We employ six monthly macroeconomic and financial variables in the SVAR model, ordered as follows: logs of seasonally adjusted industrial production ( _Y_ , 'output' hereafter), logs of seasonally adjusted consumer price index ( _P_ , ‘price’), domestic policy interest rates ( _MP_ ), overnight interest rates or 3-month or one-year government bond yields ( _Int_ ), logs of the nominal exchange rate relative to the US dollar ( _FX_ ), and the equity price index ( _S_ ).<sup>9</sup> We also include four exogenous variables to account for latent factors that can simultaneously affect endogenous variables: the international commodity price index ( _Cmdt_ ), U.S. federal funds rates ( _FFR_ ), the CBOE volatility index ( _VIX_ ), and a deterministic trend component ( _Trend_ ).<sup>10</sup> These data are sourced from Haver Analytics, national central banks, or Federal Reserve Economic Data (FRED).

A novel aspect of this paper is the use of survey-based expectations for future inflation, output growth, FX rates, and interest rates. Consumer and business expectations are sourced from monthly surveys conducted by national central banks or statistical offices, while professional forecasters’ expectations come from Consensus Economics surveys.<sup>11</sup> The availability of expectations data varies across EMEs. As a baseline, we prioritize consumer or business expectations, which tend to show greater variability, and supplement with professional forecasters’ surveys when national consumer and business surveys are unavailable or too short for some EMEs. In the SVAR model, we incorporate one-year-ahead expectations.<sup>12</sup>

# **III.2 Identification and estimation**

The baseline identification follows the standard recursive scheme using the Cholesky decomposition of the variance-covariance matrix, as in Christiano et al. (1999) and many others. The monetary policy instrument (i.e., policy interest rates) is placed after the output and price variables, in line with the Taylor-rule type monetary policy reaction function, and before financial variables—interest rates, exchange rates, and stock price—to reflect the transmission channel of monetary policy shocks through financial markets. Despite using monthly data, endogeneity issues may still persist. To address this, our model includes several exogenous variables (SVAR-X) that account for global demand and supply shocks, capturing the simultaneous movements of variables in small open economies. In addition, the main model is augmented with economic and financial forecasts by consumers, businesses, and professional forecasters.

We first estimate the SVAR models for each country using a Bayesian approach, where the routine aims to find 1,000 successful draws from at least 2,000 iterations with 1,000 burn-ins. The impulse response functions for each individual country are derived as the medians of these

> 9 These are the endogenous variables typically included in VAR models of monetary policy transmission (Rey 2015; 2016, Passari and Rey 2015, Bjørnland 2009). Summary statistics are shown in Table A1.

> 10 While our main interest in this paper is in the transmission of domestic monetary policy shocks into EMEs, we also summarize the results on the transmission of U.S. monetary policy shocks in Appendix III.

> 11 See Table A2 in appendix I for further details.

> 12 For instance, Consensus surveys report annual growth rates or expected inflation for the current and the next year on a monthly basis. We obtain one-year-ahead inflation expectation as a linear combination of the inflation expectations for the current (Eπ�) and next years (Eπ���) . Specifically, we calculate it as Eπ����� = (12-m)/12* Eπ� +m/12* Eπ��� , where m represents the number of the month.

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