C01 - Econometrics

• 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.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Econometric and statistical methods JEL Code(s): C, C0, C01, C02, C1, C13, C5, C50, C51, C52, C53

• Covariates Hiding in the Tails

  Staff Working Paper 2021-45 Milian Bachem, Lerby Ergun, Casper G. de Vries

  We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C0, C01, C1, C14, C5, C58

• Tail Index Estimation: Quantile-Driven Threshold Selection

  Staff Working Paper 2019-28 Jon Danielsson, Lerby Ergun, Casper G. de Vries, Laurens de Haan

  The most extreme events, such as economic crises, are rare but often have a great impact. It is difficult to precisely determine the likelihood of such events because the sample is small.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial stability JEL Code(s): C, C0, C01, C1, C14, C5, C58

• Characterizing the Canadian Financial Cycle with Frequency Filtering Approaches

  Staff Analytical Note 2018-34 Andrew Lee-Poy

  In this note, I use two multivariate frequency filtering approaches to characterize the Canadian financial cycle by capturing fluctuations in the underlying variables with respect to a long-term trend. The first approach is a dynamically weighted composite, and the second is a stochastic cycle model.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Financial stability, Monetary and financial indicators, Recent economic and financial developments JEL Code(s): C, C0, C01, C1, C13, C14, C18, C3, C32, C5, C51, C52, E, E3, E32, E6, E66, G, G0, G01, G1, G18

• Challenges in Implementing Worst-Case Analysis

  Staff Working Paper 2018-47 Jon Danielsson, Lerby Ergun, Casper G. de Vries

  Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.

  Content Type(s): Staff research, Staff working papers Topic(s): Financial stability JEL Code(s): C, C0, C01, C1, C14, C5, C58

• Asymmetric Risks to the Economic Outlook Arising from Financial System Vulnerabilities

  Staff Analytical Note 2018-6 Thibaut Duprey

  When financial system vulnerabilities are elevated, they can give rise to asymmetric risks to the economic outlook. To illustrate this, I consider the economic outlook presented in the Bank of Canada’s October 2017 Monetary Policy Report in the context of two key financial system vulnerabilities: high levels of household indebtedness and housing market imbalances.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Financial stability, Financial system regulation and policies, Monetary and financial indicators, Monetary policy and uncertainty, Recent economic and financial developments JEL Code(s): C, C0, C01, C1, C11, C15, E, E1, E17, E3, E32, E37, E4, E44, E47, E5, E58, E6, E66, G, G0, G01, G1, G18

• Financial Stress, Monetary Policy, and Economic Activity

  Staff Working Paper 2010-12 Fuchun Li, Pierre St-Amant

  This paper examines empirically the impact of financial stress on the transmission of monetary policy shocks in Canada. The model used is a threshold vector autoregression in which a regime change occurs if financial stress conditions cross a critical threshold.

  Content Type(s): Staff research, Staff working papers Topic(s): Financial stability, Monetary policy and uncertainty JEL Code(s): C, C0, C01, E, E5, E50, G, G0, G01