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189 Results

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.

Forecasting Banks’ Corporate Loan Losses Under Stress: A New Corporate Default Model

Technical Report No. 122 Gabriel Bruneau, Thibaut Duprey, Ruben Hipp
We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.

Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models

Staff Discussion Paper 2022-19 James Younker
This paper derives a calculation for the effective degrees of freedom of a forecast combination under a set of general conditions for linear models. Computing effective degrees of freedom shows that the complexity cost of a forecast combination is driven by the parameters in the weighting scheme and the weighted average of parameters in the auxiliary models.

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.

Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models

We use graphic processing unit computing to compare Bayesian and sample theory semiparametric binary response models. Our findings show that optimal bandwidth does not outperform regular bandwidth in binary semiparametric models.

Nonparametric Identification of Incomplete Information Discrete Games with Non-equilibrium Behaviors

Staff Working Paper 2022-22 Erhao Xie
This paper jointly relaxes two assumptions in the literature that estimates games. These two assumptions are the parametric restriction on the model primitives and the restriction of equilibrium behaviors. Without imposing the above two assumptions, this paper identifies the primitives of the game.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C5, C57

Equilibrium in Two-Sided Markets for Payments: Consumer Awareness and the Welfare Cost of the Interchange Fee

Staff Working Paper 2022-15 Kim Huynh, Gradon Nicholls, Oleksandr Shcherbakov
We construct and estimate a structural two-stage model of equilibrium in a market for payments in order to quantify the network externalities and identify the main determinants of consumer and merchant decisions.

Macroeconomic Predictions Using Payments Data and Machine Learning

Staff Working Paper 2022-10 James Chapman, Ajit Desai
We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.
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