depend on the fiscal position such that a given level of the fiscal position maps out to a set of model coeffi cients. For presenting the results, we evaluate model coeffi cients at specific values of the fiscal position which are taken to be the percentiles within the sample. Confidence bands are calculated by bootstrapping over 300 iterations. We report median estimates, along with the 16 - 84 percent bands.

## 2.3 Database

Our main database comprises an unbalanced panel that covers 34 countries (19 advanced and 15 developing), at the quarterly frequency over the period 1980:1 — 2014:1.<sup>13</sup> Real government consumption and real GDP are based on the quarterly database in Ilzetzki, Mendoza, and Vegh (2013) which are extended until 2014:1 by splicing from the OECD and Haver Analytics. Real effective exchange rates are from the narrow (wherever available) and broad indices of the BIS, and current account from the IMF’s WEO database. The short-term rate used for discounting the multiplier is drawn mainly from the IMF’s IFS database. For the robustness results, we augment this database to include quarterly real private consumption and private investment series. These are drawn from the OECD, Haver Analytics, and Eurostat. Additional details on the sources and definitions of all of these variables are provided in Table A2 in the Appendix.

The government consumption and GDP series (as well as private consumption and private investment) are converted into logarithmic form, and detrended using a linear quadratic trend as in Ilzetzki, Mendoza, and Vegh (2013). The exchange rate is transformed into quarter-to-quarter growth rates, and the current account series is seasonally-adjusted using the X11 routine. All these series are detrended and demeaned on a country-by-country basis, which effectively controls for country fixed effects in the regressions.

We also employ another database that is an unbalanced panel with the same cross sectional and time series coverage as before but at the annual frequency. This includes the conditioning variables that are not explicitly required for the identification scheme to be valid in the VAR model but are necessary to estimate the interaction terms. These are government debt and fiscal balances as percentage of GDP which are drawn from the IMF’s WEO (October 2014) database; and government consumption-to-GDP ratios which we obtain from the World Bank’s WDI database.

# 3 Results

## 3.1 Unconditional Multipliers

To establish a benchmark, we first report estimates of the unconditional multiplier from a standard panel SVAR. For that, we suppress the law of motion for the coeffi cients in Equation (2). This renders the coeffi cient matrices Al in Equation (1) invariant across countries and time. Figure 1 presents the unconditional multipliers for the select horizons: on impact, 1 year, 2 years, and long run (5 years). Barring only a few periods in the impulse horizon, the unconditional impulse responses of output due to a positive fiscal shock are either negative or insignificant.<sup>14</sup> Indeed,

> 13 The list of countries is presented in Table A1 in the Appendix. Our developing-country coverage comprises primarily emerging and frontier market economies that have some ability to tap into international financial markets, which renders the fiscal solvency risks that underpin our nonlinear crowding-out mechanisms relevant. We exclude low-income countries not only because of data reliability issues, but also because they primarily rely on concessional finance for government expenditure, which would not reflect the crowding-out mechanisms.

> 14 When we split our sample into advanced and developing economies, our estimates of the unconditional multiplier are very similar to the ones reported in Ilzetzki, Mendoza, and Vegh (2013). See Figure A2 in the Appendix.

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