C01 - Econometrics - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-28T22:16:49+00:00Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models
https://www.bankofcanada.ca/2022/09/staff-discussion-paper-2022-19/
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.2022-09-20T11:00:03+00:00enCalculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models2022-09-20Econometric and statistical methodsStaff Discussion Paper 2022-19https://www.bankofcanada.ca/wp-content/uploads/2022/09/sdp2022-19.pdfCalculating Effective Degrees of Freedom for Forecast Combinations and Ensemble ModelsJames YounkerSeptember 2022CC0C01C02C1C13C5C50C51C52C53Covariates Hiding in the Tails
https://www.bankofcanada.ca/2021/09/staff-working-paper-2021-45/
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.2021-09-29T14:11:22+00:00enCovariates Hiding in the Tails2021-09-29Econometric and statistical methodsStaff Working Paper 2021-45https://www.bankofcanada.ca/wp-content/uploads/2021/09/swp2021-45.pdfStaff Working Paper 2021-45Milian BachemLerby ErgunCasper G. de VriesSeptember 2021CC0C01C1C14C5C58Tail Index Estimation: Quantile-Driven Threshold Selection
https://www.bankofcanada.ca/2019/08/staff-working-paper-2019-28/
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.2019-08-02T10:52:22+00:00enTail Index Estimation: Quantile-Driven Threshold Selection2019-08-02Econometric and statistical methodsFinancial stabilityStaff Working Paper 2019-28https://www.bankofcanada.ca/wp-content/uploads/2019/08/swp2019-28.pdfTail Index Estimation: Quantile-Driven Threshold SelectionJon DanielssonLerby ErgunCasper G. de VriesLaurens de HaanAugust 2019CC0C01C1C14C5C58Characterizing the Canadian Financial Cycle with Frequency Filtering Approaches
https://www.bankofcanada.ca/2018/10/staff-analytical-note-2018-34/
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.2018-10-26T12:28:48+00:00enCharacterizing the Canadian Financial Cycle with Frequency Filtering Approaches2018-10-26Challenges in Implementing Worst-Case Analysis
https://www.bankofcanada.ca/2018/09/staff-working-paper-2018-47/
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.2018-09-14T08:50:24+00:00enChallenges in Implementing Worst-Case Analysis2018-09-14Financial stabilityStaff Working Paper 2018-47https://www.bankofcanada.ca/wp-content/uploads/2018/09/swp2018-47.pdfChallenges in Implementing Worst-Case AnalysisJon DanielssonLerby ErgunCasper G. de VriesSeptember 2018CC0C01C1C14C5C58Asymmetric Risks to the Economic Outlook Arising from Financial System Vulnerabilities
https://www.bankofcanada.ca/2018/03/staff-analytical-note-2018-6/
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.2018-03-14T10:15:59+00:00enAsymmetric Risks to the Economic Outlook Arising from Financial System Vulnerabilities2018-03-14Financial Stress, Monetary Policy, and Economic Activity
https://www.bankofcanada.ca/2010/05/working-paper-2010-12/
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.2010-05-21T13:24:59+00:00enFinancial Stress, Monetary Policy, and Economic Activity2010-05-21Financial stabilityMonetary policy and uncertaintyWorking Paper 2010-12https://www.bankofcanada.ca/wp-content/uploads/2010/05/wp10-12.pdfFinancial Stress, Monetary Policy, and Economic ActivityFuchun LiPierre St-AmantMay 2010CC0C01EE5E50GG0G01