where r denotes the interest rate. We utilize the median short-term rate in the sample for this purpose which is about 7.4 percent.

From (3), the impact multiplier is obtained by setting T = 0 and the long-run multiplier by setting T at an arbitrarily large number, which is taken to be T = 20 (5 years) in our exercise. At T = 20, impulse responses in our model by and large revert to their unconditional means, and so we take this to be representative of the long run. In addition, we specifically report multipliers corresponding to one-year (T = 4) and 2-year (T = 8) horizons, when the fiscal multipliers typically peak. To calculate the fiscal multiplier using the coeffi cient estimates from the IPVAR, we first cumulate the discounted impulses of output and government consumption at different horizons and compute the ratio of the two impulses. That ratio is then multiplied by the average government consumption to GDP ratio in the sample to yield multipliers.<sup>9</sup>

# 2.2 and Estimation

To identify fiscal shocks, we rely on the standard recursive identification scheme of Blanchard and Perotti (2002). The key timing assumption in this scheme is that discretionary fiscal policy does not respond to macroeconomic conditions within the quarter.<sup>10</sup> Such a timing assumption can be motivated by implementation lags typically associated with discretionary fiscal policy. In the VAR model, this timing assumption is achieved by ordering government consumption first in Equation (1), before GDP. The timing assumption for the remaining variables in the VAR follows Ilzetzki, Mendoza, and Vegh (2013): the current account is ordered before the real effective exchange rates. This ordering implies that GDP does not respond to the current account within one quarter, and that the current account does not respond within one quarter when the real effective exchange rate moves. The precise ordering of the latter two variables is, however, immaterial for our main results. Of course, there are alternative identification schemes used in the literature. For instance, Romer and Romer (2010) use a narrative approach to identify exogenous fiscal shocks for the US. Auerbach and Gorodnichenko (2012a) proxy exogenous fiscal shocks by forecast errors of government consumption for OECD countries. Due to data limitations, neither of these approaches is feasible for our sample that includes developing countries.<sup>11</sup> The IPVAR system, comprising Equations (1) and (2), is estimated with ordinary least squares (OLS) applied separately to each equation.<sup>12</sup> The estimated system yields model coeffi cients that

> 9 This step to calculate multipliers from impulse responses follows Ilzetzki, Mendoza, and Vegh (2013). Since the conditional multipliers are estimated from the panel of countries, they reflect an average estimate across those countries included in the panel. Thus, we use the average government consumption to GDP ratio in the sample to calculate the multipliers rather than country-specific government consumption to GDP ratios.

> 10 One caveat of the recursive identification scheme is that the fiscal shocks identified using this scheme may be predicted by private forecasts (Ramey, 2011). Ilzetzki, Mendoza, and Vegh (2013), who use a similar identification scheme and sample of countries like ours, provide evidence that this is unlikely the case.

> 11 There are alternative identification schemes used in the literature. For instance, Romer and Romer (2010) use a narrative approach to identify exogenous fiscal shocks for the US. Auerbach and Gorodnichenko (2012a) proxy exogenous fiscal shocks by forecast errors of government consumption for OECD countries. Due to data limitations, these approaches are not feasible for our sample that includes developing countries.

> 12 Because the error terms are uncorrelated across equations by construction, estimating the IPVAR equation by equation does not result in loss of effi ciency. See Towbin and Weber (2013) for a discussion.

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