There is growing evidence that deep recessions may have a permanent and negative impact on both the level and growth rate of the actual and potential GDP of developed economies in the medium and long term. We study the growth rate of the potential GDP of Mexico, a middle-income economy, using a modified version of the methodology proposed by Ball (2014) that employs robust time-series techniques to identify shifts and accounts for a diminishing growth rate caused by secular forces rather than crises. We find evidence in favour of the growth rate being stable around a changing mean. On the one hand, the 1982 debt crisis and 2000 recession coincide with structural changes in the Mexican economy that had a lasting impact (on growth) and permanently lowered potential GDP levels, i.e., strong evidence in favour of the hysteresis hypothesis. On the other hand, we find no significant damaging effect (on potential GDP) of either the 1995 financial crisis or the Great Recession.

We analyze the business cycle co-movement between Mexico and the US. We identify two shocks affecting US aggregate supply, three affecting its demand, and two types of monetary policy surprises with different financial implications. US shocks explain about 75% of expected output fluctuations in Mexico at a three-year horizon, with US demand shocks driving half of these variations alone. In turn, Mexican output responses to a monetary policy surprise in the US depend on the reaction of investors’ sentiment to said surprise. Finally, for the sample period studied, financial-market interconnections are as important as goods-demand linkages for the international transmission of US shocks.

This paper employs a proxy structural vector autoregressive (SVAR) model to examine the Federal Reserve's response to global supply chain shocks and their aggregate propagation under two counterfactual policy rules. Historically, the Fed has overlooked initial price surges, presumably favouring output, before adjusting its policy in response to persistent inflation. Stabilising inflation would entail an initial tightening and a mild recession, but ultimately more stable inflation expectations. Conversely, minimising a dual-mandate loss function—whether through inflation targeting (IT) or average inflation targeting (AIT)—calls for a more accommodative initial policy followed by greater subsequent tightening compared to the actual policy rule. The AIT policy rule, prioritising output and tolerating higher inflation more than the IT policy rule, would require a looser policy stance. However, prices being more sensitive than output to GSC disruptions eventually led to a much more contractionary policy under the AIT policy rule, worsening the inflation-output trade-off.

By means of a detailed empirical application, we show that highly disaggregated administrative data can be very useful for macroeconomic analysis and monetary policy design. We propose a novel methodology to track inflation dynamics by identifying supply and demand shocks at a highly disaggregated level using prices and quantities information from electronic payments data. We estimate SVAR models where supply and demand shocks are identified with sign restrictions. These estimates are then used to group products into categories of CPI inflation. As opposed to similar studies using categorical-level regressions (e.g., Shapiro, 2022), supply and demand shocks may coexist at a given point in time for a particular category, providing a much richer environment for the policymaker. We apply this strategy to the Chilean case, and our decomposition provides a reasonable narrative to explain the dynamics of inflation since the start of the COVID-19 pandemic and thereafter. The decomposition of headline inflation obtained by adding up the disaggregates is validated with alternative strategies. 

A number of studies document that a single global (mean) factor tracks the level and persistence of inflation rates reasonably well in advanced economies. However, little is known about their higher-order co-movement, despite being relevant from a policy viewpoint and for forecasting. Using quantile factor models, I construct measures of dispersion and skewness to characterize the conditional distribution of global inflation. I document that these measures evolve over time and are complementary to the global mean factor. I also provide evidence of predictive gains from these measures to point and upper-tail forecasts of domestic inflation, but only during periods of high and volatile inflation. Therefore, higher moments of global inflation may serve as a useful complement to existing inflation risk indicators, particularly at the current juncture.

Presented at: MMF PhD Conference 2023, Oxford/Warwick Macroeconomics Workshop, IAAE 2023, MMF Society Conference 2023, Real-Time Economics Conference 2023.

This paper studies the spillovers of US Trade Policy Uncertainty (TPU) shocks on a number of macroeconomic and financial variables in 36 advanced and emerging economies. For identification, I use narrative sign restrictions based on stock market jumps caused by news about future TPU and show that they are highly informative to gauge the effects of TPU shocks. Evidence from panel local projection methods shows that these shocks reduce industrial production, trade, and stock prices, in all countries in the sample, and lead to a depreciation of the exchange rate on average. Consumer prices increase in emerging economies as opposed to advanced ones. Finally, I document a stronger impact on stock markets than on macroeconomic variables both in the US and abroad. 

Presented at: LACEA-LAMES 2021, RES 2022, MMF PhD Conference 2022, IAAE 2022, Macroeconomics Workshop at University of Warwick, 2nd Quick Talks on Macroeconometrics Workshop at King’s College London, 4th Finance and Economics Workshop at Queen Mary University, and Workshop on Financial Econometrics at Lancaster University.