business cycle volatility in developing countries. This may be even more so in smaller economies for several reasons: one, these economies tend to be less diversified in terms of their production and exports and thus more exposed to fluctuations in the world prices of a few goods or commodities; two, they are typically more dependent on imports of intermediate inputs and capital goods; and three, smaller economies tend to be more open to trade.

The existing literature has investigated the role played by terms of trade shocks in developed and developing countries extensively. For instance, Mendoza (1995) shows in a three-sector intertemporal model that terms of trade shocks contribute about 50% to GDP volatility in developed and developing countries. Kose and Riezman (2001) also show that fluctuations in international relative prices explain 44% of the output volatility in Africa. Kose (2002) breaks the import prices into the price of imported capital and the price of imported inputs and shows an even larger contribution of the terms of trade shocks, finding that the world price shocks are responsible for more than 80% of output fluctuations in a representative developing economy.

The estimates obtained in these papers are generally based on the analysis of calibrated business-cycle models. On the other hand, Lubik and Teo (2005) report a significantly smaller contribution of terms of trade shocks to business cycles fluctuation in a sample of 3 developed and 2 developing countries. In fact, they find that interest rate shocks are a more important source of business cycles than terms of trade shocks. Their analysis relies on estimating a small open economy model with full information Bayesian method. In the same spirit, Schmitt-Grohe and Uribe (2017), using a structural vector autoregression (SVAR) approach, find that terms of trade shocks explain just 10% of movement in the aggregate activity in a sample of 38 poor and emerging market countries at annual frequency. We contribute to this debate by studying a sample of developed and developing countries, at quarterly frequency. We also condition the analysis on country size and the presence of fiscal rules.

To provide an empirical assessment of the importance of various shocks discussed above for aggregate fluctuations, we begin by estimating a structural vector autoregression (SVAR) model country by country. Our empirical model has the following form: where �� is a vector of domestic and external variables; and _A_ and _B_ are parameters matrices. The � � � � vector �� consists of two blocks of variables. A foreign block is ��� , �� �′ or � ����, �� , �� �′. Here _tot_ is the terms of trade, �<sup>�</sup> is the world interest rate measured as the real (3-month) T-bill rate in the US, and �<sup>�</sup> if the foreign demand measured as the US real GDP. The domestic block is composed of ���, ����, ��, ��, �����′, where _g_ is real government consumption expenditures, _tby –_ real trade balance to GDP ratio, _r_ is the real domestic interest rate, _y_ is real GDP, and _rer_ is the real exchange rate.<sup>1</sup> The variables to be included in the SVAR are chosen to capture the factors

> 1 If trade balance data were not available, we used current account.

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