Bio

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.

Research Field(s): Applied Econometrics Econometric Theory Time Series
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Staff working papers

Sectoral Uncertainty

Staff Working Paper 2022-38 Efrem Castelnuovo, Kerem Tuzcuoglu, Luis Uzeda
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.
Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Monetary policy and uncertainty JEL Code(s): C, C5, C51, C55, E, E3, E32, E4, E44

International Transmission of Quantitative Easing Policies: Evidence from Canada

Staff Working Paper 2022-30 Serdar Kabaca, Kerem Tuzcuoglu
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.
Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, International topics, Monetary policy transmission JEL Code(s): E, E5, E52, F, F4, F41, F44

Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects

Staff Working Paper 2019-16 Kerem Tuzcuoglu
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.
Content Type(s): Staff research, Staff working papers Topic(s): Credit risk management, Econometric and statistical methods, Economic models JEL Code(s): C, C2, C23, C25, C5, C58, G, G2, G24

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Journal publications

Others

Working papers

  • Composite Likelihood Estimation of AR-Probit Model: Application to Credit Ratings
  • Output Effects of Global Food Commodity Shocks
  • Interpreting the latent dynamic factors by threshold FAVAR model