Publications
Analyzing Recent Price Anomalies in Argentina: Global Influences and Domestic Distortions
with Emiliano Basco, Emilio Blanco, and Luis Libonatti
September 2025 – Latin American Journal of Central Banking
Abstract: This study investigates Argentina’s unusually high and persistent goods inflation in the aftermath of the COVID-19 pandemic, using a cross-country monthly panel and a model that decomposes inflation into observable marginal costs, global price pass-through, and changing markups. We find that, at their peak in January 2024, goods price markups were approximately 40% higher than in November 2011. Further analysis indicates that these elevated markups were primarily driven by distortionary policy interventions—particularly foreign exchange controls, non-tariff barriers, and complex import regulations—which disrupted market pricing mechanisms and significantly amplified inflation, positioning Argentina as a clear outlier in the global inflation cycle.
Working Papers
The Economic Impact of Low- and High- Frequency Temperature Changes
with Nikolay Gospodinov and Serena Ng
December 2025
Abstract: Temperature variations at different frequencies may have distinct impacts on economic outcomes. We first explore ways to estimate the low- and high-frequency components in a U.S. panel of 48 states. All methods suggest slowly evolving low-frequency components of temperature at the state level, and that they share a common factor which covaries with the low-frequency component of economic activity. While we fail to find a statistically significant impact of low-frequency temperature changes on U.S. growth, an international panel of 50 countries suggests that a 1°C increase in the low-frequency component will reduce economic growth by about one percent in the long run. The linear effect of the high-frequency component is not well determined in all panels, but there is evidence of a non-linear effect in the international panel. The findings are corroborated by time series estimation using data at the unit and national levels. Our empirical work pays attention to distortions that may arise from using one-way clustered errors for inference, and to the possible inadequacy of the additive fixed effect specification in controlling for common time effects.
A Jackknife Variance Estimator for_Panel Regressions
with Richard K. Crump and Nikolay Gospodinov
October 2024 – FRBNY Staff Reports
Abstract: We introduce a new jackknife variance estimator for panel-data regressions. Our variance estimator can be motivated as the conventional leave-one-out jackknife variance estimator on a transformed space of the regressors and residuals using orthonormal trigonometric basis functions. We prove the asymptotic validity of our variance estimator and demonstrate desirable finite-sample properties in a series of simulation experiments. We also illustrate how our method can be used for jackknife bias-correction in a variety of time-series settings.
A Simple Diagnostic for Time Series and Panel-Data Regressions
with Richard K. Crump and Nikolay Gospodinov
October 2024 – FRBNY Staff Reports
Abstract: We introduce a new regression diagnostic, tailored to time-series and panel-data regressions, which characterizes the sensitivity of the OLS estimate to distinct time-series variation at different frequencies. The diagnostic is built on the novel result that the eigenvectors of a random walk asymptotically orthogonalize a wide variety of time-series processes. Our diagnostic is based on leave-one-out OLS estimation on transformed variables using these eigenvectors. We illustrate how our diagnostic allows applied researchers to scrutinize regression results and probe for underlying fragility of the sample OLS estimate. We demonstrate the utility of our approach using a variety of empirical applications.
The Nonlinear Case Against Leaning Against the Wind
with Nina Boyarchenko, Richard K. Crump, Keshav Dogra, and Leonardo Elias
May 2024 – FRBNY Staff Reports
Abstract: We re-examine the relationship between monetary policy and financial stability in a setting that allows for nonlinear, time-varying relationships between monetary policy, financial stability, and macroeconomic outcomes. Using novel machine-learning techniques, we estimate a flexible “nonlinear VAR” for the stance of monetary policy, real activity, inflation, and financial conditions, and evaluate counterfactual evolutions of downside risk to real activity under alternative monetary policy paths. We find that a tighter path of monetary policy in 2003-05 would have increased the risk of adverse real outcomes three to four years ahead, especially if the tightening had been large or rapid. This suggests that there is limited evidence to support “leaning against the wind” even once one allows for rich nonlinearities, intertemporal dependence, and crisis predictability.