I. Introduction

One offspring of the recent, yet recurring debate on the optimal exchange rate regime for emerging market economies is the so-called hollowing-out hypothesis.² According to this, the choices are either a fully dollarized economy or a flexible exchange rate within an inflation-targeting (IT) framework.³ Those emerging market countries that do not favor the dollarization option face a shortened menu. The currency board option has been discarded, and Argentina is still trying to find a way out. The fixed exchange rate regime has been banned after the “Tequila crisis” episode in Mexico and the collapse of the Asian tigers. What are left are flexible exchange rate regimes.

In Latin America, Brazil, Chile, Colombia, and, recently, Mexico have adopted flexible regimes but with a different flavor. All of them have IT schemes, meaning flexible exchange rates with a strong commitment that the inflation rate will not exceed (or fall below) a certain target undertaken.⁴ Along with that pack of countries, Perú and Uruguay are discussing and/or preparing the formal adoption of a similar framework.

The crucial difference between this group of countries (Latin American inflation targeters) and Perú and Uruguay is that the former are not heavily dollarized.⁵ The main question of the paper is what are the consequences of adopting an IT framework in economies that are liability dollarized as having a fully flexible exchange rate regime might imply a large balance sheet problem.⁶ We will focus on evaluating alternative monetary policy rules that might be implemented within this framework rather than comparing the IT option with other possibilities.⁷

Masson and others (1997) suggest that economies should satisfy at least two prerequisites if they wish to consider the possibility of adopting IT: first, the ability to conduct independent monetary policy and, second, the ability to develop a quantitative framework linking policy instrument to inflation. The second requirement is not very restrictive, as the Brazilian case has shown how fast an economy can implement it.⁸ However, the first requirement encompasses a non-fiscal dominance condition and the absence of commitments to another nominal anchor. Most emerging market economies cannot satisfy entirely such conditions. However, it is hard to argue that fiscal dominance is a key factor only in the case of an IT regime. Even a fully dollarized economy that lacks fiscal sustainability will be severely hit when shocks come. Of course, it will make a lot of sense to reinforce the fiscal stance in all emerging market economies no matter what monetary or exchange rate policy are currently following. In terms of our discussion on the optimality of alternative policy rules, we will assume that the government not only engages in a fully credible way in the IT framework but also takes the necessary steps to avoid fiscal dominance.⁹ Also, we are not comparing the transition period of an IT regime but the behavior of that regime when we are in a new steady state.

As stressed by Calvo (2000) a remarkable difference between emerging market and developed economies lies in the importance of the structure of liabilities and the relationship between exchange rate regimes and financial fragility. Together with Mishkin (2000), he argues that a high degree of liability dollarization may hinder the feasibility of adopting an IT. As real exchange depreciations might trigger financial crisis, the exchange rate might become another target for the central bank. This will induce a larger problem as firms and households will expect that the implicit insurance against exchange rate risk will be maintained especially in turbulent times.¹⁰

Another reason might be a concern for the exchange rate pass-through on inflation. Latin American countries have been known for their high inflations in the past. The recent trend of low inflation is something relatively new. In a “truly” flexible exchange rate regime, the exchange rate is supposed to work as an external shock absorber but we are considering rather extreme cases. If the economy has a high degree of pass-through, bad past memories might kick in and inflation will go up, as the real exchange rate is a forward-looking variable. Contrary to the common wisdom, pass-through estimates are now very low in both Perú and Uruguay (see section 2 below).¹¹ However, the data seems to support the hypothesis that the pass-through is regime-dependent¹² hence, the current low estimates should be taken with caution.

Perhaps the most striking distinction between the emerging markets and the developed ones is the fact that the former are incapable of smoothing out sudden changes in their external financing needs.¹³ Calvo (2000) has argued that those Latin American countries with highly dollarized liabilities will be more exposed to these perils. As nobody could argue that a market-based de-dollarization is feasible in the short or medium-term, we should treat that constraint as a permanent one.

A much more important constraint is that in economies with high levels of liability dollarization, the balance sheet channel dominates the more traditional interest rate channel on aggregate demand. The data shows (see Table 1) that as we move towards a more fixed regime, interest rates will be more volatile compared to exchange rates suggesting that is much more costly to freely float than to allow interest rate hikes. Lahiri and Végh (2001) shows that this policy could be rationalized as optimal in the context of a model in which real exchange rate fluctuations are costly.

Table 1.

Volatility of Interest Rate and Exchange Rate

Source: Calvo and Reinhart (2000) Note: “bps” denotes basis points.

Table 1.

Volatility of Interest Rate and Exchange Rate

Type of exchange Rate Regime		Probability that the monthly change in nominal exchange rate falls within:		Probability that the monthly change in nominal interest rate falls within:
		+/- 1.0% band	+/- 2.5% band	+/- 25 bps	+/- 50 bps
Floating		51.7	79.3	33.3	46.7
Managed floating		60.1	87.5	36.3	49.4
Limited flexibility		64.6	92.0	47.5	68.7
Fixed		83.1	95.9	52.3	69.3
Memo:
	United States	26.8	58.7	59.7	80.7
	Japan	33.8	61.2	67.9	86.4
	Perú	45.2	71.4	24.8	32.3
	Uruguay	22.7	92.0	2.7	8.0

Source: Calvo and Reinhart (2000) Note: “bps” denotes basis points.

View Table

Table 1.

Volatility of Interest Rate and Exchange Rate

Type of exchange Rate Regime		Probability that the monthly change in nominal exchange rate falls within:		Probability that the monthly change in nominal interest rate falls within:
		+/- 1.0% band	+/- 2.5% band	+/- 25 bps	+/- 50 bps
Floating		51.7	79.3	33.3	46.7
Managed floating		60.1	87.5	36.3	49.4
Limited flexibility		64.6	92.0	47.5	68.7
Fixed		83.1	95.9	52.3	69.3
Memo:
	United States	26.8	58.7	59.7	80.7
	Japan	33.8	61.2	67.9	86.4
	Perú	45.2	71.4	24.8	32.3
	Uruguay	22.7	92.0	2.7	8.0

Source: Calvo and Reinhart (2000) Note: “bps” denotes basis points.

Notwithstanding all of the above, Perú and (to a lesser extent) Uruguay are considering the pros and cons of adopting an IT framework.¹⁴ Both countries have curbed inflation to one-digit levels after a severe and chronic inflationary history and are among the group of the more dollarized economies in the region and therefore are forced to fear a sudden real depreciation because of the problems that a liability-dollarized economy might pose to a vulnerable financial system.¹⁵ This raises the obvious question of how feasible it is to have an IT regime in that type of economy.

Despite all these restrictions, these two countries and probably others (for example, Turkey) will join the bandwagon and adopt IT strategies to direct their monetary policies.¹⁶ What will be the consequences of following this option in terms of real exchange rate volatility, output, and inflation? The trade-off of having a wider inflation target band with an implicit narrow real exchange rate band calls into question the credibility of the inflation target. However, a wider real exchange rate band might call into question the sustainability of the regime, as the real consequences might be widespread bankruptcies and output instability.

Even though the recommendation regarding avoiding mixing inflation targeting with exchange rate targeting seems completely necessary, it’s hard to draw a precise line in theory (or in practice) between when to lean against the wind and when to let the wind blow.¹⁷ As stressed by Mishkin and Savastano (2000), letting the exchange rate become the de facto nominal anchor of the economy through excessive intervention in a quasi-inflation targeting regime is an example of bad monetary policy under flexible exchange rates.¹⁸

In a recent policy note Perry (2001) considers that is too difficult whether to suggest to this type of country that they follow the full-dollarization option or the IT strategy. As we said before, we are not aiming to answer such a broad question. The goal of the paper is much more limited. If we take for granted that these two countries will move to an IT, which monetary policy rules should be considered the closest cousins of optimal rules? Some of the questions that we will try to answers are (i) should the central bank consider the exchange rate within its monetary policy rule? (ii) what are the consequences of a higher level of activism with respect to the exchange rate? (iii) what are the trade-offs involved in adopting a stricter or a more flexible IT regime?

Prior to the Asian crisis, only a very scant literature discussed the optimality of adopting IT for emerging markets. Most of the IT literature was focused on developed economies. Only very recently, has the literature on IT become much more concerned with developing models for small, open economies.¹⁹ The survey by Mishkin and Savastano (2000) or the work of Frenkel (1999) showed that much more analytical work needs to be done to understand the real benefits from a regime that has not been tried in a partially dollarized environment. Taylor (2000) suggests that models like those in Svensson (2000) or Battini and others (2001) should be adjusted for the emerging markets to reflect the fact that exchange rate fluctuations are more costly there than in developed economies owing to the presence of currency and maturity mismatches.

Fortunately, the literature on monetary policy rules for emerging economies has been expanding very fast in the last year. Among the most important contributors are the following papers: Céspedes and others (2000) argue that in a model in which balance sheet effects a la Bernanke and Gertler (1989) matter, the horse race is won by the flexible exchange rate regime. They show that the contractionary effect of real exchange rate depreciation through that channel will be offset by effects transmitted by other channels (i.e. net worth, risk premium, real wages, and external debt). Therefore flexible exchange rate regimes maintain their superiority as shock absorbers compared with the fixed exchange regimes.

Gertler and others (2001) provide a comprehensive comparison of fixed versus flexible exchange rate regimes. Perhaps the most important result is that the welfare challenge could go either way depending on the extent to which the market value of domestic assets is used to collateralize lending. As the authors suggest, if capital markets are shallow, as in some emerging markets, a fixed exchange rate regime case is much stronger than a flexible exchange regime in the presence of foreign currency debt and a financial accelerator.

Devereux and Lane (2000), following Bernanke and others (2000), specify and calibrate a two-sector model using data for Thailand. They conclude that when constraints in external financing become more important, the benefits associated with monetary policy rules that include the real exchange rate become smaller. The paper’s weakness is that the IT rules are simple Taylor rules and not inflation forecast-based (IFB) rules. In the same way, Cook (2000) calibrates -using data for Southeast Asia- a model in which the entrepreneurs may borrow only in foreign currency and compares it with another model in which there is no liability dollarization. His main findings are that a fixed exchange rate regime is better, in terms of welfare, than a simple IT rule. However, these results assume that agents do not hedge against the exchange rate risk even though they are fully liability dollarized.

In a similar fashion, Ghironi and Rebucci (2000) use Argentina as an example to compare, in terms of welfare, three alternative regimes: a currency board, a full-dollarization regime, and an IT framework. The ranking of these three options depends heavily on the relationship between currency and default risk, which is not included in the model.²⁰

In this paper, we consider a model that borrows the structure of Svensson (2000) and includes an explicit role for balance sheet effects as in Céspedes and others (2000) and Bernanke and others (2000). We calibrate the model for a financially robust economy and a financially vulnerable economy. For the former, we use Australian and New Zealand data, and for the latter we use Peruvian and Uruguayan data.

The structure of the paper is as follows. Section II briefly describes the different monetary policy choices made in Latin America in the past decade. Section III presents a small, open-economy model that will describe the transmission channels and discuss which ones are more important. Section IV presents the model parameterization. Section V discusses the optimality of alternative policy rules for both the financially vulnerable and financially robust economy cases. In addition, we study the optimality of nonlinear policy in the vulnerable economy case. In Section VI we conclude and discuss new avenues for further research.

II. An Overview of Monetary Policy in Latin America

The long-term quest for one digit inflation is about to become a reality for all Latin American countries. After three decades of high inflation the region has come back on track and the downward trend on inflation looks very promising (see Figure 1).²¹

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Latin American Inflation Rate in the 1990s

(logarithmic scale)

Citation: IMF Working Papers 2003, 039; 10.5089/9781451845853.001.A001

Source: IMF

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A key feature of this recent disinflation is that even though the outcome is more or less homogeneous across the board, the policies used have been quite disparate. Table 2 shows that a variety of nominal anchors and exchange rate regimes have been used. Just to add to the differences, Ecuador and El Salvador dollarized their economies while others are moving toward the recent trend of setting inflation targets as a framework for monetary policy (Mexico, Perú).²² However, Latin America has not been the exception in the worldwide trend toward more flexible exchange rates.²³

Table 2.

Monetary and Exchange Rate Policy in Latin America in the 1990s

Notes: The first three columns are based on Corbo (2000), Mishkin and Savastano (2000), and several central bank reports. The third column refers to years in which there has been a substantial reform in the Charter of the central bank or the startup of target announcements. The fourth column might show two regimes as some transition has happened. The fifth column is the annual variation of CPI, according to World Bank reports.

Table 2.

Monetary and Exchange Rate Policy in Latin America in the 1990s

	1	2	3	4	5
Country	Current Nominal Anchor	Explicit Anchor?	Year of Reform	Exchange Rate Regime, Levy Yeyati & Sturzenegger (2000) Classification	Inflation
					1990	2000
Argentina	e	Yes	1991	Fixed	1343.9	-0.7
Bolivia	m	No	1995	Managed-Bands	18.0	3.8
Brasil	π	Yes	1999	Managed-Floating	1584.6	5.5
Chile	π	Yes	1989–99	Floating	27.3	4.7
Colombia	π	Yes	1991–99	Bands-Floating	32.4	8.8
Costa Rica	m	No	1995	Floating-Managed	27.3	10.4
Ecuador	$	Yes	1992–2000	Managed-Fixed	49.5	96.6
El Salvador	$	No	2001	Managed-Bands	19.3	3.4
Guatemala	m	Yes	1995	Floating-Managed	59.6	4.2
Mexico	π	In transition	1995–2001	Managed-Floating	29.9	8.9
Nicaragua	m	No	1992	Not Available	13490.2	9.2
Panamá	$	Yes	1907	Fixed	0.8	1.4
Paraguay	m	No	1995	Managed-Floating	44.1	9.6
Perú	π	In transition	1994	Managed-Floating	7649.6	3.7
Uruguay	e	Yes	1995	Fixed(Bands)-Floating	129.0	5.8
Venezuela	e	No	1992	Fixed (Bands)-Floating	36.5	14.2

Notes: The first three columns are based on Corbo (2000), Mishkin and Savastano (2000), and several central bank reports. The third column refers to years in which there has been a substantial reform in the Charter of the central bank or the startup of target announcements. The fourth column might show two regimes as some transition has happened. The fifth column is the annual variation of CPI, according to World Bank reports.

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Table 2.

Monetary and Exchange Rate Policy in Latin America in the 1990s

	1	2	3	4	5
Country	Current Nominal Anchor	Explicit Anchor?	Year of Reform	Exchange Rate Regime, Levy Yeyati & Sturzenegger (2000) Classification	Inflation
					1990	2000
Argentina	e	Yes	1991	Fixed	1343.9	-0.7
Bolivia	m	No	1995	Managed-Bands	18.0	3.8
Brasil	π	Yes	1999	Managed-Floating	1584.6	5.5
Chile	π	Yes	1989–99	Floating	27.3	4.7
Colombia	π	Yes	1991–99	Bands-Floating	32.4	8.8
Costa Rica	m	No	1995	Floating-Managed	27.3	10.4
Ecuador	$	Yes	1992–2000	Managed-Fixed	49.5	96.6
El Salvador	$	No	2001	Managed-Bands	19.3	3.4
Guatemala	m	Yes	1995	Floating-Managed	59.6	4.2
Mexico	π	In transition	1995–2001	Managed-Floating	29.9	8.9
Nicaragua	m	No	1992	Not Available	13490.2	9.2
Panamá	$	Yes	1907	Fixed	0.8	1.4
Paraguay	m	No	1995	Managed-Floating	44.1	9.6
Perú	π	In transition	1994	Managed-Floating	7649.6	3.7
Uruguay	e	Yes	1995	Fixed(Bands)-Floating	129.0	5.8
Venezuela	e	No	1992	Fixed (Bands)-Floating	36.5	14.2

Notes: The first three columns are based on Corbo (2000), Mishkin and Savastano (2000), and several central bank reports. The third column refers to years in which there has been a substantial reform in the Charter of the central bank or the startup of target announcements. The fourth column might show two regimes as some transition has happened. The fifth column is the annual variation of CPI, according to World Bank reports.

Another key characteristic of Latin America is a by-product of the high-inflation experience. In Table 3 we show a raw measure of liability dollarization. The differences are quite marked across the countries. This combination (liability dollarization and flexible exchange rates) does not seem to be the best one. Berg and Borensztein (2000) suggest that highly liability dollarized economies should move toward a more fixed regime (full dollarization) whereas the rest of the economies should choose a more flexible exchange rate regime.²⁴ As discussed by Calvo and Végh (1996), the distinction between currency and asset (liability) substitution is crucial.²⁵ The macroeconomic instability of Latin American economies linked to real exchange rate fluctuations is not related to the standard argument that it is impossible to pursue an independent monetary policy when currency substitution is widespread. The macroeconomic fluctuations are mostly the consequence of the effects of real exchange rate fluctuations on a banking system and, through it, in firms and households in which liabilities have been heavily dollarized.

Table 3.

Dollarization in Latin America

(percent)

Notes: The figures correspond to the ratio of dollar deposits in the banking system to M3. All data is from the World Bank except for Argentina, Colombia, Guatemala and Uruguay, for which the data comes from their central banks. We could not find data for Brazil and Venezuela.

Table 3.

Dollarization in Latin America

(percent)

Country	1995	1996	1997	1998	1999	Average	Average Growth Rate
Argentina	43.85	44.81	44.35	56.25	50.81	48.02	2.99
Bolivia	78.22	80.09	81.12	84.01	87.22	82.13	2.20
Chile	5.89	4.08	3.74	6.11	8.51	5.67	7.64
Colombia	9.21	15.70	16.32	13.99	10.46	13.14	2.56
Costa Rica	31.00	31.50	34.86	36.95	39.94	34.85	5.20
Ecuador	24.27	28.05	36.84	44.65	71.28	41.02	24.04
El Salvador	4.32	5.95	7.91	7.88	7.95	6.80	12.97
Guatemala	11.81	11.57	14.13	12.64	12.93	12.62	1.82
México	15.76	15.93	13.02	10.89	8.52	12.82	-11.57
Nicaragua	54.12	60.04	61.13	64.22	62.80	60.46	3.02
Paraguay	33.25	38.40	42.53	48.15	54.39	43.34	10.34
Perú	62.48	68.21	66.63	69.54	70.06	67.38	2.32
Uruguay	82.06	83.26	84.57	84.94	85.07	83.98	0.72

Notes: The figures correspond to the ratio of dollar deposits in the banking system to M3. All data is from the World Bank except for Argentina, Colombia, Guatemala and Uruguay, for which the data comes from their central banks. We could not find data for Brazil and Venezuela.

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Table 3.

Dollarization in Latin America

(percent)

Country	1995	1996	1997	1998	1999	Average	Average Growth Rate
Argentina	43.85	44.81	44.35	56.25	50.81	48.02	2.99
Bolivia	78.22	80.09	81.12	84.01	87.22	82.13	2.20
Chile	5.89	4.08	3.74	6.11	8.51	5.67	7.64
Colombia	9.21	15.70	16.32	13.99	10.46	13.14	2.56
Costa Rica	31.00	31.50	34.86	36.95	39.94	34.85	5.20
Ecuador	24.27	28.05	36.84	44.65	71.28	41.02	24.04
El Salvador	4.32	5.95	7.91	7.88	7.95	6.80	12.97
Guatemala	11.81	11.57	14.13	12.64	12.93	12.62	1.82
México	15.76	15.93	13.02	10.89	8.52	12.82	-11.57
Nicaragua	54.12	60.04	61.13	64.22	62.80	60.46	3.02
Paraguay	33.25	38.40	42.53	48.15	54.39	43.34	10.34
Perú	62.48	68.21	66.63	69.54	70.06	67.38	2.32
Uruguay	82.06	83.26	84.57	84.94	85.07	83.98	0.72

Notes: The figures correspond to the ratio of dollar deposits in the banking system to M3. All data is from the World Bank except for Argentina, Colombia, Guatemala and Uruguay, for which the data comes from their central banks. We could not find data for Brazil and Venezuela.

It is not clear what the optimal choice of monetary and exchange rate policy should be if an economy is liability dollarized. Much of the earlier literature focused on only the currency-substitution case in which the choice is much easier.²⁶ However, that is no longer the most critical problem. The low-inflation period that the region has enjoyed has restored much weight to the domestic currency in the typical transactions motive for holding money across countries. So, it seems that the hysteresis hypothesis is valid in the asset and liability dollarization but not in the currency-substitution process.

Another pain in the neck for those emerging market economies that wish to have a floating exchange rate cum inflation targeting is the pass-through coefficient from the exchange rate to domestic prices. We calculate 10-year rolling estimates of this coefficient for Perú and Uruguay.²⁷ The results are shown in Figure 2. Surprisingly, the half-life of an exchange rate shock increased substantially in the 1990s. This could be interpreted as a significant reduction in the inflationary inertia after successful disinflation programs or as an improvement in the credibility of the central banks. These results go against the conventional wisdom that emergent economies that have experienced high and recurrent inflation periods should be characterized as economies with high pass-through coefficients.

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Half-life of an Exchange Rate Shock for Perú and Uruguay, 10-Year Rolling Sample

(months)

Citation: IMF Working Papers 2003, 039; 10.5089/9781451845853.001.A001

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III. A Small, Open-Economy Model

Based on the previous work of Ball (1999), Leitemo (1999), and specially Svensson (2000) on open economies, we propose a small, open-economy model that will help us to derive quantitative results about the transmission mechanisms that underlie a liability-dollarized economy and to discuss the policy options in such an economy. The model is a standard forward-looking, rational-expectations model, in which the monetary authority has a flexible exchange rate regime and cares about inflation and output variability. We explicitly include the financial-vulnerability characteristic as in Bernanke and others (1998) and Céspedes and others (2000).

We want to answer the question of what is the optimal way to implement monetary policy for a given set of economic characteristics as discussed in Poole (1970). In our case, the defining characteristic would be the financial robustness or weakness of the economy.

B. Risk Premium and Contractionary Depreciations

In a general equilibrium setting under price rigidities, the risk premium comes from the correlation between household consumption and the exchange rate.³⁰ Instead of assuming the risk premium as an exogenous process, as in many studies, we link its behavior with the net worth of entrepreneurs that are liability dollarized.

An attempt to endogenize the risk premium within a closed economy with asymmetric information and principal-agent problems is presented by Bernanke and others (2000). They rely on the premise that the deterioration of domestic credit market conditions not only reflects problems at the real side of the economy, but also there are frictions that might constitute the main depressing factor of economic activity. In other words the credit market works as an amplifier of both nominal and real shocks as in Kiyotaki and Moore (1997). The core of this mechanism, known as the financial accelerator, consists of an inverse relation between the external finance premium and the net worth of potential borrowers. The external finance premium is defined as the difference between the cost of external funds and the opportunity cost of firms to finance their operations with internal resources. The net worth of borrowers is the value of their liquid assets (internal funds) plus the collateral value of their illiquid assets minus non-performing liabilities. Under an underdeveloped domestic financial market and when creditors have limited resources to finance investment projects, the equilibrium implies that lenders will have to be compensated with a larger financial risk premium.

Céspedes and others (2000) and Gertler and others (2001) extend the previous analysis to an open economy framework in which firms demand dollar-denominated foreign loans and the external finance premium may be thought as an exchange rate risk premium. These authors model the links between the real exchange rate, the net worth of firms and the risk premium, focusing on balance sheet effects and taking as a starting point the budget constraint of firms that engage in investment Considering that the risk premium is an inverse function of the value of the firms’ net worth and as the cost of external financing comes as given by the foreign interest rate plus the risk premium, Céspedes and others (2000), determine that the evolution of the risk premium follows:

$\begin{array}{ccc}{\phi }_{t+1}-{\phi }_t=-{\psi }_2{x}_t+{\psi }_2(y_t-q_t)-{\psi }_3[(y_t-y_{t/t-1})-(q_t-q_{t/t-1})]& & \mathbf{(}\mathbf{10}\mathbf{)}\end{array}$

where all coefficients are positive constants. The change in the risk premium depends on three factors. The first term is related with changes in the demand for exports, denoted as x_t; given an output level, a rise in exports is compensated by a lower investment that requires less external financing and therefore a lower risk premium. The second term captures the effects of changes in output and the real exchange rate, or in real output valued in dollars. A decrease in y_t − q_t (either due a lower y_t or a higher q_t) implies lower levels of investment and again lower financing needs and lower risk premium. Finally, the third term represents the unexpected changes in output measured in dollars, closely linked to firms’ net worth. An unanticipated real depreciation increases the burden of the debt (denominated in dollars) that lowers the firms’ net worth.

A higher real exchange rate increases the cost of investment relative to firms’ net worth. Moreover, lower output levels will reduce the return of previous realized investments. In that sense, a real depreciation or a decline in output might generate positive effects over the risk premium.³¹

Assuming that exports are a linear function of foreign demand and the risk premium is subject to i.i.d. disturbances ξ_φt, equation (10) could be written as

$\begin{array}{ccc}{\phi }_{t+1}={\phi }_t-{\psi }_1{y}_t^{*}+({\psi }_2-{\psi }_3)(y_t-q_t)+{\psi }_3(y_{t/t-1}-q_{t/t-1})+{\xi }_{\phi ,t+1}& & \mathbf{(}\mathbf{11}\mathbf{)}\end{array}$

As in Céspedes and others (2000) we distinguish two types of economies regarding the impact that balance sheet effects might have. A financially robust economy is one in which the transmission channel of a real exchange rate depreciation to output is dominated by the price effect predicted in the textbook case of open economies. A real depreciation increases output in the short run as the external competitiveness of the economy improves. In the other hand, in a financially vulnerable economy the depreciation is contractionary, basically due to the dominance of the negative wealth effect over the price effect above mentioned. A real depreciation increases the competitiveness of the economy but at the same time reduces the net worth of firms, as they are liability dollarized.

Using equation (11), the elasticity of the risk premium to the real exchange rate is given by:

$\begin{array}{ccc}\frac{\partial ({\phi }_{t+1}-{\phi }_t)}{\partial q_t}={\psi }_3-{\psi }_2=\lambda & & \mathbf{(}\mathbf{12}\mathbf{)}\end{array}$

In a financially vulnerable economy λ is positive while in a robust one, λ is negative so the direct comparison of the values of ψ₂ and ψ₃ characterize the financial vulnerability. Consequently, there is a mechanism by which the potential positive effect of a real depreciation over the risk premium generates, by the interest parity condition, a higher local interest rate and therefore a recession. Likewise, Hausmann and others (2000) suggest that, in a liability dollarized economy with incomplete pass-through, the exchange rate fluctuations have an impact on output via two channels, a direct wealth effect (the balance sheet channel) and a credit channel through an increase in the interest rate. If the first one dominates the second, the depreciation will be contractionary.³²

Céspedes and others (2000) find that the steady state value of λ is proportional to the ratio dollar-debt to investment (see the first three columns of Table 4). A higher dollar debt-to-capital ratio will imply that λ is nonnegative and therefore the economy is more vulnerable.

Table 4.

Financial Vulnerability in Latin America

Notes: The term “Debt” refers to “External Debt”. Columns 1 to 4 correspond to World Bank figures. In column 5 we show estimates of equation (11) with monthly data from the IMF, from January 1991 to December 2000 (see Appendix B2 for further details), except Peru and Costa Rica (from January 1992 to December 2000) and Brazil (from January 1994 to December 2000). Figures in parenthesis are standard errors of estimated coefficients, * denotes statistical significance at 5 percent and ** at 10 percent.

Table 4.

Financial Vulnerability in Latin America

		1		2	3	4		5
Country		Debt/Investment		NPV of Debt to Investment Ratio	Private Debt to Investment Ratio	GDP Services (% of GDP)		Elasticity of Risk Premium to Real Exchange Rate
		1998	1999	1998	1999	1998	1999	1999:01	2000:12
Bolivia		3.10	3.92	2.51	0.43	62.4	63.5	0.2497	(0.1704)
Brazil		1.38	1.33	1.31	0.38	62.8	59.9	0.3349	(0.0314)*
Chile		1.64	2.39	1.96	1.35	57.4	57.4	-0.2105	(0.1638)
Colombia		1.72	3.07	1.68	1.09	61.0	62.9	-0.0492	(0.0210)**
Costa Rica		1.40	1.68	1.33	0.55	56.7	56.8	-0.1044	(0.0402)*
Ecuador		3.11	6.57	2.92	3.45	55.2	50.3	-0.0523	(0.0817)
El Salvador		1.74	1.85	1.55	0.19	60.4	60.0	-0.0979	(0.0371)*
Guatemala		1.31	1.44	1.41	0.20	56.6	56.8	-0.0897	(0.0257)*
México		1.58	1.47	1.54	0.95	66.3	66.8	0.1726	(0.0605)*
Nicaragua		8.40	6.63	7.38	0.42	45.4	45.7	0.0110	(0.0048)**
Paraguay		1.17	1.65	1.10	0.06	48.8	46.1	-0.2323	(0.1348)
Perú		2.12	2.62	2.20	0.67	56.1	55.5	-0.1244	(0.0545)**
Uruguay		3.87	4.13	2.08	1.02	65.3	68.0	0.2136	(0.0462)*
Venezuela		1.83	2.33	1.86	1.73	59.6	68.5	0.2517	(0.1248)**
Memo:
	Australia							-0.0994	(0.0377)*
	New Zealand							-0.1082	(0.0406)*

Notes: The term “Debt” refers to “External Debt”. Columns 1 to 4 correspond to World Bank figures. In column 5 we show estimates of equation (11) with monthly data from the IMF, from January 1991 to December 2000 (see Appendix B2 for further details), except Peru and Costa Rica (from January 1992 to December 2000) and Brazil (from January 1994 to December 2000). Figures in parenthesis are standard errors of estimated coefficients, * denotes statistical significance at 5 percent and ** at 10 percent.

View Table

Table 4.

Financial Vulnerability in Latin America

		1		2	3	4		5
Country		Debt/Investment		NPV of Debt to Investment Ratio	Private Debt to Investment Ratio	GDP Services (% of GDP)		Elasticity of Risk Premium to Real Exchange Rate
		1998	1999	1998	1999	1998	1999	1999:01	2000:12
Bolivia		3.10	3.92	2.51	0.43	62.4	63.5	0.2497	(0.1704)
Brazil		1.38	1.33	1.31	0.38	62.8	59.9	0.3349	(0.0314)*
Chile		1.64	2.39	1.96	1.35	57.4	57.4	-0.2105	(0.1638)
Colombia		1.72	3.07	1.68	1.09	61.0	62.9	-0.0492	(0.0210)**
Costa Rica		1.40	1.68	1.33	0.55	56.7	56.8	-0.1044	(0.0402)*
Ecuador		3.11	6.57	2.92	3.45	55.2	50.3	-0.0523	(0.0817)
El Salvador		1.74	1.85	1.55	0.19	60.4	60.0	-0.0979	(0.0371)*
Guatemala		1.31	1.44	1.41	0.20	56.6	56.8	-0.0897	(0.0257)*
México		1.58	1.47	1.54	0.95	66.3	66.8	0.1726	(0.0605)*
Nicaragua		8.40	6.63	7.38	0.42	45.4	45.7	0.0110	(0.0048)**
Paraguay		1.17	1.65	1.10	0.06	48.8	46.1	-0.2323	(0.1348)
Perú		2.12	2.62	2.20	0.67	56.1	55.5	-0.1244	(0.0545)**
Uruguay		3.87	4.13	2.08	1.02	65.3	68.0	0.2136	(0.0462)*
Venezuela		1.83	2.33	1.86	1.73	59.6	68.5	0.2517	(0.1248)**
Memo:
	Australia							-0.0994	(0.0377)*
	New Zealand							-0.1082	(0.0406)*

Notes: The term “Debt” refers to “External Debt”. Columns 1 to 4 correspond to World Bank figures. In column 5 we show estimates of equation (11) with monthly data from the IMF, from January 1991 to December 2000 (see Appendix B2 for further details), except Peru and Costa Rica (from January 1992 to December 2000) and Brazil (from January 1994 to December 2000). Figures in parenthesis are standard errors of estimated coefficients, * denotes statistical significance at 5 percent and ** at 10 percent.

An alternative approach is suggested by Calvo and Reinhart (2000) with emphasis on the degree of capital mobility. The approach is motivated by depreciations in emerging economies that have suffered from sudden stops in the access to external funding as in Brazil in 1999. Under imperfect capital mobility, there are real balance restrictions that drive consumers to limit their expenditure in non-tradable goods after a real depreciation (assuming inelastic tradable goods), provoking a negative wealth effect. As in the previous case, if this effect dominates the substitution effect, the depreciation will be contractionary. Moreover, these authors suggest that this dominance is empirically plausible as in emergent economies domestic output has a large services component, which are complementary with respect to tradable goods (both capital and consumption goods), as is shown in the fourth column of Table 4. The wealth effect is reduced under perfect capital mobility as the real balance and external financing restrictions are relaxed.³³

The fifth column of Table 4 shows the estimated using a specification close to equation (11). A first observation is that 7 LA economies (out of 14 analyzed) satisfied the definition of financial vulnerability. This illustrates the importance of modeling the wealth effect of depreciations, at least for this group of countries. On the other hand, the ratio of the NPV of debt to investment (second column), a more adequate measure of the debt to investment ratio at the steady state, has a clear relationship with the financial vulnerability measure of these economies as lower ratios correspond to the more robust economies.

Moreover, excluding Chile and Ecuador, the degree of financial vulnerability could be expressed as a function of the ratio of private debt to investment (third column). This is consistent with Céspedes and others (2000). Finally, combining information in Tables 3 and 4, we plot in Figure 3 the relationship between liability dollarization and financial vulnerability. The conclusion is as follows; once emergent economies surpass a threshold (40 percent) the potential contractionary effects of a depreciation are much higher.

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Dollarization and Financial Vulnerability in Latin America

Citation: IMF Working Papers 2003, 039; 10.5089/9781451845853.001.A001

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The model dynamics replicates several stylized facts related to monetary policy and external variables in emergent economies. First of all, the effect of the exchange rate on aggregate demand through (6) and (3) has a one-period lag. The expectations effect of this variable has even a longer lag as shown in (1). On the monetary policy side, movements in the interest rate generate short run responses in output in the following period. While from (3), the effect on inflation has a two-period lag. This mechanism is consistent with the short run expansionary effect of monetary policy and with the fact that monetary policy has a long control lag.

Finally, in a financially vulnerable economy, the negative wealth effect of a real exchange rate depreciation is observed one period after the substitution effect. An increase in q_t increases q_t+1/t, y_t+1 and φ_t+1, according to (6), (3) and (11), respectively. The lift in the risk premium increases i_t+1, r_t+1 and therefore reduces y_t+2, following (5), (4), and (3). In the medium term, the positive substitution effect is lower than the negative wealth effect if β_q < β_r, which is reasonable and plausible.

IV. Model Parameterization

The model has to be solved numerically as the solution could not be characterized analytically. We calibrate the parameters of the model with estimates from four different countries. In Appendix Table 1 we show the results of those estimations for Australia, New Zealand, Perú, and Uruguay. On the one hand, we used Australia and New Zealand as a benchmark for a financially robust economy. On the other hand, we considered Perú and Uruguay estimates to parameterize the financially vulnerable economy.

The estimated values of the model parameters reflect important differences in the model dynamics of each type of economy. As it is shown in Table 5, robust economies show less inflationary inertia and a higher forward looking component in the inflation rate (α_π is 0.3 in the robust and 0.5 in the vulnerable economy). Also, movements in the real exchange rate are more important in vulnerable economies, even though that all four economies used in this study show similar openness ratios and have been subject to the same external shocks (i.e, Asian crisis) in the sample period. In the robust economy, the impact of the exchange rate on the inflation rate in the Phillips curve is 3.5 percent of the inertia coefficient (α_π) whereas in a vulnerable economy this effect is almost 5 times (17 percent).

Table 5.

Model Dynamics Relationships of the Robust and Vulnerable Economics

Note: Based on the estimates in Appendix Table 1

Table 5.

Model Dynamics Relationships of the Robust and Vulnerable Economics

	Australia	New Zealand	Robust	Peru	Uruguay	Vulnerable
Aggregate Supply
1 − απ	0.73	0.69	0.70	0.55	0.52	0.50
αq/απ×100	4.9	2.4	3.5	19.0	15.7	17.0
αy/απ× 100	30.3	23.1	25.0	13.1	7.5	10.0
αy× βy× 100	6.6	6.0	6.0	2.4	2.0	2.2
Aggregate Demand
βq/βy× 100	3.9	4.1	4.0	7.0	6.9	7.0
βy/βy× 100	11.0	12.2	11.5	87.9	72.8	80.0
βq/βr× 100	39.1	41.6	40.0	82.3	80.7	80.0
βφ/βq× λφq	-0.02	-0.01	0.0	0.64	1.11	1.08
Risk Premium Equation
λφq = ψ3 − ψ2	-0.08	-0.09	-0.09	0.12	0.21	0.17
ψ1/βy*× 100	80.5	17.9	50.0	167.2	133.9	150.0
αq/ψ2 × 100	6.4	3.0	4.5	27.7	21.5	25.0

Note: Based on the estimates in Appendix Table 1

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Table 5.

Model Dynamics Relationships of the Robust and Vulnerable Economics

	Australia	New Zealand	Robust	Peru	Uruguay	Vulnerable
Aggregate Supply
1 − απ	0.73	0.69	0.70	0.55	0.52	0.50
αq/απ×100	4.9	2.4	3.5	19.0	15.7	17.0
αy/απ× 100	30.3	23.1	25.0	13.1	7.5	10.0
αy× βy× 100	6.6	6.0	6.0	2.4	2.0	2.2
Aggregate Demand
βq/βy× 100	3.9	4.1	4.0	7.0	6.9	7.0
βy/βy× 100	11.0	12.2	11.5	87.9	72.8	80.0
βq/βr× 100	39.1	41.6	40.0	82.3	80.7	80.0
βφ/βq× λφq	-0.02	-0.01	0.0	0.64	1.11	1.08
Risk Premium Equation
λφq = ψ3 − ψ2	-0.08	-0.09	-0.09	0.12	0.21	0.17
ψ1/βy*× 100	80.5	17.9	50.0	167.2	133.9	150.0
αq/ψ2 × 100	6.4	3.0	4.5	27.7	21.5	25.0

Note: Based on the estimates in Appendix Table 1

In a similar fashion, the impact of the real exchange rate on the output gap, measured in the aggregate demand equation, are quite different. The ratio β_q/β_y is 4 percent in the robust case and 7 percent in the vulnerable one. The ratio β_q/β_r captures the relative importance of two different channels in the aggregate demand. One is the typical price effect of a real depreciation that increases exports and therefore increases aggregate demand, the other is the impact of monetary policy decisions in the aggregate demand through the typical credit channel. In the robust economy the dominant effect is the latter while in the vulnerable economy the former dominates.

In the same vein, the ratio β_φ/β_q measures the importance of the balance sheet channel. The ratio in the robust economy is 0 percent, whereas in the vulnerable economy the impact of the real exchange rate relative to the impact of the interest rate on aggregate demand is 1.08 percent. Finally, while in a robust economy the impact of external demand shock represents 11.5 percent of the domestic shocks $({\beta }_{y^{*}}/{\beta }_y)$ in the vulnerable economy this figure is 80 percent.

The ratio ${\psi }_1/{\beta }_{y^{*}}$ could be thought as the basis points decreased (increased) in the risk premium vis-à-vis an increase (decrease) in output given a positive (negative) shock in the external demand. In a robust economy this ratio is 50 percent while in a vulnerable economy is 150 percent. Finally, the ratio α_q/ψ₂ reflect the increase in inflation vs. the increase in the risk premium in the event of a real depreciation. While for the robust economy the ratio is 4.5 percent, a vulnerable economy has a ratio of 25 percent.

Based on these ratios and relationships the parameter values used for the simulations are reported in Table 6. It is important to emphasize that the differences between a robust and a vulnerable economy lie on the differences in the risk premium equation and in the importance of domestic vis-à-vis external shocks. In order to compare the simulated variances, we set all shock variances to 0.5, excepting the Phillips curve and aggregate demand that are set to 1.0. Finally, the share of imported goods in the CPI inflation is set to w = 0.3.

Table 6.

Baseline Parameters for Robust and Vulnerable Economies

Table 6.

Baseline Parameters for Robust and Vulnerable Economies

Aggregate Supply and Aggregate Demand
(1), (5) and (3)
	Robust	Vulnerable		Robust	Vulnerable
α π	0.300	0.500	β r	0.032	0.031
α y	0.075	0.050	β*y	0.092	0.352
α q	0.011	0.085	β q	0.013	0.025
β y	0.800	0.440	β φ	0.000	0.148
Risk Premium Equation and External Variables
(16), (11) (12) and (13)
	Robust	Vulnerable			Both
ψ 1	0.046	0.528		γ*π	0.95
ψ 2	0.233	0.340		γ *y	0.90
ψ 3	0.148	0.509		f*π	0.76
λ φq	−0.086	0.169		f*y	0.43

View Table

Table 6.

Baseline Parameters for Robust and Vulnerable Economies

Aggregate Supply and Aggregate Demand
(1), (5) and (3)
	Robust	Vulnerable		Robust	Vulnerable
α π	0.300	0.500	β r	0.032	0.031
α y	0.075	0.050	β*y	0.092	0.352
α q	0.011	0.085	β q	0.013	0.025
β y	0.800	0.440	β φ	0.000	0.148
Risk Premium Equation and External Variables
(16), (11) (12) and (13)
	Robust	Vulnerable			Both
ψ 1	0.046	0.528		γ*π	0.95
ψ 2	0.233	0.340		γ *y	0.90
ψ 3	0.148	0.509		f*π	0.76
λ φq	−0.086	0.169		f*y	0.43

V. Model Solution and Simulations

In this section we present three exercises in order to answer the question of which is the best way to conduct an IT regime conditional on the economy type. First of all, we compute the optimal policy rule without restrictions on the set of policy indicators. Then, we restrict our attention to fixed rules in which a much narrow set of indicators is used to guide monetary policy. Instead of calibrate the parameters associated with those rules we compute optimized coefficients for each rule. Finally, we study the optimality of a non linear policy rule for the financially vulnerable economy case.

A. The Optimal Rule

Given a baseline assumption that χ = 0.5, the optimal policy rule for each case is shown in Table 7. As the central bank cares about the CPI inflation, the reaction function includes almost all variables in the system as the real exchange rate is a forward looking variable.³⁹

Table 7.

Optimal Rule for Robust and Vulnerable Economies

Table 7.

Optimal Rule for Robust and Vulnerable Economies

Economy	π t	y t	${\pi }_t^{*}$	$y_t^{*}$	$i_l^{*}$
Robust	1.508	0.643	−0.447	0.086	0.440
Vulnerable	1.369	0.067	−0.587	0.432	0.524
Economy	φ t	q t−1	π t+1/t	q t/t−1	y t/t−1
Robust	0.590	−0.422	0.244	−0.065	0.022
Vulnerable	1.197	−0.411	0.071	−0.291	0.343

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Table 7.

Optimal Rule for Robust and Vulnerable Economies

Economy	π t	y t	${\pi }_t^{*}$	$y_t^{*}$	$i_l^{*}$
Robust	1.508	0.643	−0.447	0.086	0.440
Vulnerable	1.369	0.067	−0.587	0.432	0.524
Economy	φ t	q t−1	π t+1/t	q t/t−1	y t/t−1
Robust	0.590	−0.422	0.244	−0.065	0.022
Vulnerable	1.197	−0.411	0.071	−0.291	0.343

Table 8.

Unconditional Standard Deviations Under the Optimal Rule

Table 8.

Unconditional Standard Deviations Under the Optimal Rule

Economy	${\pi }_t^c$	π t	y t	i t	r t	q t	E[Lt]
Robust	1.868	1.440	1.930	2.331	3.971	3.768	3.936
Vulnerable	3.265	2.044	2.189	3.216	5.410	8.888	6.574

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Table 8.

Unconditional Standard Deviations Under the Optimal Rule

Economy	${\pi }_t^c$	π t	y t	i t	r t	q t	E[Lt]
Robust	1.868	1.440	1.930	2.331	3.971	3.768	3.936
Vulnerable	3.265	2.044	2.189	3.216	5.410	8.888	6.574

The coefficient on expected inflation is larger in the robust economy as we expect because of less inflationary inertia in the robust economy vis-à-vis the vulnerable economy. One of the most important variables in the vulnerable economy is the risk premium. This is completely as expected as fluctuations in the risk premium transmit that volatility to the output gap, the real exchange rate and inflation. In general, the vulnerable economy shows higher coefficients in all external variables. One striking result is the fact that the output gap plays almost no role in the reaction function in the vulnerable economy case.

The variances of the most relevant variables under an optimal rule are:

As expected the robust economy is significantly less volatile than the vulnerable one. There is more structural volatility in the robust economy. In particular, the major source of volatility in the vulnerable economy comes from real exchange rate fluctuations. We simulate in Figure 4 the variance frontiers for both economies with different preferences in the loss function. A low value of χ represents a “hawkish” central, while a value of χ = 1 represents a “dove” central bank that is more concerned about the output gap instead of the inflation rate. A first result is the variance dominance of the robust economy compared to a financially vulnerable economy.

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Variance Frontiers for Robust and Vulnerable Economies Under the Optimal Rule

Citation: IMF Working Papers 2003, 039; 10.5089/9781451845853.001.A001

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As shown in Figure 4 a central bank too hawkish may exacerbate the volatilities of the real exchange rate and the real interest rate. The reason behind might be that the central bank has to behave in a much more discretionary form if chooses to be too hawkish. In particular, it will be willing to intervene in the money market and in the exchange rate market to stop any deviation in the inflation target. This evidence suggests that it might be optimal to have a flexible IT regime (as the baseline assumption) instead of a strict one.

B. Alternative Rules

As our goal is try to shed some light in which type of rules might be optimal for economies in which balance sheet effects matter we simulate the six types of rules presented in the above section for both classes of economy. The results (optimized coefficients) are presented in Table 9 and in Table 10 we present the variances associated with those optimized rules.

Table 9.

Simple Fixed Rules for Robust and Vulnerable Economies

Table 9.

Simple Fixed Rules for Robust and Vulnerable Economies

Robust Economy		π t	π t+1/t	y t	q t−1	q t
Rule 1	fπ1 = 0, −fq1 = fq = fπw	1.217	-	0.208	−0.365	0.365
Rule 2	fπ1 = 0, −fq1 = fq	1.152	-	0.259	−0.228	0.228
Rule 3	fπ1 = 0	0.836	-	0.380	−0.202	0.549
Rule 4	fπ1 = 1, −fq1 = fq	1.878	1.000	0.830	−0.536	0.536
Rule 5	fπ1 = 1	1.106	1.000	0.396	−0.236	0.507
Rule 6	Unrestricted parameters	0.741	0.783	0.440	−0.338	0.403
Vulnerable Economy		π t	π t+1/t	y t	q t−1	q t
Rule 1	fπ1 = 0, −fq1 = fq = fπw	0.787	-	0.056	−0.236	0.236
Rule 2	fπ1 = 0, −fq1 = fq	0.761	-	0.066	−0.190	0.190
Rule 3	fπ1 = 0	1.139	-	0.173	−0.099	0.610
Rule 4	fπ1 = 1, −fq1 = fq	0.723	1.000	0.052	−0.280	0.280
Rule 5	fπ1 = 1	1.142	1.000	0.159	−0.198	0.650
Rule 6	Unrestricted parameters	1.168	0.526	0.104	−0.050	0.613

View Table

Table 9.

Simple Fixed Rules for Robust and Vulnerable Economies

Robust Economy		π t	π t+1/t	y t	q t−1	q t
Rule 1	fπ1 = 0, −fq1 = fq = fπw	1.217	-	0.208	−0.365	0.365
Rule 2	fπ1 = 0, −fq1 = fq	1.152	-	0.259	−0.228	0.228
Rule 3	fπ1 = 0	0.836	-	0.380	−0.202	0.549
Rule 4	fπ1 = 1, −fq1 = fq	1.878	1.000	0.830	−0.536	0.536
Rule 5	fπ1 = 1	1.106	1.000	0.396	−0.236	0.507
Rule 6	Unrestricted parameters	0.741	0.783	0.440	−0.338	0.403
Vulnerable Economy		π t	π t+1/t	y t	q t−1	q t
Rule 1	fπ1 = 0, −fq1 = fq = fπw	0.787	-	0.056	−0.236	0.236
Rule 2	fπ1 = 0, −fq1 = fq	0.761	-	0.066	−0.190	0.190
Rule 3	fπ1 = 0	1.139	-	0.173	−0.099	0.610
Rule 4	fπ1 = 1, −fq1 = fq	0.723	1.000	0.052	−0.280	0.280
Rule 5	fπ1 = 1	1.142	1.000	0.159	−0.198	0.650
Rule 6	Unrestricted parameters	1.168	0.526	0.104	−0.050	0.613

Table 10.

Unconditional Standard Deviations Under Alternative Fixed Rules

Table 10.

Unconditional Standard Deviations Under Alternative Fixed Rules

Robust Economy	${\pi }_t^c$	π t	y t	i t	r t	q t	E[Lt]
Rule 1	1.874	1.989	2.177	5.331	6.081	4.876	4.362
Rule 2	1.826	1.938	2.173	5.403	5.956	5.060	4.308
Rule 3	1.109	1.486	2.190	5.750	6.287	2.619	3.467
Rule 4	1.517	1.468	2.183	6.048	6.564	0.771	4.172
Rule 5	1.113	1.487	2.190	5.738	6.275	2.638	3.464
Rule 6	1.122	1.482	2.189	5.712	6.245	2.653	3.459
Optimal	1.951	1.742	1.808	3.215	4.448	3.003	3.237
Vulnerable Economy	${\pi }_t^c$	π t	y t	i t	r t	q t	E[Lt]
Rule 1	2.610	2.029	2.984	3.702	4.069	4.871	6.317
Rule 2	2.644	2.066	2.810	3.645	4.166	4.778	6.134
Rule 3	2.128	2.340	2.822	3.180	3.727	2.128	4.761
Rule 4	2.078	2.711	2.785	3.163	3.848	3.658	4.598
Rule 5	2.113	2.340	2.895	3.200	3.735	2.168	4.840
Rule 6	2.086	2.340	2.484	3.171	3.723	2.097	4.221
Optimal	2.177	2.315	2.211	3.018	3.331	4.192	4.047

View Table

Table 10.

Unconditional Standard Deviations Under Alternative Fixed Rules

Robust Economy	${\pi }_t^c$	π t	y t	i t	r t	q t	E[Lt]
Rule 1	1.874	1.989	2.177	5.331	6.081	4.876	4.362
Rule 2	1.826	1.938	2.173	5.403	5.956	5.060	4.308
Rule 3	1.109	1.486	2.190	5.750	6.287	2.619	3.467
Rule 4	1.517	1.468	2.183	6.048	6.564	0.771	4.172
Rule 5	1.113	1.487	2.190	5.738	6.275	2.638	3.464
Rule 6	1.122	1.482	2.189	5.712	6.245	2.653	3.459
Optimal	1.951	1.742	1.808	3.215	4.448	3.003	3.237
Vulnerable Economy	${\pi }_t^c$	π t	y t	i t	r t	q t	E[Lt]
Rule 1	2.610	2.029	2.984	3.702	4.069	4.871	6.317
Rule 2	2.644	2.066	2.810	3.645	4.166	4.778	6.134
Rule 3	2.128	2.340	2.822	3.180	3.727	2.128	4.761
Rule 4	2.078	2.711	2.785	3.163	3.848	3.658	4.598
Rule 5	2.113	2.340	2.895	3.200	3.735	2.168	4.840
Rule 6	2.086	2.340	2.484	3.171	3.723	2.097	4.221
Optimal	2.177	2.315	2.211	3.018	3.331	4.192	4.047

We can draw several conclusions out of this simulation exercise. First of all, in both economies Ball-type rules are superior. However, this is much clearer in the vulnerable economy where the difference of including the real exchange rate depreciation is significant. Another expected and clear result is that simpler rules come with a price in terms of higher volatility. Again the difference is much stronger in the vulnerable economy. This might support the hypothesis that in vulnerable economies make sense to look a wider set of indicators to design the monetary policy, with special attention in the exchange rate.