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Macroeconomic Volatilityduring Argentina#s ImportSubstitution StageP. Ruben MercadoPublished online: 21 Jul 2010.

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ISSN 0269-2171 print/ISSN 1465-3486 online/01/020151-11 � 2001 Taylor & Francis LtdDOI: 10.1080/02692170110037650

International Review of Applied Economics, Vol. 15, No. 2, 2001

P. Ruben Mercado, Department of Economics, Bryn Mawr College, 101 N. Merion Avenue, BrynMawr, Pennsylvania 19010-2899, USA. E-mail: pmercado@brynmawr.edu

Macroeconomic Volatility during Argentina’s Import

Substitution Stage

P. RUBEN MERCADO

ABSTRACT In this article, I analyze the characteristics and sources of macroeconomicvolatility for the case of Argentina during the import substitution industrialization (ISI).This case is particularly relevant since Argentina has been one of the most extreme examplesof macroeconomic volatility in the Latin American region. I estimated a small vector autoregression (VAR) to approximate the main dynamic features of the Argentine economyduring the ISI: the balance of trade improving but the `contractionary’ effect ofdevaluation, and the short-run persistence of `political-economic’ switching regimes.Counterfactual experimentation showed the impossibility of reaching a complete economicstabilization during the historical period under analysis and the existence of sharp tradeoffsamong the internal balance, the external balance and policy volatility. It also indicated thatat least a half of the observed policy volatility could be attributed to exogenous shocks. Theseresults suggest that the performance of the Argentine economy during the ISI could havebeen better under more rational policy management but, surprisingly, that the improvementwould not be as large as one would expect.

1. Introduction

Argentina’s import substitution industrialization was an ̀ inward looking’ strategy ofdevelopment that focused on the promotion of local industries oriented towards thedomestic market. This developmental strategy was introduced after the world crisisof 1929, when the country experienced a sharp restriction in its access to foreignmarkets of goods and capital. It lasted until the end of the 1980s, when an ambitiouspolicy of economic openness, deregulation and privatization marked the end of thatexperience.

Until the mid± 1970s, the import substitution industrialization (ISI) strategydelivered moderate growth. However, throughout its period of existence it alsogenerated very unstable short-term macroeconomic conditions. As in other LatinAmerican countries, the source of the observed volatility has been attributed eitherto exogenous shocks Ð following the tradition of the Economic Commission forLatin America and the Caribbean (ECLAC)1 Ð or to an inconsistent macro-economic management Ð in a `macroeconomics of populism’ fashion.2 However,most of these explanations have relied on qualitative and historical analyses.

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152 P. Ruben Mercado

Econometric studies have begun to be carried out only recently. For example, IDB(1995) and Haussman & Gavin (1996) apply static cross-country regressions toidentify the main sources of volatility in Latin America.

The problem with standard regression approaches is the well-known issue ofendogeneity. For example, a regression of GDP on the money supply and on theterms of trade may be used to explain the sources of observed GDP volatility asstemming from money supply volatility or from terms of trade volatility. However,a good deal of money supply volatility could just be an outcome of stabilizationpolicy responses to terms of trade volatility.

As a way of overcoming that problem, in this article I use dynamic econometrictechniques and stochastic counterfactual experimentation to analyze the character-istics and sources of macroeconomic volatility for the case of Argentina during theISI. This is a particularly relevant case since Argentina has been one of the mostextreme examples of macroeconomic volatility in Latin America.

Section 2 presents an analysis of the dynamic interactions among policy tools,internal and external balance using a Vector Auto Regression (VAR) and an two-state Switching Regime Model. Section 3 analyzes the main characteristics andsources of the observed macroeconomic volatility using stochastic counterfactualexperimentation. Finally, Section 4 presents the conclusions.

2. Policy Tools, Internal and External Balance

Argentina’s production structure during the ISI mainly comprised two sectors: anexport-oriented agricultural sector and an industrial sector producing for thedomestic market.3 While foreign currency prices of exports and imports weredetermined in the international market, the price of domestic goods was determinedby a markup on labor costs and imported inputs costs. Short run substitutionresponses to price changes were very small for both exports and imports. Thenominal exchange rate and nominal wage were the two main policy tools to achieveinternal and external balance.4

An increase in nominal wages had expansive effects on GDP, since they werethe main component of aggregate demand. At the same time, the wage increase hadnegative effects on the balance of trade due to both an increase in the imports ofintermediate inputs and a decrease in the exportable surplus.

An exchange rate devaluation had the opposite effect of improving the balanceof trade position due to both a fall in the importation of intermediate goods and anincrease in the exportable surplus. However, the devaluation had contractionaryeffects on GDP due to its re-distributive impacts. Indeed, the export goodsproduced by the agricultural sector constituted, at the same time, the mainconsumption good of Argentina’s labor force. Thus, its domestic demand was veryinelastic. The devaluation of the exchange rate implied an increase of the domesticprice of agricultural products and, in time, a decrease in the real wage and thus inaggregate demand and GDP.5

It should now be clear how the nominal exchange rate and nominal wagetended to work in opposite directions. Therefore, it can be said that the relative valueof these two variables was the main policy tool to affect the short-term level ofactivity and the trade balance (Canitrot, 1981). To capture the dynamic interactionsamong policy tools, internal balance and external balance during Argentina’s ISI, Iestimated a Vector Auto Regression using annual data for the period 1930 ± 88, withthe following variables:6

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Macroeconomic volatility during Argentina’s import substitution stage 153

ew = ln 1 e

w 2 ; xm = ln 1 x

m 2 ; gdp = ln(GDP)

where:

e = Official nominal exchange rate7

w = Nominal wagex = Real exportsm = Real imports

GDP = Real gross domestic product.8

Following Perron (1989) the series were detrended with segmented trends, sincethey showed the presence of s̀purious unit roots’ due to specific structural breaks.9

The non-existence of unit roots and, therefore, of co-integrating relationshipsamong variables, indicates that detrending will not imply a significant risk ofdiscarding useful information on long-run co-movements in the data. Moreover, theanalytical focus on short-run stabilization problemsÐ that is, on volatility aroundtrend values Ð also points toward the estimation of a stationary VAR.

The VAR’s number of lags was determined by applying likelihood ratio tests. Thelikelihood test statistics and critical values at 5% of significance corresponding to fourversus three, two, one and zero lags were, respectively: 10.54 < ( x 2

(9) = 16.92);17.79 < ( x 2

(18) = 28.27); 25.72 < ( x 2(27) » 36); and (88.63 > ( x 2

(32) » 43). Thus, allbut the last restriction were accepted, yielding a simple one-lag specification for theVAR. This was not surprising, given the annual frequency of the data.

During the period of interest (1930 ± 88) standard deviations for gdp and xmreached, as a percentage of the mean, 4.36% and 24.3% respectively, somethingvery unusual for developed countries but not so rare for the developing world.Nevertheless, what is very striking is the variability of ew, the basic `policy tool’ forthe Argentine economy. The standard deviation of ew reached 38% as a percentageof the mean and showed sharp changes in short periods. This is a first indication ofeither a very volatile policy environment or of serious difficulties in conducting asmooth stabilization policy during the historical period of interest.

The VAR was estimated with its variables in this order: (ew, xm, gdp) andshocked with Cholesky factored shocks to analyze its impulse response behavior.Having ew as the first variable implies the implicit assumption that policy shockscontemporaneously affect xm and gdp. That is, that the economy starts respondingto policy shocks within a year, something that seems a reasonable proposition. Onthe other hand, it also implies that the policy variable ew responds with a lag toshocks in xm or in gdp. In other words, it means that the autonomous part of thepolicy process takes at least one year to respond to disequilibria in the external orinternal balance due to the existence of recognition and implementation lags,something that also seems quite plausible. The order of the two other remainingvariables (xm, gdp) is not relevant, since the impulse response behavior of theestimated VAR was robust with respect to their order.

The impulse responses of the VAR to Cholesky factored shocks of one standarddeviation each as a percentage of the mean are shown in Fig. 1. Thin lines representxm responses, dotted lines gdp responses and bold lines ew responses. Shocks’ sizesare: 28% for ew, 22% for xm and 3.1% for gdp.

Shocks in xm are short lived in their effects on gdp and xm itself, and they affectew negatively. Shocks in gdp have a negative impact on xm. That is, an economic

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154 P. Ruben Mercado

expansion tends to deteriorate the balance of trade. However, the size of this lasteffect is not as large as the one we would expect for an economy like that ofArgentina during the ISI stage, with inelastic exports and heavy dependence onimports of intermediate inputs.

Shocks in ew can be interpreted, for example, as a devaluation of the nominalexchange rate that is higher than a given increase in nominal wage. As expected, apositive shock in ew improves the balance of trade (xm) by more than 5% as apercentage of the mean. This effect gradually decays over the course of five years.The dynamic response of gdp shows the `contractionary effect of devaluation’ : gdpfalls 1.5% as a percentage of the mean, but this effect is short lived and is followedby an expansion in the medium term.

A very interesting pattern appears in the response of ew to its own shocks.Indeed, shocks to ew are almost completely ̀ eroded’ after six years, with the bulk ofthe erosion (around 85% of the initial shock) occurring during the first four years.If nominal wages (w), instead of being a policy variable, were indexed to the pricelevel, this erosion could be explained as arising from a price level increase inducedby a nominal exchange rate (e) devaluation which, in time, results in a nominal wageadjustment. That is, the price level and the indexation mechanism would provide anautomatic link between e and w. However, and despite the existence of wageindexation mechanisms during some periods of the Argentine ISI, nominal wageadjustments were still strongly determined by the Government. Therefore, theerosion of shocks in ew has to be rationalized as coming from an endogenouscomponent, institutionally built-in, in the process of policy formation.

From this perspective, the Government would be forced to act in response totwo kinds of signals, stemming from the external and the internal balancerespectively. A negative shock to ew, if highly persistent, would lead to anunsustainable balance of trade deficit, ultimately inducing a corrective devaluation.On the other hand, a positive and highly persistent ew shock would affect the

Fig. 1. Impulse Response Functions

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Macroeconomic volatility during Argentina’s import substitution stage 155

internal balance, bringing about a permanent drop of gdp below trend levels.Pressures coming from powerful unions and industrialists producing for thedomestic market would force the Government to increase nominal wages to re-activate the economy. It should be clear, then, that the Government, within theeconomic, social and institutional framework characteristic of the Argentine ISIstrategy, could manipulate the ew ratio in the short-run, but not in the long-run.

Alternatively, this kind of ̀ political-economic’ mechanism can be modeled by aSwitching-Regime Model of the endogenous part of the process of policy formation.In so doing, I characterized a `wage-led’ regime as the one in which nominal wagesgrow faster than the nominal exchange rate, so that the rate of growth of (e/w) isnegative. This regime would then be prone to generate external imbalances. Idefined an `exchange rate-led’ regime as one with positive rates of growth of (e/w),thus prone to generate internal imbalances.10 I estimated a Switching-Regime AutoRegressive model in which each regime is the outcome of an unobserved two-stateMarkov chain. The estimated model was:11

dewt ± m s t= ^

n

i = 1f t (dewt± i ± m s t± i

) + « t (1)

where:

dew: rate of growth of (e/w)12

m : average growth rate within the regimef : autoregressive coefficientn: number of lagged dew (equal to 5)s: state characterizing a regime

s = 1 if m > 0, `exchange rate-led regime’s = 2 if m < 0, `wage-led regime’

« : disturbancepi j: Markov transition probabilities (i, j = 1,2)

The estimation results are reported in Table 1.The average rate of growth of (e/w) is 19.04% for the `exchange rate-led’

regime and ± 10.01% for the `wage-led’ regime. The average persistence of regime`i’ is given by:

Avreg(i) =1

1 ± pii

(2)

The estimated probability of remaining in the `exchange rate-led’ regime (p11 ) isequal to 0.67, while the estimated probability of staying in the `wage-led’ regime(p22) is 0.76. This yields that `exchange rate-led’ and `wage-led’ regimes persist forapproximately three and four years respectively.

Table 1. Switching-regime model parameter estimates (standard errors in parentheses)

m 1 = 19.04 m 2 = ± 10.01 P11 = 0.67 P22 = 0.76 s 1 = 41 s 2 = 7(2.66) (0.89) (0.12) (0.09) (13) (2)

f 1 = ± 0.18 f 2 = ± 0.35 f 3 = ± 0.50 f 4 = ± 0.31 f 5 = ± 0.25(0.12) (0.08) (0.06) (0.10) (0.07)

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156 P. Ruben Mercado

The degree of regime persistence matches what was found before in theimpulse-response analysis of the estimated VAR.13 Moreover, the VAR also capturesthe expected effects of shocks in ew, that is, the balance of trade improving butcontractionary effect of devaluation, and the expansive but trade worsening effectsof wage increases. However, the size of the ew shock (28%) required to generate theobserved responses, while within the range of standard historical values, is anindication of the low power of the only available policy tool. The implications of thiswill be fully appreciated in the context of the counterfactual experiments presentedin the next section.

3. Counterfactual Stochastic Policy Analysis

To analyze the relationships among volatility in the internal and external balanceand in the policy tool, I performed a series of counterfactual stochastic experiments.I compared the actual behavior of the historical series for xm, gdp and ew against thesimulated performance of a single planner. This planner was supposed to set theoptimal path of the policy shocks to minimize the volatility of ew, xm and gdpsubject, as a constraint, to the estimated VAR, and facing exogenous shocks.14 Informal terms, the planner’s problem can be set as one of finding the controls(ut )

Tt = 0 to minimize a quadratic criterion function J of the form:

J = E 5 ^T

t = 0 1 1

2x 9t W xt +

1

2u 9t L ut 2 6 (3)

subject to the state-space representation of the estimated VAR, where E is theexpectation operator; W and L are weighting matrices; x9 = (ew, xm, gdp) is thevector of VAR’s variables; and (ut = Sew) are shocks to the policy variable ew.

To implement the simulations, I applied a Certainty Equivalence tech-nique.15 This technique combines the above procedure for choosing the optimalvalue of Sew with a Monte Carlo procedure to generate the exogenous dis-turbances. The simulations were performed for the period 1931± 88, thus coveringthe ISI stage. The objective of these simulations was to minimize the deviations ofew, xm and gdp from their trend.16 Since the model was estimated with detrendedvalues, this was equivalent to minimizing deviations from zero. For each MonteCarlo run, a random drawing of the exogenous disturbances was made fromnormal distributions with mean zero and variances equal to those of thecorresponding orthogonal shocks obtained from the estimated VAR’s residualcovariance matrix. Then, given the values of these additive shocks, the optimalvalue of Sew was computed.17

Experiments’ results are reported in Fig. 2. The vertical axis measures, as apercentage of the mean, the standard deviation from target for xm (the balance oftrade) while the horizontal does so for gdp. Each of the diamonds or dots denotes anexperiment result Ð the average of 50 Monte Carlo runs Ð for a given set of weights,while the big square represents the historical performance. On top of each of them,there is a number that accounts for the corresponding standard deviation of ew, alsoas a percentage of the mean.

The results from a first set of experiments, corresponding to a very lowweight Ð equal to 0.01 Ð on ew, are represented by diamonds. They describe adownward sloping curve, from left to right, which corresponds to changes in relativeweights between xm and gdp. The experiments begin with high weights for xm and

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Macroeconomic volatility during Argentina’s import substitution stage 157

low weights for gdp, equal to 9.99 and 0.01 respectively, and end with high weightsfor gdp and low weights for xm.

The implied tradeoff between xm volatility and gdp volatility is readily apparent.It is also clear that the frontier is located far from the origin (0,0). Given that theweights on the policy variable ew were set close to zero in all these experiments, thisindicates the impossibility of reaching a complete economic stabilization during thehistorical period under analysis.

As I said before, the numbers on top of the curve indicate, as a percentage ofthe mean, the standard deviations from the target of the policy instrument ew. Allthe `optimal’ ew standard deviations are larger than that of the actual performance,equal to 38%. This indicates that the better performance of the counterfactualexercise, which poses the optimal frontier curve below the actual performancesquare, is due to its poorer performance in terms of ew volatility.

The results from a second set of experiments are also reported in Fig. 2. Theycorrespond to a higher weight on ew, 0.1 instead of the previous 0.01, and arerepresented by dots. As expected, the increased weight on ew shifts the optimalfrontier towards the actual performance. The standard deviations of ew are still highalthough, as expected, they are lower than in the previous set of experiments.

The comparison between the optimal frontiers and the actual performance ofthe economy in the previous sets of experiments yields some interesting insights.The actual performance is above the two optimal policy frontiers, indicating that thehistorical performance could have been better. However, contrary to what onewould expect in this kind of counterfactual exercise, the distance between actualand optimal performance is not too great. Of course, the first set of experiments’optimal frontier lies far away from the actual performance square, but this is due totremendous and unfeasible variations in ew. In short, it can be said that there were

Fig. 2. Policy Frontiers

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sharp tradeoffs among the internal balance (gdp volatility), the external balance (xmvolatility), and policy volatility.

To obtain a measure of the proportion of macroeconomic volatility generatedby exogenous shocks and by policy mismanagement, I performed anotherexperiment. In this experiment Ð only one simulation Ð the planner faced thesequence of historical exogenous shocks to xm and gdp instead of those generatedduring Monte Carlo runs. That is, for this simulation I used the orthogonal shocksto xm and gdp obtained from the sequence of estimated VAR’s residuals. The goal ofthe experiment was to determine if similar performances to the actual ones for xmand gdp could have been obtained at a l̀ower cost’ in terms of ew volatility.

The results show that, for similar performances in terms of xm (a standarddeviation, as a percentage of the mean, of 24.4% in the experiment compared withan actual one of 24.3%) and in terms of gdp (4.5% in the experiment relative to anactual one of 4.36%), the standard deviation of ew in the experiment was equal to19.9%, while the actual one was 38%. These results were obtained after a gridsearch over the space of possible weights on xm, gdp and ew to best approximate theactual volatility in terms of xm and gdp. The corresponding weights used to generatethe reported results were: five on xm, 30 on gdp and one on ew.

The `cost reduction’ in terms of policy volatility Ð for performances similar tothe actual ones in terms of xm and gdp Ð can be measured as:

STD optimal ew ± STD actual ew

STD actual ew=

19.9% ± 38%

38%= ± 0.48. (4)

The result suggests that almost one half of the observed volatility in ew could beattributed to policy mismanagement, while the remaining proportion is due tounavoidable responses to exogenous shocks to the economy.

The results of the counterfactual experiments are quite surprising. Indeed, theyindicate that the performance of the Argentine economy could have been better, butthat the improvement would not be as large as one should expect under the strongassumption of an optimizing planner in charge of policy-making. The results alsocontrast against the relatively recent emphasis on policy mismanagement as themain source of volatility in Latin American countries before the 1990s.18

Finally, it is well known that policy analysis with a reduced form representationof the data has been criticized by Lucas (1976) and later defended by Sims (1982and 1986). In connection with this issue, there are three elements that may reduceits scope for the case under analysis.

First, the counterfactual simulations were performed without altering theestimated policy reaction function, that is, the VAR equation for ew. Only thediscretionary shocks Sew were supposed to be determined by the optimizingplanner. Of course, even by only manipulating Sew the planner was, in fact,r̀eacting’ to changes in the target variables, thus affecting the `overall’ reactionfunction. However, at least a part of that `overall’ function was kept constantthroughout the simulations.

Secondly, the behavior of the optimal Sew Ð the orthogonal policy shocks toew Ð corresponding to the last experiment did not appear to be systematicallydifferent from its historical one. Moreover, Ljung± Box statistics corresponding tothe autocorrelation functions of the actual shocks, and of the optimal ones up toten lags, were not significantly different from zero. That is, policy innovationswere not different from white noise in both cases. In this sense, the counter-

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Macroeconomic volatility during Argentina’s import substitution stage 159

factual exercise does not seem to have implied a predictable change in the pre-existing policy behavior that could have been `figured out’ by forward lookingagents.

Finally, there is some evidence indicating that the policy variable ew couldbe considered as s̀uper-exogenous’ , thus considerably reducing the scope of theLucas critique (Ericsson, 1992; Favero & Hendry, 1992). To test for super-exogeneity, I carried out sequential re-estimations and Chow tests of the policyreaction function Ð that is, the VAR equation for ew Ð with annual breakingpoints over the sample period of interest (1930 ± 88). There were two significantstructural breaks: one in 1956 and the other in 1975, with p-values for thecorresponding Chow statistic equal to 0.048 and 0.029 respectively. Super-exogeneity will hold (Engle & Hendry, 1993) if the dummy variables equivalentto the structural breaks in the `marginal’ model (the policy reaction function)become not significant when added to the `conditional’ model (the equations forxm and gdp in the VAR).

After trying different specifications, the 1956 structural break in the policyreaction function was captured by a dummy variable representation of a changein the slope of ewt ± 1 . The coefficient of the corresponding interaction term (dum.ewt ± 1 ) was significantly different from zero, with a t-statistic equal to ± 2.85.19 Ithen re-estimated the `conditional model’ (the VAR equations for xm and gdp)augmented by the corresponding interaction terms, and I tested the jointrestriction that the coefficients of those interaction terms were not significantlydifferent from zero. The log-likelihood test statistic was equal to 0.15, smallerthat the corresponding critical value at 5% of significance ( x 2

(2) = 5.99). Thus,the restrictions were accepted, suggesting that the break in the `marginal’ modeldid not affect the parameters of the `conditional’ model. That is, indicating thatew was super-exogenous.

4. Conclusions

Counterfactual experimentation with a small VAR showed the impossibility ofreaching a complete economic stabilization, and the existence of sharp tradeoffsamong the internal balance, the external balance and policy volatility duringArgentina’s ISI. It also showed that at least a half of the observed policy volatilitycould be attributed to exogenous shocks. These results indicate that the perform-ance of the Argentine economy during the ISI could have been better under morerational policy management but, surprisingly, that the improvement would not be aslarge as one would expect. These results also contrast against the relatively recentemphasis on policy mismanagement as the main source of volatility in LatinAmerican countries before the 1990s. Of course, macroeconomic mismanagementmade the actual performance of the Argentine economy worse than what it couldhave been. However, having implemented a more consistent macroeconomic policywould not have eliminated all the volatility problems.

There are two factors that could explain the aforementioned lack ofperformance’s improvement. First, the magnitude and volatility of the shocksexperienced by the Argentine economyÐ shocks stemming from the variation in itsterms of trade, recurrent foreign credit rationing, and capital flows reversals Ð andsecond, the lack of enough policy tools. These factors considerably reduced thechances of successful stabilization, pointing towards the necessity of developingmechanisms to deal with the direct impacts of shocks Ð such as diversification or

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buffering schemesÐ and to a wider policy tools box Ð such as the implementationof appropriate institutional arrangements to conduct more flexible fiscal andmonetary policies. It can then be argued that Argentina’s macroeconomic volatilitywas strongly linked to long-run aspects of its development process.20

Notes

I thank David Kendrick for his valuable suggestions, and Heather Anderson, Stephen Magee, WilliamGlade, David Drukker and the participants at the Bryn Mawr-Haverford-Swarthmore Seminar for theircomments.

1. For a brief introduction to the ECLAC approach, see Birdsall & Lozada (1996).2. See, for example, Dornbusch & Edwards (1991).3. See Canitrot (1981).4. The nominal exchange rate was determined, directly or indirectly, by the Government. The average

nominal wage was either determined through centralized bargaining between workers andemployers’ unions under Government surveillance, or directly imposed by the Government.Monetary and fiscal policies were secondary tools for macroeconomic management.

5. For a general analysis of this type of c̀ontractionary effect of devaluation’, see Krugman & Taylor(1978).

6. The only available data covering this period is annual data. Data sources are: for Real GDP, Exportsand Imports, Argentina’s Central Bank as reported in Domenech (1986) and Lechuga (1993); forNominal Wages, Domenech (1986) and INDEC (1983); for the Official Nominal Exchange Rate,Organizacion Techint (1975, 1976, 1986 and 1990).

7. The Official Nominal Exchange Rate is defined as units of Argentine currency per one US dollar.Thus, a devaluation, given the Nominal Wage, implies an upward movement in ew.

8. All variables are indexes with base: 1930 = 100.9. The gdp series is a case of c̀hanging growth’ (in Perron’s terminology) with a break in 1975, while

ew and xm are cases of `crash’ (also in Perron’s terminology), with breaks in 1955 and 1946respectively.

10. For the Argentine case, there is a certain correspondence between ̀ wage-led’ regimes and `populist’governments, and between `exchange rate-led’ regimes and `authoritarian’ governments.

11. The estimation was carried-out using a filter developed by Hamilton. See Hamilton (1989; 1990;1994).

12. Since ew was defined as ew = ln(e/w), the rate of growth of (e/w) is approximated by

dewt = ewt ± ewtt ± 1 .

Since ew was detrended, dewt is the rate of growth above or below trend growth13. A Vector Switching-Regime Model could perhaps be used as an alternative to the VAR. However,

hypothesis testing to determine which model better characterizes the data poses seriousmethodological problems (see Hamilton, 1994). Also, it would be very difficult to performcounterfactual policy analysis of the sort to be presented below with a Vector Switching-RegimeModel. The VAR representation of the data can be interpreted as a linear approximation, and thesimilarity found between regime persistence and shock persistence can be seen as a corroboration ofthe relative accuracy of that approximation.

14. The approach followed here Ð counterfactual optimal policy analysis with multivariate time seriesmodels Ð was pioneered by Litterman (1984).

15. See Kendrick (1981).16. During Argentina’s ISI, capital flows were restricted and foreign lending periodically rationed. Thus,

trade had to be balanced or with some surplus for interest payments. Variance minimization aroundtrend values corresponds, in fact, to this second situation, given that the trend in xm was slightlyupward sloping during most of the period of interest.

17. The simulations were performed in GAMS. For a primer on macroeconomic stochastic simulationsusing GAMS, see Mercado et al. (1998).

18. See for example Dornbusch & Edwards (1991).19. I was unable to find a significant dummy variable representation of the 1975 break. The residual

covariance matrices of both VARs (with and without a dummy variable in the policy reactionfunction) were practically the same. Thus, to make the model more tractable for simulationpurposes, I used the VAR without dummy in the counterfactual experiments.

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Macroeconomic volatility during Argentina’s import substitution stage 161

20. It is curious to see how, even in today’s Argentina, and in spite of the implementation of ambitiouspolicies of structural reform during the 1990s Ð economic openness, privatization and deregula-tion Ð some of these problems remain, at least in form. The Argentine economy is still very muchaffected by foreign shocks Ð e.g. capital flows reversals after the 1994 Mexican crisis and the 1998Brazilian crisis, fall in primary export prices at the end of the 1990s Ð and lacks policy tools to dealwith them, since the currency board system in place since 1991 eliminates the recourse to exchangerate and monetary policy.

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