Felipe Leal

In a small economy, with complete markets and domestic price stickiness, a monetary policy rule that reacts to domestic inflation implements the efficient allocation, as long as it also reacts to the natural rate of interest. In this case, a policy response to the exchange rate or to foreign variables is inefficient. We show that, when the central bank is unable to observe the natural rate of interest, a domestic inflation targeting rule that also reacts to the real exchange rate can be optimal. This implementable rule stabilizes domestic inflation while simultaneously facilitating efficient adjustments in relative prices (terms of trade), through nominal devaluations. The policy response to the real exchange rate mirrors the optimal policy reaction to the unobservable natural rate of interest. Indeterminacy can arise, but a stronger policy response to domestic inflation can prevent this from happening.

We augment the two-country DSGE model from Mandelman and Zlate (2012) with endogenous migration of both skilled and unskilled labor, and analyze the economic impact of restricting migration for each type of labor on the migrants’ source and host countries. We calibrate the model with data for Guatemala and the U.S. as the source and host countries respectively. Restricting unskilled immigration has a more severe contractionary impact on the host economy than restricting skilled migration—reducing aggregate output by approximately 1 percent and consumption by 0.6 percent compared to the steady state—, given that in the steady state a lot more unskilled migrants are needed in the production function. Restricting unskilled immigration impacts negatively skilled immigration flows, but not vice versa, and it has a significant overall impact on the source economy, with aggregate consumption and income declining (mostly due to a fall in remittances). Our findings also highlight a key policy trade-off for the host economy: while a restrictive immigration policy (especially for unskilled labor) could reduce wage inequality by compressing the skill premium—raising host-country unskilled wages by 8 percent while lowering skilled wages by 1 percent compared to the steady state—, and could increase output per capita, it comes at the cost of lower overall economic activity and aggregate consumption. Moreover, by increasing the wage gap for unskilled labor between host and source economies, it also raises incentives for immigration, despite the increased cost of migration.

We apply Machine Learning (ML) methods with Big Data, similar to Medeiros et al. (2019) for the US, to forecast headline and core inflation of the CPI in Chile. We document that the ML methods do not consistently dominates in the inflation forecast for the Chilean case over simple and univariate linear competitors such as AR, the mean and median of past inflation, which have proven to be highly competitive. In fact, these are the best performing methods in many cases. A second contribution of this work is the construction of a large data set with macroeconomic variables related to the Chilean economy, similar to McCracken and Ng (2016), who built (and maintain) a similar data set for the US.