The impact of commodity price shocks in a copper-rich economy: the case of Chile

Abstract

The present paper analyzes how shocks to the copper price affect the economy of Chile, the world’s largest producer of this metal. Chile is an interesting case study, it being an emerging commodity producing economy that has a fiscal rule, which explicitly takes into account the expected future copper price, and has adopted an inflation targeting monetary policy with a fully floating exchange rate. The empirical analysis consists in estimating structural vector autoregressive models where the shocks are identified by sign restrictions in order to make the distinctions of those caused by increasing world demand, decreasing copper supply, and specific copper demand, e.g., speculation in future price increases. Positive copper price shocks, independently of the source, result in an appreciated currency, whereas the effects on the other macroeconomic variables do depend on the source of the shock. While demand shocks affect the Chilean gross domestic product positively, this is not the case when the copper price increases because of a supply-side event or a specific copper demand. Particularly, the activity in the mining sector is affected, while the non-mining sector expands only when the shock is caused by increasing world demand. One explanation could be the stabilizing effect of the fiscal rule. The impacts on inflation and the interest rate are correlated because of the inflation targeting monetary policy.

Appendices

Appendix A: MT impulse response function

The MT impulse response functions of Fry and Pagan (2011) corresponds to the impulse response function of the individual structural model, out of the 1000 estimated, which is element-wise closest to the median. In the present context, the MT impulse response functions are defined by optimizing with respect to the responses of the local variables from impulses to the global ones, i.e., responses of four variables from three shocks, for 20 quarters. Let $\hat{\varTheta }_{q,n}^{{\left( {r,s} \right)}}$ be the response of variable r = 1, …, 4 for the quarter q = 1, …, 20 to the shock s = 1, 2, 3 of the successful draw n = 1, …, 1000, and let $\hat{\varTheta }_{{q,{\text{med}}}}^{{\left( {r,s} \right)}}$ denote the median response of variable r for quarter q from a shock s. The MT impulse response is calculated as:

$$ \hat{\varTheta }^{\text{MT}} = {\arg \min}_{n = 1, \ldots ,1000} \mathop \sum \limits_{r = 1}^{4} \mathop \sum \limits_{s = 1}^{3} \frac{1}{{\hat{V}_{r,s} }}\mathop \sum \limits_{j = 1}^{20} \left( {\hat{\varTheta }_{j,n}^{{\left( {r,s} \right)}} - \hat{\varTheta }_{j,n}^{{\left( {r,s} \right)}} } \right)^{2} , $$

where the measure of variability of the set of successful impulse responses for variable r and shock s is calculated as $\hat{V}_{r,s} = \mathop {\hbox{max} }\nolimits_{{q \in \{ 1, \ldots ,20]}} \frac{1}{1000}\mathop \sum \nolimits_{n = 1}^{1000} \left( {\hat{\varTheta }_{q,n}^{{\left( {r,s} \right)}} - \left( {\frac{1}{1000}\mathop \sum \nolimits_{n = 1}^{1000} \hat{\varTheta }_{q,n}^{{\left( {r,s} \right)}} } \right)} \right)^{2}$.³⁵

Appendix B: Preliminary tests

Table 5 Misspecification tests (p values)

Table 6 Trace tests (p values)

Appendix C: Robustness analyses

Table 7 Robustness analyses. Supply shock. Accumulated effect of a 10% copper price shock (median, %)

Table 8 Robustness analyses. Demand shock. Accumulated effect of a 10% copper price shock (median, %)

Table 9 Robustness analyses. Specific copper demand shock. Accumulated effect of a 10% copper price shock (median, %)