Kerem Tuzcuoglu is a Senior Economist in the Financial Stability Department. His research focuses on theoretical and applied econometrics, nonlinear time series models, and Bayesian econometrics with the applications on macroeconomics and finance. He received his Ph.D. in Economics from Columbia University.
This paper examines the contribution of several supply factors to US headline inflation since the start of the COVID-19 pandemic. We identify six supply shocks using a structural VAR model: labor supply, labor productivity, global supply chain, oil price, price mark-up and wage mark-up shocks.
We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a disaggregated industrial production series for the US economy. We identify unexpected changes in durable goods uncertainty as drivers of downturns, while unexpected hikes in non-durable goods uncertainty are expansionary.
This paper examines the cross-border spillovers from major economies’ quantitative easing (QE) policies to their trading partners. We concentrate on spillovers from the US to Canada during the zero lower bound period when QE policies were actively used.
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.
The Risk Amplification Macro Model (RAMM) is a new nonlinear two-country dynamic model that captures rare but severe adverse shocks. The RAMM can be used to assess the financial stability implications of both domestic and foreign-originated risk scenarios.