C14 - Semiparametric and Nonparametric Methods: General - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-28T12:09:41+00:00Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models
https://www.bankofcanada.ca/2022/07/staff-working-paper-2022-31/
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.2022-07-04T11:20:56+00:00enComparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models2022-07-04Credit risk managementEconometric and statistical methodsStaff Working Paper 2022-31https://www.bankofcanada.ca/wp-content/uploads/2022/07/swp2022-31.pdfComparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response ModelsXiangjin ShenIskander KaribzhanovHiroki TsurumiShiliang LiJuly 2022CC1C14C3C35C5C51C6C63DD1Covariates 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 2021CC0C01C1C14C5C58Maturity Composition and the Demand for Government Debt
https://www.bankofcanada.ca/2020/07/staff-working-paper-2020-29/
The main objectives of debt management are to raise stable and low-cost funding to meet the government’s financial needs and to maintain a well-functioning market for government securities.2020-07-08T09:46:24+00:00enMaturity Composition and the Demand for Government Debt2020-07-08Debt managementFinancial marketsStaff Working Paper 2020-29https://www.bankofcanada.ca/wp-content/uploads/2020/07/swp2020-29.pdfStaff Working Paper 2020-29Jason AllenJakub KastlMilena WittwerJuly 2020CC1C14DD4D44EE5E58GG1G12Identifying Consumer-Welfare Changes when Online Search Platforms Change Their List of Search Results
https://www.bankofcanada.ca/2020/03/staff-working-paper-2020-5/
Online shopping is often guided by search platforms. Consumers type keywords into query boxes, and search platforms deliver a list of products. Consumers' attention is limited, and exhaustive searches are often impractical.2020-03-06T10:32:58+00:00enIdentifying Consumer-Welfare Changes when Online Search Platforms Change Their List of Search Results2020-03-06Econometric and statistical methodsMarket structure and pricingStaff Working Paper 2020-5https://www.bankofcanada.ca/wp-content/uploads/2020/03/swp2020-5.pdfStaff Working Paper 2020-5Ryan MartinMarch 2020CC1C14DD1D11D12D6D8D83LL4L40Extreme Downside Risk in Asset Returns
https://www.bankofcanada.ca/2019/12/staff-working-paper-2019-46/
Financial markets can experience sudden and extreme downward movements. Investors are highly concerned about the performance of their assets in such scenarios. Some assets perform badly in a downturn in the market; others have milder reactions.2019-12-06T09:33:41+00:00enExtreme Downside Risk in Asset Returns2019-12-06Asset pricingEconometric and statistical methodsStaff Working Paper 2019-46https://www.bankofcanada.ca/wp-content/uploads/2019/12/SWP2019-46.pdfExtreme Downside Risk in Asset ReturnsLerby ErgunDecember 2019CC1C14GG1G11G12Tail 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 2018CC0C01C1C14C5C58A Look Inside the Box: Combining Aggregate and Marginal Distributions to Identify Joint Distributions
https://www.bankofcanada.ca/2018/07/staff-working-paper-2018-29/
This paper proposes a method for estimating the joint distribution of two or more variables when only their marginal distributions and the distribution of their aggregates are observed. Nonparametric identification is achieved by modelling dependence using a latent common-factor structure.2018-07-04T10:50:38+00:00enA Look Inside the Box: Combining Aggregate and Marginal Distributions to Identify Joint Distributions2018-07-04Bank notesDigital currencies and fintechEconometric and statistical methodsStaff Working Paper 2018-29https://www.bankofcanada.ca/wp-content/uploads/2018/07/swp2018-29.pdfA Look Inside the Box: Combining Aggregate and Marginal Distributions to Identify Joint DistributionsMarie-Hélène FeltJuly 2018CC1C14DD1D14EE4E41On the Evolution of the United Kingdom Price Distributions
https://www.bankofcanada.ca/2018/06/staff-working-paper-2018-25/
We propose a functional principal components method that accounts for stratified random sample weighting and time dependence in the observations to understand the evolution of distributions of monthly micro-level consumer prices for the United Kingdom (UK).2018-06-21T11:01:35+00:00enOn the Evolution of the United Kingdom Price Distributions2018-06-21Econometric and statistical methodsInflation and pricesStaff Working Paper 2018-25https://www.bankofcanada.ca/wp-content/uploads/2018/06/swp2018-25.pdfOn the Evolution of the United Kingdom Price DistributionsBa M. ChuKim HuynhDavid T. Jacho-ChávezOleksiy KryvtsovJune 2018CC1C14C8C83EE3E31E37