Bio

Kerem Tuzcuoglu is a Principal Researcher in the Financial Stability Department. His research focuses on theoretical and applied econometrics, nonlinear time series and panel data models, and Bayesian econometrics with applications in macroeconomics, monetary policy, international economics, and finance. He received his Ph.D. in Economics from Columbia University.

Research Field(s): Applied Econometrics Bayesian Methods Business Cycles Econometric Theory Empirical Finance Forecasting International Macroeconomics Macroeconomics Monetary Economics Quantitative Macroeconomics Time Series Unconventional Monetary Policy

Staff working papers

Forecasting Recessions in Canada: An Autoregressive Probit Model Approach

Staff Working Paper 2024-10 Antoine Poulin-Moore, Kerem Tuzcuoglu
We forecast recessions in Canada using an autoregressive (AR) probit model. The results highlight the short-term predictive power of the US economic activity and suggest that financial indicators are reliable predictors of Canadian recessions. In addition, the suggested model meaningfully improves the ability to forecast Canadian recessions, relative to a variety of probit models proposed in the Canadian literature.
Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C5, C51, C53, E, E3, E32

Supply Drivers of US Inflation Since the COVID-19 Pandemic

Staff Working Paper 2023-19 Serdar Kabaca, Kerem Tuzcuoglu
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.
Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Inflation and prices JEL Code(s): C, C3, C32, E, E3, E31, E32

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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Technical reports

Risk Amplification Macro Model (RAMM)

Technical Report No. 123 Kerem Tuzcuoglu
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.
Content Type(s): Staff research, Technical reports Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Financial stability, Monetary policy transmission JEL Code(s): C, C5, C51, E, E3, E37, E4, E44, F, F4, F44

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Journal publications