All SVAR models are estimated by using quarterly data on a country-by-country basis. The country-specific estimations are based on a Bayesian approach with the Minnesota priors proposed by Litterman (1986). For models with recursive identification, a total of 2,000 samples are drawn, and a burn-in sample of 1,000 draws is discarded. For models identified by sign restrictions, we search for 1,000 successful draws (satisfying our sign restrictions) of at least 2,000 iterations with 1,000 burn-ins. In all models, the remaining 1,000 draws are used to determine the cumulative impulse responses and forecast error variance decompositions for each country as well as for each country group. For example, when empirical results are presented for oil exporters as a group, 1,000 draws coming from all oil exporting countries are pooled together, and the corresponding measures (e.g., median or interquartile range) are calculated out of this pool. Following Fry and Pagan (2005, 2011), rather than directly calculating the corresponding measures (e.g., median, or interquartile range) across all draws for a particular horizon, we search for the draw that produces impulses closest to these measures by minimizing the distance between a measure and the selected draw for a horizon of five years.

# **3.1. Real oil price shocks and current account balances**

The purpose of this investigation is to estimate the effects of real oil price shocks on current account balances, where recursive identification is used. **Benchmark model.** In the benchmark model, consists of year-on-year change in log real crude oil prices (global variable), year-on-year change in log real GDP (country-specific variable), and current account tt −1 balance as percentage of GDP (country-specific variable). The recursive structure imposed on zz 0 requires an ordering of the variables used in the estimation. Accordingly, real crude oil prices growth is ordered first as oil prices are mainly determined in the global economy. Real GDP growth is ordered second as it AA represents country-specific developments that can have an immediate impact on country-specific current account balances, but they cannot have an immediate impact on globally determined real crude oil prices. Current account balance is ordered last because it is not only the variable of interest but also can be affected immediately by changes in globally-determined crude oil prices or country-specific real GDP; however, the current account balance does not have an immediate impact on global real crude oil prices or real GDP. All variables can impact other variables after one quarter. Based on data availability, this model is estimated individually for 45 EMDEs by using quarterly data.<sup>4</sup> **Robustness check.** As a robustness check, we include the country-specific real effective exchange rate as an additional endogenous variable. Accordingly, in this model, consists of year-on-year changes in log real crude oil prices (global variable), log real GDP (country-specific variable), current account balance as tt percentage of GDP (country-specific variable), and log real effective exchange rate (country-specific zz variable). We follow a similar ordering of variables as in the benchmark model, with the real effective exchange rate ordered last since it can be affected immediately by any changes in the other variables. All variables can impact other variables after one quarter. As data for real effective exchange rates are not available for certain countries, this model is estimated individually for 29 EMDEs by using quarterly data.

# **3.2. Alternative real energy price shocks and current account balances**

The purpose of this investigation is to provide a richer understanding of the effects of energy price shocks by incorporating the prices of alternative source of energy (oil, natural gas, and coal) on current account balances, where recursive identification is used.

> 4 See Section 3.4 for a detailed description of the data.

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