a decline in consumption, including for imports. The overall effect of the banking shock on lending rates is negative, despite the appearance of the shock in equation (16): as the shock makes private demand contract and inflation fall, the endogenous response of monetary policy—combined with a contraction in the demand for money—makes T-bill (and lending) rates fall.

Figure 7 shows the shock decomposition for the volume of credit to the private sector, credit to the public sector and real imports. The figure reveals how strongly the credit rationing of banks affected the contraction in lending and import demand. In the absence of the banking shock, the other two shocks would have resulted in an increase in lending for smoothing purposes. In the case of government debt, all shocks are initially contributing to its increase.

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Structural shock decomposition 2.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

Baseline simulation paths for selected quantity variables and their decomposition to 4 structural shocks of the model. Units are percentage point deviations from pre-crisis trend at quarterly frequency.

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Finally, note that the tightening of monetary policy exacerbates the negative impact of the shocks in the initial quarters. The lending rate is further increased, private credit is further reduced and demand (see imports) contracts slightly more given this tightening policy. However, relative to the contribution of the external and financial shocks triggered by the crisis, the impact of policy is far less decisive for the evolution of demand and economic activity. This reflects the severity and sheer magnitude of the exogenous shocks that hit the economy during this episode. In the following section, the role of monetary policy is discussed in detail.

Having described the performance of the model and how each shock contributed to the path of key variables, we can now justify our choice for the weights on the different components of the bank risk shock (the μ_js). As can be seen from the shock decomposition exercise, the greatest impact of the bank risk shock is on credit volumes. This makes it natural to normalize the shock to consumers’ borrowing constraint (u_F,1,_t) and guided our calibration of the overall bank shock itself (u_F,t). The choice of μ_F,2 is guided by the observation that (u_F,1,_t) is highly contractionary, as it has large effects on aggregate demand. Shocks to import financing (u_F,2,_t) do not have such large effects; a substantial weight on (u_F,2,t) is thus helpful in matching the large current account reversal absent a notable output decline. The increase in lending spreads helps calibrate u_F,3,t—absent such a shock, lending spreads would not increase. Finally, u_F,4,t helps track the behavior of reserve money. In its absence the model would require an implausibly large contraction in reserve money to replicate the path of the T–bill rate.

G. The role of the monetary policy response: shock counterfactuals

Recall that the monetary policy rule is specified in terms of the growth rate of reserve money, reproduced here for convenience:

with discretionary deviations from the rule captured by the shock process u_M,t.

Figure 8 displays the decomposition of the dynamics of reserve money growth, the T–bill rate and real broad money. Not surprisingly, the monetary shock accounts for most of the movements in reserve money growth and the T–bill rate. In other words, fluctuations in monetary policy are directly responsible for the behavior of two key nominal variables (short rates and reserve money growth). This is not true of real money variables: most of the variance of real broad money balances is accounted for by the real shocks.

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Structural shock decomposition: monetary variables.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

Baseline simulation paths for the monetary variables (reserve money growth, T-bill rate, broad money) and their decomposition to monetary policy shock (um) and exogenous (real) shocks (exog. shocks). Units are percentage point deviations from pre-crisis trend at quarterly frequency.

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To assess the role of policy, we first simulate the model without policy shocks. Figure 9 compares the model dynamics with and without policy shocks. In contrast with the previous stop and go pattern, reserve money growth is now mostly flat. Given the contraction in demand for broad money, this results in an initial decline in the T-bill rate, which amplifies the nominal depreciation and raises inflation. At the same time, the increase in the lending rate is not as large, as liquidity is more abundant than under baseline. Another clear effect of the neutral monetary policy is the dampening of the increase in outstanding public debt since the lower T-bill rate implies lower debt servicing costs.

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Counter-factual simulation 1: flat money growth.

Citation: IMF Working Papers 2012, 094; 10.5089/9781475502848.001.A001

Baseline simulation (blue) versus counter-factual (red with cross). Units are percentage or percentage point deviations from pre-crisis trend at quarterly frequency.

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In terms of real variables, the effect of the accommodating policy stance appears to be limited. The contraction on import demand is slightly smaller, as is the contraction in credit to the private sector. A closer look reveals sizeable effects however. Table 2 summarizes the relative performance of alternative policy responses, relative to the baseline, along a number of dimensions. The average difference between private spending (Ct) under “stop and go” and under the more neutral stance, over the period 2009:I to 2009:IV, is 2.8 percent of steady state spending. During the same period the model predicts a moderately higher inflation—3 percentage points higher—although in line with Zambia’s implied inflation target of 10 percent, while the nominal exchange rate would have been 12 percent more depreciated.

Table 2:

Model performance across alternative monetary policy responses

Each row indicates the average difference between the alternative monetary policy regime and the baseline, in percent of that variable s steady state value, in 2009.

Table 2:

Model performance across alternative monetary policy responses

Monetary Policy	Neutral	Loose	Broad Money Targeting	Credit Growth Targeting	Taylor Rule
Private Spending	2.8	5.4	3.4	5.5	4.1
Inflation	3.0	6.2	3.3	7.3	6.1
Nominal depreciation	12.4	24.0	14.7	31.8	29.6

Each row indicates the average difference between the alternative monetary policy regime and the baseline, in percent of that variable s steady state value, in 2009.

View Table

Table 2:

Model performance across alternative monetary policy responses

Monetary Policy	Neutral	Loose	Broad Money Targeting	Credit Growth Targeting	Taylor Rule
Private Spending	2.8	5.4	3.4	5.5	4.1
Inflation	3.0	6.2	3.3	7.3	6.1
Nominal depreciation	12.4	24.0	14.7	31.8	29.6

Each row indicates the average difference between the alternative monetary policy regime and the baseline, in percent of that variable s steady state value, in 2009.

Figure 10 displays simulation results if monetary policy had actually been loosened, i.e., money growth higher than average during 2009:I to 2009:IV. The inflationary effects are now larger (see also Table 2). Inflation is now 6 percentage points higher than under the baseline, while the nominal exchange rate is 24 percent more depreciated. However, the model predicts lending rates would have stayed flat and private spending would have been 5.4 percent higher. One important observation is that, given the steady state value of the T–bill nominal interest rate, there is a lot of room for interest rates to fall—thirteen hundred basis points—without hitting the zero lower bound. In this scenario monetary policy comes very close to hitting the zero bound but stays above it.