1. The basic model

The GEE is geared toward answering the question “Did the involvement of the IMF through an ESAF arrangement significantly improve the outcomes for important macroeconomic variables relative to what they would have been in the absence of ESAF-support?”. To answer this question the macroeconomic outcomes or target variables in countries are described as a function of: (i) policies that would have been observed in the absence of a Fund-supported program; (ii) exogenous external factors; (iii) the existence or otherwise of a Fund-supported program; and (iv) unobservable random shocks:

where y_ij is the jth target variable in country i, x_ik is a k-element vector of policy variables that would be observed in country i in the absence of Fund support, w_ih is an h-element vector of exogenous external variables for each country i, d_i is a dummy variable equal to one if a Fund program is in effect and zero otherwise, and ε_ij is a zero mean, fixed variance, serially uncorrelated error. For the jth target variable, β_jk and α_ih are kxl and hxl vectors, respectively, of fixed parameters. After postulating a rule for policies in the absence of a Fund-supported program (x_ik), the model is estimated using pooled cross-section and time-series data drawn from countries and periods in which Fund support was in place and those in which Fund support was absent. The aim is to get consistent estimates for ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$, the “independent effect” of Fund-supported programs on each target variable. If these are statistically significant at a reasonable confidence level, Fund-supported programs are found to have significant effects.

Policies adopted in the absence of a Fund-supported program (x_ik) are directly observable only for nonprogram periods, and thus a key element of the GEE is the construction of a counterfactual for policies during programs. In Goldstein and Montiel (1986) and subsequent empirical applications, this counterfactual is based upon a policy reaction function that links changes in policy instruments to the deviation of the observed lagged value for each target from its desired value, ${\mathrm{y}}_{\mathrm{ij}}^{\mathrm{d}}$. Specifically, the policy reaction function is described by:

where y_ij is a j-element vector of target variables, η_ik is a zero mean, fixed variance, serially uncorrelated error term assumed to be uncorrelated with ε_ij, and Δ is the first difference operator. ^4/ The kxj parameter matrix γ_kj indicates the extent to which policy instruments are adjusted in response to disequilibria in the target variables.

Substituting equation (2) into equation (1) and subsuming ${\mathrm{y}}_{\mathrm{ij}}^{\mathrm{d}}$ in the constant (that is, assuming ${\mathrm{y}}_{\mathrm{ij}}^{\mathrm{d}}$ is invariant across countries and over time) this substitution gives: ^5/

Equation (3) constitutes the basic GEE reduced form model as applied in most earlier studies. While conceptually rigorous, its operational usefulness depends on the validity of several rather restrictive assumptions. The remainder of this section considers some of these issues and suggests possible ways of addressing them. ^6/

2. The counterfactual—which policy reaction function?

The choice of a policy reaction function is a critical step in applying the GEE, and the theoretical and empirical literature offers a profusion of possibilities. The policy reaction function can be set explicitly in an optimizing framework, but this does little, if anything, to narrow the choice; different specifications of policymakers’ objective functions and of the constraints they face generate a wide range of alternative reaction functions (see Turnovsky (1977)). This paper starts with the specification of the policy reaction function used by Goldstein and Montiel (1986), Greene (1989) and Khan (1990) (equation (2)) and experiments with two alternative less restrictive specifications, which are described in this sub-section.

According to equation (2), policies are adjusted only in response to target disequilibria in the previous period. However, it is plausible that policy adjustments in the current period also take account of the impact of external exogenous factors not captured in the lagged value of the target variable. Changes in the terms of trade or in primary commodity prices may have lagged effects; for example, fiscal revenues this year may be weakened by a drop in export prices last year. Assuming policymakers adjust policies with information only on past exogenous shocks (that is, policies are set at the beginning of the year), an enhanced policy reaction function with exogenous external factors (w_ih) lagged one period gives:

When equation (4) is substituted into equation (1) to eliminate the unobservable values of x_ik (and subsuming ${\mathrm{y}}_{\mathrm{ij}}^{\mathrm{d}}$ in the constant) the specification of the GEE becomes:

A second variation of equation (2) results from setting the formulation of policies explicitly in an optimizing framework. One simple approach is to assume that policymakers in each country i choose policies (x_ik) to minimize a quadratic loss function (L) of the form

Subjecting this to the constraint of the economic model postulated in the GEE framework,

the policy reaction function takes the form in levels

with ${\mathrm{y}}_{\mathrm{ij}}^{\mathrm{d}}$ (assumed to be fixed) and other constant terms subsumed in ${\hat{\gamma }}_{\mathrm{o}}$. After substitution for x_ik in equation (1), the reduced form GEE becomes

In this equation, terms in x_ik drop out and the interpretation of the estimated coefficients on the lagged target variable and the exogenous external factors differs from that in equation (3). ^7/

In practice, in an exercise where the goal is to evaluate the effectiveness of Fund-supported programs, an appeal to optimizing principles to motivate the policy reaction function may be thought to create a potential conundrum for the counterfactual approach. If countries are assumed to adopt optimal policies in the absence of a Fund-supported program, why would a country ever turn to the Fund for support? Following this line of argument, the influence of a program would depend on the Fund’s seal of approval strengthening confidence in the economy (and catalyzing external assistance) and thereby enhancing the effectiveness of any given stance of policy. Yet, if policies are the same with or without a program, this presumes that domestic and foreign economic agents fail to recognize the equivalence of policies under the two regimes. In reality the Fund’s seal of approval expands the opportunity set of policies beyond that available to a country not receiving Fund support. In fact, almost all of the countries that had ESAF arrangements were also seeking agreements with creditors to reschedule or restructure external debt, for which entering an arrangement from the Fund was a prerequisite.

Even with careful specification of the policy reaction function, there remains a question of whether individual country behavior can be sensibly aggregated in a uniform model that is stable across countries and over time. Specifically, differing institutional characteristics (for example, the degree of policy discipline inherent in specific exchange rate arrangements or the relationship with a major donor), changing political conditions, or varying severities of economic distress are likely to result in countries formulating policies with respect to different or changing objective functions or subject to different or changing constraints. Another question is whether it is appropriate to assume that the policy reaction function of a program country had it not received Fund support is identical to that of a nonprogram country that did not seek Fund support. For example, the counterfactual for a country receiving Fund support may involve the imposition of exchange restrictions, while countries that do not seek Fund support may constrain themselves to “Fund-type” policies—that is, avoiding the use of exchange restrictions.

3. Characterizing the independent effect of Fund-Supported programs

The simple GEE framework captures the independent effect of Fund support in an additive term (${\beta }_{\mathrm{j}}^{\mathrm{IMF}}{\mathrm{d}}_{\mathrm{i}}$) constant across countries and over time. This term is meant to capture four channels through which a Fund-supported program could affect the macroeconomic targets: (i) changes in the state of confidence in the economy; (ii) changes in the desired value of targets, for example through structural reforms aimed at raising the rate of potential growth; (iii) policies different from what they would have been in the absence of a program; and (iv) changes in the effectiveness of any given stance of policies.

Ideally, the specification of the effect of Fund support would not be restricted to an additive term constant across countries and time but would decompose program effects in a way allowing their magnitude and type to vary across programs. In doing so, it is reasonable to assume that the first two channels described above—changes in confidence and in desired values of targeted variables—could be captured in additive terms. Even here, however, it would be optimal to allow the coefficient ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ to vary across program years (that is by allowing program-specific coefficients).

To capture the third channel of program influences—policies different from what they would have been in the absence of Fund-support—modifications to the model are needed. ^8/ The policies supported by the Fund differ for each country, reflecting countries’ preferences on how to address their problems, the nature of the macroeconomic disequilibria, and the severity of the external financing constraint. Thus, for given relationships between policies and targets, the effect of Fund-supported programs on macroeconomic targets should differ across countries and over time. This variation could be captured with multiplicative dummies to relate the estimate of program effects directly to the difference between policies under the program and the counterfactual. The effect of a Fund-supported program would be

where ${\mathrm{x}}_{\mathrm{ik}}^{\mathrm{P}}$ is the vector of (observed) policies adopted under a program, and ${\mathrm{A}}_{\mathrm{j}}^{\mathrm{IMF}}$ is a constant. The first term in (11) captures the effect of changes in policy instruments and ${\mathrm{A}}_{\mathrm{j}}^{\mathrm{IMF}}$ all other program effects.

The fourth type of program effect—changes in policy effectiveness—operates by altering the β_jk parameters, for given changes in policy variables. For example, changes in fiscal and credit policy under a Fund-supported program may have a greater impact than they would otherwise have had owing to credibility effects when policies and targets are set in a preannounced medium-term framework. In these circumstances, estimates of equation (3) that assume constancy of the β_jk coefficients are open to the “Lucas critique” (Lucas (1976)), and may not measure the true value of ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$. Ideally, the variation of the β_jk parameters would be endogenized by modelling the link between public policies and private expectations. Alternatively, the β_jk coefficients could be allowed to vary across countries and over time, or the variation in the β_jk coefficients could be restricted to systematic differences between program and nonprogram periods. Under the latter specification, the effect of Fund support is:

or

of which expression (11) is a special case and ${\beta }_{\mathrm{ik}}^{\mathrm{P}}$ is a vector of parameters linking policies and targets during programs. The second term in equation (12a) captures the effect of changes in policy effectiveness.

Two steps are needed to get estimates of ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ as defined in equation (12) or (12a). First, the policy reaction function must be estimated to get values of x_ik for program periods (the unobservable counterfactual policies). Second, using the estimates of x_ik and substituting for ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ in equation (1), the following equation must be estimated.

In sum, plausible characterizations of the influence of programs on targets suggest that a simple invariant additive term in the GEE may not do full justice to the range of potential program effects. In practice, the possibility of a more informative decomposition of program effects that allows variation across countries requires a large sample and the ability to identify empirically a stable policy reaction function as examined in Section IV.

4. Dynamics and the importance of initial conditions

Dynamic characteristics of the economy are likely to influence the effects of Fund-supported programs in two main ways that are captured to varying degrees in the simple GEE model. First, the full effects of changes in policies—those supported by the Fund or the counterfactual—and exogenous influences on target variables, particularly output growth, may occur with long lags. In the simplest form of the GEE presented in this paper, such dynamic effects are not allowed for: effects from changes in policies and exogenous factors are assumed to occur within one year. Some applications (Khan (1990)) have eased this restriction by estimating the model with two year average (rather than single year) data for the target variables. This procedure goes some way toward accounting for short lags, but at the cost of not accounting for contemporaneous effects of policies in the second year, which may or may not be a program year.

A second role for dynamic influences works through initial conditions in two ways. First, departures from the desired values of the macroeconomic targets in the preprogram period may generate a policy response even in the absence of a program. In the GEE model, this influence is captured by the term β_jkγ_kj in the coefficients on the lagged target variables in equation (3). If the likelihood of adopting a program is positively correlated with negative prior shocks, then a failure to account for corrective policy, responses that would have taken place in the absence of Fund support would lead to an overstatement of the true effect of a Fund-supported program.

Second, current values of the target variables may be influenced by initial conditions through inertial effects. ^9/ These are not captured in the static model postulated in equation (1), where the stochastic terms ε_ij and η_ik are assumed to be serially uncorrelated: all shocks are assumed to be transitory and to cause one-period changes in target variables that are fully reversed in the following period. This is a quite restrictive assumption. Not only does it require that the time series for each target variable be stationary, but it also rules out a wide range of stationary stochastic processes for which the impact of temporary shocks persists over time. ^10/ If, in fact, significant inertia exists, then imposing full one-period reversion to mean will result in an understatement of the positive effects of a Fund-supported program after a negative shock.

In principle, the following more general dynamic form of equation (1) would allow a wider range of potential dynamic effects:

where L is the lag operator and ρ_s(L) is a lag polynomial in L of order s which captures inertia in the behavior of the target variables. The order of the lag operators may differ for each variable. Substituting equation (2) (the policy reaction function) into equation (14) gives:

which is a general dynamic form of the reduced form GEE (equation (3)).

1. The sample

The model, as specified in equation (3) with some of the modifications suggested in Section II, is estimated for the period 1986-91 with data for 61 of the 66 countries (Appendix Table 1) eligible to use ESAF resources as of 1992 (exclusions are noted below). Nineteen of these countries had ESAF arrangements at some time during the sample period. ^11/ The sample was restricted to ESAF-eligible countries, rather than a larger set of developing countries, for two reasons: first, the many years when stand-by and extended arrangements were in effect in non-ESAF-eligible countries could not be characterized as nonprogram years, yet these arrangements were in many respects not comparable to ESAF arrangements; second, including only low-income countries reduced the scope for parameter instability owing to differences in structural and institutional conditions between low-income countries and other developing countries.

Even the sample restricted to ESAF-eligible countries is quite diverse. The program countries are dominated by heavily indebted African countries with a narrow range of exports and relatively simple market structures. The nonprogram countries are more diverse including, in about equal proportions, indebted African countries and small Caribbean or Pacific island economies with close institutional and financial links to particular industrial countries. There are also a few South and Southeast Asian countries.

For estimation, a number of data points are dropped from the full sample of ESAF-eligible countries. Some observations are excluded because of inadequate data owing to civil strife (Afghanistan (1990-91), Angola, Liberia, and Nicaragua (all years)); major discontinuities which could not be corrected (Djibouti (all years)); extreme isolation (Albania (all years)); or political discontinuities (Democratic Republic of Yemen (1990-1991)) and Yemen Arab Republic (1989-1991)). Years in which countries had a SAF arrangement (except when followed immediately by an ESAF arrangement), Stand-by, or Extended arrangement are omitted because they were considered invalid as nonprogram counterfactuals; this, together with lack of data in 1991, exclude Somalia entirely from the sample.

The exclusion of data points renders the structure of the panel data of program and nonprogram years incomplete. Techniques for analyzing panel data in which missing observations are random or follow a regular (or “rotating”) pattern are not applicable because the gaps in the data do not conform to either of these patterns. Instead, for estimation purposes, the panel data are handled as a pooling of annual observations, with the number of program and nonprogram years varying from country to country.

For the purpose of investigating the stability and the dynamic specification of the GEE a larger set of observations for each country would have been preferable. Extending the sample, however, would have required dropping several countries owing to the lack of consistent data for earlier periods. Also, the number of additional useable observations (that is, years in which countries did not have a Stand-by or Extended arrangement in place) would have been a rather small proportion of the total.

Even for the limited sample covered, the quality of data is poor. In many instances, the accuracy of the measures of macroeconomic variables, such as GDP, is likely to very weak. Also, ad hoc correction for breaks in the series were frequently needed. These fundamental weaknesses in the data qualify the inferences or judgements that can be drawn from them.

2. Definitions: targets, policies, exogenous influences and period of Fund support

This study considers three target variables (y_ji) that reflect the objectives of the ESAF: (i) the growth rate of real GDP; (ii) the average rate of consumer price inflation; and (iii) a measure of external viability—the ratio of external debt service to exports. The last target is preferred to other external indicators, such as the current account, the overall balance of payments, or the level of international reserves, as a measure of external viability for several reasons. ^12/ Most countries entered ESAF arrangements with large debt overhangs, and reducing the debt service ratio to manageable levels was the primary external objective. Within this goal, targeted and actual outcomes for other external variables varied widely depending on initial conditions and prospects for attracting concessional inflows. Moreover, for non-program periods, developments in these other variables sometimes reflected the imposition of formal or informal trade and exchange restrictions rather than changes in the viability of the external position. Increases in reserves were at times associated with the accumulation of arrears. Also, a reserves target did not exist for a sizable proportion of the countries (CFA and ECCB country members) that did not directly own international reserves

Three policy instruments (x_ik) are considered: (i) the deficit of the central government in relation to GDP; (ii) the growth of net domestic assets of the banking system (NDA) ^13/; and (iii) the change in the nominal effective exchange rate (NEER). Ideally, the vector of policy instruments would also include indicators of structural reforms/conditions and institutional arrangements (such as flexible or fixed exchange rate regimes). However, these variables cannot be easily quantified or reduced to an index. The external environment indicators (w_ih) comprise changes in the terms of trade and the growth of export markets. ^14/ Whenever possible, the data were taken from Executive Board documents.

Several difficulties arise in defining the variable denoting the presence of a Fund-supported program (d_i). First, the distinction between program and non-program years is blurred when Fund support starts in the middle of the year. For this study, any year in which a SAF/ESAF program was in effect for six months or more was considered a program year. In applying this rule, the program periods (periods over which a program was framed) rather than annual arrangement periods (periods over which programs were approved) were considered. Even this rule, however, does not clearly delineate the period during which Fund support influenced policies and outcomes. Usually, substantive negotiations and policy actions occurred in anticipation of Fund support in the year preceding the formal program. Also, in some cases, the Fund had a considerable influence on policies after an ESAF arrangement. For example, The Gambia’s SAF/ESAF arrangements stretched from FY1986 (July-June) to FY1990, but even in FY1991, the Fund monitored macroeconomic developments vis-a-vis quantified targets agreed with the authorities.

How to handle variations in program implementation across countries presents a second difficulty. Some would argue that implementation should be reflected in the estimated model, for example, by representing the influence of the Fund as the proportion of purchases made relative to total access under the arrangement. Purchases are likely to be an imperfect indicator of implementation, however, because purchases in SAF/ESAF programs are scheduled at low (six-month) frequencies and waivers may be granted to permit purchases even in the event of policy slippages. Alternatively, the effectiveness of Fund support could be judged in terms of outcomes, whether or not programmed policies are implemented: a period when a country has an agreed program with the Fund but fails to implement it or meet the targets is treated as a program year when the effect of the Fund is zero. This study takes the latter approach and uses a binary, one-zero index of Fund involvement for the dummy variable (d_i).

A third point to recognize is that estimates of Fund-supported programs will likely include the effects of parallel World Bank programs. As distinct from the Fund’s Stand-by and Extended Facilities, SAF/ESAF arrangements require explicit collaboration among country authorities, the World Bank and the Fund.

1. The generalized evaluation estimator

Estimation results were obtained for the basic GEE equation (3) and several of the modified versions suggested in Section II to ease the restrictiveness of the simple framework: (i) the expanded equation (5), which includes lagged exogenous external influences in the policy reaction function was estimated; ^15/ (ii) country and time dummies were introduced to help account for some of the cross-country differences in economic structures and time-specific exogenous developments not captured in the terms of trade and market growth variables; and (iii) a correction procedure developed by Heckman (1979) (explained in detail in the Annex) was tried to correct for possible sample selection bias. Other methods discussed in Section II to ease the restrictive assumptions of the GEE were not feasible. Specifically, consideration of a richer characterization of the effects of Fund support as described in equations (11-13) was not possible because the estimates of the policy reaction function, which are needed to estimate counterfactual policies during program periods, proved to be very poor. Also, because of the short time series available, a more complex dynamic structure for the GEE could not be explored.

Regression estimates based on pooled time-series, cross-country data are prone to heteroschedasticity, and even after the inclusion of country-specific and time dummies in the estimated equations, regression residuals displayed heteroschedasticity. ^16/ A weighted least squares correction for heteroschedasticity was not attempted because, without information on the form of heteroschedasticity, the primacy of one weighing scheme over another is unknown. ^17/ Instead, the ordinary least squares coefficients were retained, and the reported t-statistics were computed from heteroschedasticconsistent estimates of the standard errors based on White’s variance-covariance estimator that provides consistent estimates even when the exact form of heteroschedasticity is not known. ^18/

Table 1 presents the preferred estimates from these exercises. These estimates exclude the lagged values of the exogenous variables (the modification suggested in equation (5)) because their coefficients were not significantly different from zero and the terms had little effect on the fit of the regression or the estimates of the other coefficients. The results also exclude the time dummies mentioned above because their coefficients were generally not significant and had little effect on (or worsened) the fit of the equations. The impact of Fund-supported programs is found to be sizeable and statistically significant with respect to growth (at the 5 percent level) and the external debt service ratio (at the 10 percent level), but not inflation. On average, growth rates are found to be more than 1 percentage point per annum higher during program periods than they would have been in the absence of a Fund-supported program. Debt-service ratios are found on average to be more than 5 percentage points lower during program periods than they would have been in the absence of a Fund-supported program. This effect on the debt-service ratio is not attributable to the link between Paris and London Club agreements and Fund support, because the debt service ratio is measured before debt relief. ^19/

Table 1.

Estimates of the GEE ^1/

^1/The regression estimates were obtained using a ordinary least squares procedure, with country-specific dummies included in the specification. Standard errors and t-statistics of coefficients are computed using White’s heteroschedasticity-consistent variance-covariance estimator. The figures in parentheses are t-statistics; ${\overline{\mathrm{R}}}^2$ is the adjusted coefficient of determination; S.E.E. is the standard error of the regression. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

Table 1.

Estimates of the GEE ^1/

Target Variable	Real GDP Growth Rate	Inflation Rate	External Debt Service Ratio
Constant	-6.619 (-1.71)	10.248 (1.08)	22.258** (3.98)
Lagged real GDP growth rate	-1.107** (-17.96)	-0.764* (-2.18)	0.022 (0.09)
Lagged inflation rate	0.0005 (0.13)	-0.687** (-4.76)	0.027 (1.09)
Lagged external debt service ratio	0.013 (0.74)	0.106 (1.14)	-0.376** (-3.09)
Lagged fiscal balance/GDP	-0.042 (-1.37)	-0.467 (-1.31)	0.097 (0.76)
Lagged net domestic asset growth	0.004 (1.82)	-0.088 (-1.47)	-0.020 (-1.78)
Lagged percentage change in NEER	-0.009 (-1.03)	0.436* (2.12)	0.058 (1.05)
Current percentage change in terms of trade	0.002 (0.21)	-0.104 (-0.78)	-0.104** (3.44)
Current export market growth	0.090 (1.78)	0.293 (1.26)	-0.059 (-0.30)
IMF program dummy	1.374* (2.18)	-3.330 (-0.35)	-5.552 (-1.75)
${\overline{\mathrm{R}}}^2$	0.537	0.398	0.069
S.E.E.	3.259	29.612	15.734
Number of observations	291	291	291
Breusch-Pagan test for heteroschedasticity	1.35	10.83*	23.71**
Jarque-Bera test for normality of residuals	26.57**	28,231.00**	7,086.90**

^1/The regression estimates were obtained using a ordinary least squares procedure, with country-specific dummies included in the specification. Standard errors and t-statistics of coefficients are computed using White’s heteroschedasticity-consistent variance-covariance estimator. The figures in parentheses are t-statistics; ${\overline{\mathrm{R}}}^2$ is the adjusted coefficient of determination; S.E.E. is the standard error of the regression. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

View Table

Table 1.

Estimates of the GEE ^1/

Target Variable	Real GDP Growth Rate	Inflation Rate	External Debt Service Ratio
Constant	-6.619 (-1.71)	10.248 (1.08)	22.258** (3.98)
Lagged real GDP growth rate	-1.107** (-17.96)	-0.764* (-2.18)	0.022 (0.09)
Lagged inflation rate	0.0005 (0.13)	-0.687** (-4.76)	0.027 (1.09)
Lagged external debt service ratio	0.013 (0.74)	0.106 (1.14)	-0.376** (-3.09)
Lagged fiscal balance/GDP	-0.042 (-1.37)	-0.467 (-1.31)	0.097 (0.76)
Lagged net domestic asset growth	0.004 (1.82)	-0.088 (-1.47)	-0.020 (-1.78)
Lagged percentage change in NEER	-0.009 (-1.03)	0.436* (2.12)	0.058 (1.05)
Current percentage change in terms of trade	0.002 (0.21)	-0.104 (-0.78)	-0.104** (3.44)
Current export market growth	0.090 (1.78)	0.293 (1.26)	-0.059 (-0.30)
IMF program dummy	1.374* (2.18)	-3.330 (-0.35)	-5.552 (-1.75)
${\overline{\mathrm{R}}}^2$	0.537	0.398	0.069
S.E.E.	3.259	29.612	15.734
Number of observations	291	291	291
Breusch-Pagan test for heteroschedasticity	1.35	10.83*	23.71**
Jarque-Bera test for normality of residuals	26.57**	28,231.00**	7,086.90**

^1/The regression estimates were obtained using a ordinary least squares procedure, with country-specific dummies included in the specification. Standard errors and t-statistics of coefficients are computed using White’s heteroschedasticity-consistent variance-covariance estimator. The figures in parentheses are t-statistics; ${\overline{\mathrm{R}}}^2$ is the adjusted coefficient of determination; S.E.E. is the standard error of the regression. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

On the other right hand-side variables very few coefficients are significant at the 5 or 1 percent confidence level. The exceptions are own lagged levels of the target variables, which enter with coefficients significantly different from zero at the 1 percent level; the lagged level of the real GDP growth rate and the change in the nominal effective exchange rate (which has an implausible positive sign) in the inflation equation; and the terms of trade in the debt service ratio equation. The lagged fiscal balance and lagged net domestic asset growth are not found to have a significant impact on the outcome of any target variable. ^20/

In broad terms, these results are similar to those of Khan (1990)—the most recent and largest study using the GEE methodology to estimate the effects of programs supported by upper credit tranche Stand-by and Extended arrangements. That study uses a sample of 1,104 observations spanning 69 countries over the period 1973-88, including 315 program years. ^21/ It is noteworthy that Khan’s sample is quite different from that used in this study: it excludes SAF/ESAF arrangements from the program sample, ends several years prior to the sample used in this paper and includes low- and middle-income developing countries. Like this study, Khan finds that lagged values of target variables significantly influence current changes of these variables. He also finds few significant effects of lagged policies on current target variables; the main difference in this respect is that Khan finds significant and plausible effects of the lagged fiscal balance on the target variables, while in this study the fiscal effect is found to be insignificant. Both studies find programs lead to reductions (though not statistically significant) in inflation. The most important difference between the two studies, however, is in the estimate of the effect of Fund support on growth: Khan finds a significantly negative effect on growth, in contrast to the significantly positive effect in the current study.

2. The policy reaction function

Identification of the coefficients and measurement of their standard errors in the policy reaction function from the reduced form GEE is neither possible nor necessary for obtaining estimates of the ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ terms. ^22/ Nevertheless, obtaining estimates of them, by directly estimating the counterfactual policy reaction functions with data from the (observable) nonprogram periods only, is useful for two reasons. First, it provides a means of evaluating the validity of the reaction functions for the sample over which they can be estimated. Second, direct estimates of the policy reaction function are part of the procedure to correct for sample selection bias arising when unobservable factors that influence countries’ decisions to receive Fund support also influence their policy reactions. To identify whether this potential source of bias exists, and to correct for it, a two-step procedure first proposed by Heckman (1979) is used. ^23/ First, a probit model of the probability of not having a program is estimated. Second, the probit estimates are used to calculate the inverse Mills ratio ^24/ (for each nonprogram observation) which, when included in the policy reaction function estimated for the nonprogram observations, accounts for the bias due to nonrandom sampling. Annex I describes the procedure in detail and reports estimates of the probit model.

As with the GEE, country and time dummies were introduced in the policy reaction functions. Most of the time and country dummies had coefficients insignificantly different from zero and had little effect on or worsened the overall fit of the equation (reduced the ${\overline{\mathrm{R}}}^2$) and therefore were not retained in the reported results.

The regression estimates of the policy reaction function are poor in several respects (Table 2): the R-squared statistics are negative or very close to zero; t-statistics for individual coefficients are insignificant (except on the debt service ratio in the equation for the nominal effective exchange rate); F-tests can not reject the null joint hypothesis of zero slopes; and the regression residuals exhibit statistically significant heteroschedasticity and nonnormality. The insignificant coefficients of the inverse Mills ratio suggest that sample selection bias is not present in the sample. However, in view of the poor performance of the estimated policy reaction function this result is not particularly revealing. In short, these estimates provide a weak basis for deriving estimates of the unobservable counterfactual policies for program periods. Thus, no attempt was made to estimate equation (13) to identify more complex characterizations of the independent effects of Fund-supported programs.

Table 2.

Estimates of the Policy Reaction Function ^1/

^1/The regression estimates were obtained from the sample of nonprogram observations using an ordinary least squares procedure. Standard errors and t-statistics of coefficients are computed using White’s heteroschedasticity-consistent variance-covariance estimator. The figures in parentheses are t-statistics; is the adjusted coefficient of determination; S.E.E. is the standard error of the regression. A single asterisk indicates statistical significance of the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

^2/Values of the inverse Mills ratio were computed using the estimated probit equation reported in Annex Table 1.

Table 2.

Estimates of the Policy Reaction Function ^1/

Policy Variable	Δ Fiscal Balance/GDP	Δ Net Domestic Asset Growth	Δ Percentage Change in NEER
Constant	1.643 (0.67)	-2.209 (-0.11)	-2.607 (-0.35)
Lagged real GDP growth rate	0.024 (0.19)	-1.090 (-1.11)	-0.204 (-0.69)
Lagged inflation rate	0.006 (1.02)	-0.081 (-0.12)	0.017 (0.29)
Lagged external debt service ratio	-0.0007 (-0.04)	-0.097 (-0.40)	-0.152* (-2.22)
Inverse Mills ratio 2/	-3.911 (-0.65)	16.271 (0.24)	13.070 (0.76)
${\overline{\mathrm{R}}}^2$	-0.013	-0.016	0.019
S.E.E.	7.064	106.339	23.239
Number of observations	203	203	203
F-statistic (zero slopes)	0.36	0.22	1.96
Breusch-Pagan test for heteroschedasticity	0.70	62.27**	21.88**
Jarque-Bera test for normality of residuals	4,875.06**	17,986.30**	495.36**

^1/The regression estimates were obtained from the sample of nonprogram observations using an ordinary least squares procedure. Standard errors and t-statistics of coefficients are computed using White’s heteroschedasticity-consistent variance-covariance estimator. The figures in parentheses are t-statistics; is the adjusted coefficient of determination; S.E.E. is the standard error of the regression. A single asterisk indicates statistical significance of the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

^2/Values of the inverse Mills ratio were computed using the estimated probit equation reported in Annex Table 1.

View Table

Table 2.

Estimates of the Policy Reaction Function ^1/

Policy Variable	Δ Fiscal Balance/GDP	Δ Net Domestic Asset Growth	Δ Percentage Change in NEER
Constant	1.643 (0.67)	-2.209 (-0.11)	-2.607 (-0.35)
Lagged real GDP growth rate	0.024 (0.19)	-1.090 (-1.11)	-0.204 (-0.69)
Lagged inflation rate	0.006 (1.02)	-0.081 (-0.12)	0.017 (0.29)
Lagged external debt service ratio	-0.0007 (-0.04)	-0.097 (-0.40)	-0.152* (-2.22)
Inverse Mills ratio 2/	-3.911 (-0.65)	16.271 (0.24)	13.070 (0.76)
${\overline{\mathrm{R}}}^2$	-0.013	-0.016	0.019
S.E.E.	7.064	106.339	23.239
Number of observations	203	203	203
F-statistic (zero slopes)	0.36	0.22	1.96
Breusch-Pagan test for heteroschedasticity	0.70	62.27**	21.88**
Jarque-Bera test for normality of residuals	4,875.06**	17,986.30**	495.36**

^1/The regression estimates were obtained from the sample of nonprogram observations using an ordinary least squares procedure. Standard errors and t-statistics of coefficients are computed using White’s heteroschedasticity-consistent variance-covariance estimator. The figures in parentheses are t-statistics; is the adjusted coefficient of determination; S.E.E. is the standard error of the regression. A single asterisk indicates statistical significance of the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

^2/Values of the inverse Mills ratio were computed using the estimated probit equation reported in Annex Table 1.

3. Significance and stability of the estimates

How much confidence can be placed on the estimates of the effects of programs? The applications of the GEE to date have tended to focus on the size and significance of the estimates of the ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ parameters, without testing the validity of the basic assumptions in the GEE or, in some cases, even without reporting indicators of the overall goodness of fit. In this section, some simple experiments to determine the robustness of the parameters estimates are reported.

There are many ways to evaluate the regression estimates, and the approach taken here is not intended to be exhaustive. As measured by the R-squared statistic, the overall fit of the estimated equations is modest (almost nil for the external debt service ratio). This, together with the evidence of heteroschedastic residuals (even after the inclusion of country and time dummies) and the large number of coefficients insignificantly different from zero or with counterintuitive signs, suggests the possibility of misspecification and, therefore, of biased coefficient estimates. One potentially important omission is structural conditions/reforms that figured prominently in SAF/ESAF-supported programs. A second concern is the possibility of heterogeneity bias (Hsiao (1986)). ^25/ This arises when parameter estimates are obtained by imposing identical coefficients on pooled data but the true values of the parameters differ significantly across countries and/or over time, owing for example to differences in policy regimes. In these circumstances, pooled estimates that ignore parameter heterogeneities are likely to be biassed. A third concern is that the regression residuals fail to pass the Jarque-Bera test for normality, signalling the risk of invalid inferential statements, even in large samples (Jarque and Bera (1987)).

The reliability of the parameter estimates is also revealed by the stability properties of the model. Earlier applications of the GEE have not reported tests of stability. ^26/ Most investigate changes in program effectiveness as measured by the ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ coefficients (or the equivalent for World Bank programs) between two sub-periods of the sample used; Greene (1989) and Corbo and Rojas (1992) also report changes in estimates of the other coefficients of the reduced form GEE. Yet, in each study, the evidence of changes in point estimates of the ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ and other coefficients (at times statistically significant) is not seen as a possible indication of instability in the underlying model.

The most appealing approach to exploring instability would be to estimate a varying parameters model in which estimated parameters are allowed to vary across countries ^27/ and over time ^28/ and therefore also between program and nonprogram periods. This general approach has the merit of permitting tests of stability and uniformity restrictions nested within a less restrictive framework. ^29/ However, such an approach requires a data set considerably larger than that available in this study: to explore inter-country instability, the number of data points for each country must exceed the number of regressors; and to explore instability between program and nonprogram periods, the number of data points for each country in each regime (program and nonprogram) must exceed the number of regressors.

In light of the constraints imposed by the structure of our sample (that is, the small and unequal number of annual observations for each country), the stability of the individual parameters β_jk and ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ is examined by recursive regression methods. ^30/ Two types of recursive exercises were done: in the first, the reduced form GEE was estimated recursively, starting with a baseline sample (comprising all nonprogram observations plus one program observation) and then adding in program observations one at a time (reported as “program recursions”); ^31/ in the second, the recursive procedure was carried out by starting with the full sample and then subtracting nonprogram observations one-by-one (reported as “nonprogram recursions”). The share of estimates in the recursions that differs significantly from the pooled sample estimates provides an indication of the sensitivity of the estimated parameters to variations in the sample. Also of interest, particularly for parameters where whole sample estimates are significantly different from zero, is the share of recursion estimates that differ significantly from zero. ^32/

The recursion estimates of the effectiveness of IMF support—the ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ coefficients—show that the point estimates and statistical significance are sensitive to changes in the sample (Charts 1-3). Of the six recursive exercises (two for each of the three equations), four produce estimates of ${\beta }_{\mathrm{j}}^{\mathrm{IMF}}$ that are significantly different from the whole sample estimates in at least 15 percent of the recursions. Significant deviations are more frequent in the program than in the non-program recursions. In all the recursive exercises, a sizeable share (64-100 percent) of recursion estimates are not significantly different from zero. Thus, the finding of significant effects of Fund support on growth and the debt service ratio cannot be considered robust to variations in the composition of the sample.

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Recursive Point Estimates and Confidence Intervals for IMF Program Dummy Variable

(Real GDP growth rates)

Citation: IMF Working Papers 1995, 092; 10.5089/9781451951486.001.A001

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Recursive Point Estimates and Confidence Intervals for IMF Program Dummy Variable

(Rate of inflation)

Citation: IMF Working Papers 1995, 092; 10.5089/9781451951486.001.A001

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Recursive Point Estimates and Confidence Intervals for IMF Program Dummy Variable

(External debt service ratio)

Citation: IMF Working Papers 1995, 092; 10.5089/9781451951486.001.A001

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For the coefficients on the lagged policy variables, the recursive exercises suggest that the full sample estimates of policy effects are relatively robust (Tables 3 and 4). Typically, small proportions (in 13 of the 18 exercises, none) of the recursions produce estimates significantly different from the full sample estimates. As almost all of the full sample estimates are insignificantly different from zero, most (11) of the 18 recursions produce estimates that are also not significantly different from zero. There are a few exceptions to these generalizations, although none suggest the basis for questioning the general pattern of lagged policy variables having little effect on target variables.

Table 3.

Share of Statistically Significant t-statistics at the 5 Percent Level in Recursive Estimates of the GEE ^1/

^1/Excluding full sample estimates, the total number of recursive estimates was equal to 87 for program year observations, and to 202 for nonprogram year observations.

^2/Recursive procedure starts with the baseline sample (all nonprogram observations plus one program observation) and adds program observations one-by-one.

^3/Recursive procedure starts with the entire sample and subtracts out one-by-one non-program observations.

Table 3.

Share of Statistically Significant t-statistics at the 5 Percent Level in Recursive Estimates of the GEE ^1/

Target Variable	Lagged Fiscal Balance/GDP		Lagged NDA Growth		Lagged NEER Percent Change
	β 1		β 2		β 3
	H0:β1 = 0	H0:β1 = full sample value	H0:β2 = 0	H0:β2 = full sample value	H0:β3 = 0	H0:β3 = full sample value
Δ Real GDP growth rate
Share in total program year recursions 2/	0.0	0.0	0.0	0.0	0.0	0.0
Share in total nonprogram year recursions 3/	45.0	0.0	47.0	0.0	0.0	4.5
Δ Inflation rate
Share in total program year recursions 2/	0.0	0.0	0.0	0.0	3.4	0.0
Share in total nonprogram year recursions 3/	0.0	0.0	10.9	13.9	85.6	0.0
Δ External debt service ratio
Share in total program year recursions 2/	0.0	36.8	0.0	0.0	0.0	0.0
Share in total nonprogram year recursions 3/	54.0	49.5	57.4	0.0	0.0	2.5

^1/Excluding full sample estimates, the total number of recursive estimates was equal to 87 for program year observations, and to 202 for nonprogram year observations.

^2/Recursive procedure starts with the baseline sample (all nonprogram observations plus one program observation) and adds program observations one-by-one.

^3/Recursive procedure starts with the entire sample and subtracts out one-by-one non-program observations.

View Table

Table 3.

Share of Statistically Significant t-statistics at the 5 Percent Level in Recursive Estimates of the GEE ^1/

Target Variable	Lagged Fiscal Balance/GDP		Lagged NDA Growth		Lagged NEER Percent Change
	β 1		β 2		β 3
	H0:β1 = 0	H0:β1 = full sample value	H0:β2 = 0	H0:β2 = full sample value	H0:β3 = 0	H0:β3 = full sample value
Δ Real GDP growth rate
Share in total program year recursions 2/	0.0	0.0	0.0	0.0	0.0	0.0
Share in total nonprogram year recursions 3/	45.0	0.0	47.0	0.0	0.0	4.5
Δ Inflation rate
Share in total program year recursions 2/	0.0	0.0	0.0	0.0	3.4	0.0
Share in total nonprogram year recursions 3/	0.0	0.0	10.9	13.9	85.6	0.0
Δ External debt service ratio
Share in total program year recursions 2/	0.0	36.8	0.0	0.0	0.0	0.0
Share in total nonprogram year recursions 3/	54.0	49.5	57.4	0.0	0.0	2.5

^1/Excluding full sample estimates, the total number of recursive estimates was equal to 87 for program year observations, and to 202 for nonprogram year observations.

^2/Recursive procedure starts with the baseline sample (all nonprogram observations plus one program observation) and adds program observations one-by-one.

^3/Recursive procedure starts with the entire sample and subtracts out one-by-one non-program observations.

Table 4:

Generalized Evaluation Estimates of Policy Parameters (β) ^1/

^1/Standard errors and t-statistics computing White’s heteroschedasticity-consistent variance-covariance estimator. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

Table 4:

Generalized Evaluation Estimates of Policy Parameters (β) ^1/

Target Variable			Policy Variable
			Lagged Fiscal Balance/GDP			Lagged Net Domestic Asset Growth			Lagged NEER (Percentage Change)
			β1-2 standard errors	β1 (t-statistic)	β1+2 standard errors	β2-2 standard errors	β2 (t-statistic)	β2+2 standard errors	β3-2 standard errors	β3(t-statistic)	β3+2 standard errors
Δ Real GDP growth rate
	Full sample estimates		-0.103	-0.042	0.019	0.0004	0.004	0.009	-0.027	-0.009	0.009
				(-1.37)			(1.82)			(1.03)
	Recursive estimates
		•Minimum	-0.411	-0.180	0.051	-0.001	0.003	0.006	-0.055	-0.022	0.012
				(1.55)			(1.46)			(-1.30)
		•Maximum	-0.094	-0.033	0.028	-0.004	0.013	0.030	-0.005	0.010	0.026
				(-1.08)			(1.51)			(1.32)
Δ Inflation rate
	Full sample estimates		-1.181	-0.467	0.247	-0.207	-0.088	0.031	0.025	0.436*	0.846
				(-1.31)			(-1.47)			(2.12)
	Recursive estimates
		•Minimum	-6.101	-2.592	0.917	-0.216	-0.116*	-0.015	-0.190	0.170	0.530
				(-1.48)			(-2.31)			(0.95)
		•Maximum	-0.954	1.358	3.669	-0.032	0.162	0.356	0.193	0.873*	1.553
				(1.17)			(1.67)			(2.567)
Δ External debt service ratio
	Full sample estimates
			-0.157	0.097	0.351	-0.043	-0.020	0.002	-0.052	0.058	0.168
	Recursive estimates			(0.76)			(-1.78)			(1.05)
		•Minimum
			-0.291	-0.116	0.058	-0.068	-0.041**	-0.013	-0.174	-0.057	0.060
				(-1.33)			(-2.99)			(-0.97)
		•Maximum
			1.095	1.790	2.486	-0.067	0.004	0.076	-0.018	0.128	0.275
				(5.149)**			(0.11)			(1.76)

^1/Standard errors and t-statistics computing White’s heteroschedasticity-consistent variance-covariance estimator. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

View Table

Table 4:

Generalized Evaluation Estimates of Policy Parameters (β) ^1/

Target Variable			Policy Variable
			Lagged Fiscal Balance/GDP			Lagged Net Domestic Asset Growth			Lagged NEER (Percentage Change)
			β1-2 standard errors	β1 (t-statistic)	β1+2 standard errors	β2-2 standard errors	β2 (t-statistic)	β2+2 standard errors	β3-2 standard errors	β3(t-statistic)	β3+2 standard errors
Δ Real GDP growth rate
	Full sample estimates		-0.103	-0.042	0.019	0.0004	0.004	0.009	-0.027	-0.009	0.009
				(-1.37)			(1.82)			(1.03)
	Recursive estimates
		•Minimum	-0.411	-0.180	0.051	-0.001	0.003	0.006	-0.055	-0.022	0.012
				(1.55)			(1.46)			(-1.30)
		•Maximum	-0.094	-0.033	0.028	-0.004	0.013	0.030	-0.005	0.010	0.026
				(-1.08)			(1.51)			(1.32)
Δ Inflation rate
	Full sample estimates		-1.181	-0.467	0.247	-0.207	-0.088	0.031	0.025	0.436*	0.846
				(-1.31)			(-1.47)			(2.12)
	Recursive estimates
		•Minimum	-6.101	-2.592	0.917	-0.216	-0.116*	-0.015	-0.190	0.170	0.530
				(-1.48)			(-2.31)			(0.95)
		•Maximum	-0.954	1.358	3.669	-0.032	0.162	0.356	0.193	0.873*	1.553
				(1.17)			(1.67)			(2.567)
Δ External debt service ratio
	Full sample estimates
			-0.157	0.097	0.351	-0.043	-0.020	0.002	-0.052	0.058	0.168
	Recursive estimates			(0.76)			(-1.78)			(1.05)
		•Minimum
			-0.291	-0.116	0.058	-0.068	-0.041**	-0.013	-0.174	-0.057	0.060
				(-1.33)			(-2.99)			(-0.97)
		•Maximum
			1.095	1.790	2.486	-0.067	0.004	0.076	-0.018	0.128	0.275
				(5.149)**			(0.11)			(1.76)

^1/Standard errors and t-statistics computing White’s heteroschedasticity-consistent variance-covariance estimator. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

In order to investigate the stability of the parameters in the policy reaction functions (γ_kj), a simplified recursive exercise over the sample of nonprogram observations was performed. The recursions began with an initial sample of 25 observations, and observations were added one-by-one. ^33/ The results, reported in Tables 5 and 6, indicate that the coefficient estimates from the full sample of non-program observations are generally robust across recursions, and (except for the lagged external debt service ratio in the equation the nominal effective exchange rate policy) are insignificantly different from zero. Although the estimates of the coefficient on inflation in the exchange rate equation reveal a significant reversal of sign across recursions, sign reversals are not widespread; most of the estimates are neither significantly different from zero nor from the full sample estimate.

Table 5:

Sensitivity of Estimates of Policy Reaction Function Parameters (γ) ^1/

^1/Standard errors and t-statistics computing White’s heteroschedasticity-consistent variance-covariance estimator. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

^2/Recursive least squares estimates obtained by adding observations one-by-one to an initial sample of 25 nonprogram years (corresponding to a minimum of 20 degrees freedom).

Table 5:

Sensitivity of Estimates of Policy Reaction Function Parameters (γ) ^1/

Target Variable			Policy Variable
			Δ Fiscal Balance/GDP			Δ Net Domestic Asset Growth			Δ NEER (Percentage Change)
			γ-2 standard errors	γ (t-statistic)	γ+2 standard errors	γ-2 standard errors	γ (t-statistic)	γ+2 standard errors	γ-2 standard errors	γ (t-statistic)	γ+2 standard errors
Lagged GDP growth rate
	Full sample estimates		-0.222	0.0024	0.269	-3.063	-1.090	0.882	-0.795	-0.204	0.388
				(0.19)			(-1.11)			(-0.69)
	Recursive estimates 2/
		•Minimum	-0.712	-0.159	0.394	-2.793	-1.181	0.431	-1.861	-0.685	0.491
				(-0.57)			(-1.46)			(-1.16)
		•Maximum	-0.205	0.156	0.516	-1.184	0.149	1.482	-0.355	0.510	1.375
				(0.86)			(0.22)			(1.18)
Lagged inflation rate
	Full sample estimates		-0.003	0.006	0.016	-1.474	-0.081	1.312	-0.097	-0.017	0.131
				(1.02)			(-0.12)			(0.29)
	Recursive estimates 2/
		•Minimum	-1.196	-0.448	0.300	-2.636	-1.398*	-0.160	-0.768	-0.439**	-0.110
				(-1.20)			(-2.26)			(-2.67)
		•Maximum	-0.148	0.104	0.356	-0.851	0.504	1.861	0.192	0.511**	0.830
				(0.82)			(0.74)			(3.20)
Lagged external debt service ratio
	Full sample estimates		-0.041	-0.0007	0.039	-0.584	-0.097	0.389	-0.289	-0.152*	-0.015
				(-0.04)			(-0.40)			(-2.22)
	Recursive estimates 2/
		•Minimum	-0.102	-0.029	0.044	-1.300	-0.691*	-0.081	-0.708	-0.397*	-0.085
				(-0.79)			(-2.27)			(-2.55)
		•Maximum	-0.127	0.075	0.277	-0.222	0.277	0.775	-0.065	0.211	0.487
				(0.74)			(1.11)			(1.53)

^1/Standard errors and t-statistics computing White’s heteroschedasticity-consistent variance-covariance estimator. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

^2/Recursive least squares estimates obtained by adding observations one-by-one to an initial sample of 25 nonprogram years (corresponding to a minimum of 20 degrees freedom).

View Table

Table 5:

Sensitivity of Estimates of Policy Reaction Function Parameters (γ) ^1/

Target Variable			Policy Variable
			Δ Fiscal Balance/GDP			Δ Net Domestic Asset Growth			Δ NEER (Percentage Change)
			γ-2 standard errors	γ (t-statistic)	γ+2 standard errors	γ-2 standard errors	γ (t-statistic)	γ+2 standard errors	γ-2 standard errors	γ (t-statistic)	γ+2 standard errors
Lagged GDP growth rate
	Full sample estimates		-0.222	0.0024	0.269	-3.063	-1.090	0.882	-0.795	-0.204	0.388
				(0.19)			(-1.11)			(-0.69)
	Recursive estimates 2/
		•Minimum	-0.712	-0.159	0.394	-2.793	-1.181	0.431	-1.861	-0.685	0.491
				(-0.57)			(-1.46)			(-1.16)
		•Maximum	-0.205	0.156	0.516	-1.184	0.149	1.482	-0.355	0.510	1.375
				(0.86)			(0.22)			(1.18)
Lagged inflation rate
	Full sample estimates		-0.003	0.006	0.016	-1.474	-0.081	1.312	-0.097	-0.017	0.131
				(1.02)			(-0.12)			(0.29)
	Recursive estimates 2/
		•Minimum	-1.196	-0.448	0.300	-2.636	-1.398*	-0.160	-0.768	-0.439**	-0.110
				(-1.20)			(-2.26)			(-2.67)
		•Maximum	-0.148	0.104	0.356	-0.851	0.504	1.861	0.192	0.511**	0.830
				(0.82)			(0.74)			(3.20)
Lagged external debt service ratio
	Full sample estimates		-0.041	-0.0007	0.039	-0.584	-0.097	0.389	-0.289	-0.152*	-0.015
				(-0.04)			(-0.40)			(-2.22)
	Recursive estimates 2/
		•Minimum	-0.102	-0.029	0.044	-1.300	-0.691*	-0.081	-0.708	-0.397*	-0.085
				(-0.79)			(-2.27)			(-2.55)
		•Maximum	-0.127	0.075	0.277	-0.222	0.277	0.775	-0.065	0.211	0.487
				(0.74)			(1.11)			(1.53)

^1/Standard errors and t-statistics computing White’s heteroschedasticity-consistent variance-covariance estimator. A single asterisk indicates statistical significance at the 5 percent level; two asterisks indicate statistical significance at the 1 percent level.

^2/Recursive least squares estimates obtained by adding observations one-by-one to an initial sample of 25 nonprogram years (corresponding to a minimum of 20 degrees freedom).

Table 6:

Share of Statistically Significant t-statistics at the 5 Percent Level in Recursive Estimates of the Policy Reaction Function ^1/

(In Percent)

^1/Excluding full nonprogram sample estimates, the total number of recursive estimates was equal to 178 for non-program years observations. Shares of statistically significant t-statistics at the 10 percent level are reported in parentheses.

Table 6:

Share of Statistically Significant t-statistics at the 5 Percent Level in Recursive Estimates of the Policy Reaction Function ^1/

(In Percent)

Policy Variable	Δ Fiscal Balance/GDP		Δ Net Domestic Assets Growth		Δ NEER Percentage Change
	H0: γ= 0	H0: γ= full sample value	H0: γ= 0	H0: γ= full sample value	H0: γ=0	H0: γ= full sample value
Lagged GDP Growth Rate	0.0	0.0	0.0	0.0	0.0	0.0
	(0.0)	(0.0)	(0.0)	(3.4)	(0.0)	(0.6)
Lagged Inflation Rate	39.3	0.0	0.6	9.0	3.4	2.8
	(57.9)	(38.6)	(3.4)	(9.6)	(7.3)	(7.3)
Lagged External Debt Service Ratio	0.0	0.0	9.0	0.0	70.8	5.6
	(0.0)	(0.0)	(14.0)	(7.9)	(73.6)	(14.0)

^1/Excluding full nonprogram sample estimates, the total number of recursive estimates was equal to 178 for non-program years observations. Shares of statistically significant t-statistics at the 10 percent level are reported in parentheses.

View Table

Table 6:

Share of Statistically Significant t-statistics at the 5 Percent Level in Recursive Estimates of the Policy Reaction Function ^1/

(In Percent)

Policy Variable	Δ Fiscal Balance/GDP		Δ Net Domestic Assets Growth		Δ NEER Percentage Change
	H0: γ= 0	H0: γ= full sample value	H0: γ= 0	H0: γ= full sample value	H0: γ=0	H0: γ= full sample value
Lagged GDP Growth Rate	0.0	0.0	0.0	0.0	0.0	0.0
	(0.0)	(0.0)	(0.0)	(3.4)	(0.0)	(0.6)
Lagged Inflation Rate	39.3	0.0	0.6	9.0	3.4	2.8
	(57.9)	(38.6)	(3.4)	(9.6)	(7.3)	(7.3)
Lagged External Debt Service Ratio	0.0	0.0	9.0	0.0	70.8	5.6
	(0.0)	(0.0)	(14.0)	(7.9)	(73.6)	(14.0)

^1/Excluding full nonprogram sample estimates, the total number of recursive estimates was equal to 178 for non-program years observations. Shares of statistically significant t-statistics at the 10 percent level are reported in parentheses.