Appendix I. Technical Appendix
Adolfson, Malin, et al., 2007, “Bayesian Estimation of an Open Economy DSGE Model with Incomplete Pass-Through,” Journal of International Economics, Vol. 72(2), pp. 481–511.
Christiano, Lawrence J., Martin Eichenbaum, and Charles L. Evans, 1999, “Monetary Policy Shocks: What have we Learned and to what end?”, Handbook of Macroeconomics, Vol. 1, pp. 65–148.
Kilian, Lutz, 2009, “Not all Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market,” American Economic Review, Vol. 99(3), pp. 1053–69.
Levy Yeyati, Eduardo, and Tomas Williams, 2012, “Emerging Economies in the 2000s: Real Decoupling and Financial Recoupling,” Journal of International Money and Finance, Elsevier, Vol. 31(8), pp. 2102–26.
Smets, Frank, and Rafael Wouters, 2007, “Shocks and Frictions in U.S. Business Cycles: A Bayesian DSGE Approach,” American Economic Review Vol. 97(3), pp. 586–606.
de la Torre, Augusto, Eduardo Levy Yeyati, and Samuel Pienknagura, 2013, “Latin America and the Caribbean as Tailwinds Recede: In Search of Higher Growth,” LAC Semiannual Report, World Bank, Washington, DC.
Villani, Mattias, 2009, “Steady-State Priors for Vector Autoregressions,” Journal of Applied Econometrics Vol. 24.4, pp. 630–50.
Prepared by Andrea Pescatori and Martin Sasson.
Commodities are defined as the sum of mining, agriculture and silviculture, and fishing. Their 2017 shares of exports are 54.9 and 43.9, respectively, not dissimilar to their 2003–2017 average. The value of copper exports as share of GDP peaked at 25 percent in 2007.
The domestic power sector is the largest copper consumer in China, due to investment in power network upgrades and rural power network renovation. The residential construction sector is also a key driver of copper demand. The relevance of China, however, has clearly increased over time. In the empirical analysis we, thus, weight China GDP growth by its share of world trade (see Figure 3).
The BVAR, for example, does not attempt to distinguish between domestic supply and demand shocks. Partial identification strategies have a long-standing tradition in the VAR literature, one of the first examples is in Christiano and others (1999).
All variables are in log-differences but for the 3-month interest rate, domestic policy uncertainty, the fiscal balance- to-GDP, and credit-to-GDP which are introduced untransformed in levels, while the real effective exchange rate is in logs. For robustness, in the BVAR, we have also introduced the fiscal balance over GDP (in levels), replaced domestic policy uncertainty with credit-to-GDP (in levels), replaced U.S. GDP with G7 GDP (in PPP US$), and the copper price with the World Bank’s commodity price index. Results are qualitatively unchanged and quantitatively very similar.
Chile’s data are from Banco Central de Chile, copper price data are from Bloomberg and divided by U.S. CPI, China and U.S. data are from HAVER. The domestic policy uncertainty index comes from the Economic Policy Uncertainty website: http://www.policyuncertainty.com/chile_monthly.html.
Given the large volatility of q/q growth rates, results are presented as a 4-quarter moving average of the q/q growth rates employed in the BVAR. This also eases the comparability with the other methodology that adopts y/y growth rates.
As standard in the literature (see Kilian 2007, for example), we assume the first point to be the VAR steady state.