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2191 Results

Seasonal Adjustment of Weekly Data

Staff discussion paper 2024-17 Jeffrey Mollins, Rachit Lumb
The industry standard for seasonally adjusting data, X-13ARIMA-SEATS, is not suitable for high-frequency data. We summarize and assess several of the most popular seasonal adjustment methods for weekly data given the increased availability and promise of non-traditional data at higher frequencies.
June 23, 2004

Financial System Review - June 2004

Financial System Review - June 2004
This section of the Financial System Review examines the recent performance of the Canadian financial system and the factors, both domestic and international, that are influencing it. In each issue, one or more subjects of particular interest are discussed as highlighted topics.

Strengthening Inflation Targeting: Review and Renewal Processes in Canada and Other Advanced Jurisdictions

Staff discussion paper 2020-7 Robert Amano, Thomas J. Carter, Lawrence L. Schembri
We summarize the review and renewal process at four central banks (Reserve Bank of New Zealand, Bank of England, Sveriges Riksbank and the US Federal Reserve Bank) and compare them with the process at the Bank of Canada, which has been well-established since 2001.

The Neutral Rate in Canada: 2018 Estimates

Staff analytical note 2018-22 Xin Scott Chen, José Dorich
The neutral nominal policy rate serves as a benchmark for assessing the degree of monetary stimulus and provides a medium- to long-run anchor for the policy rate. Since quantitative measures of the neutral rate are subject to considerable uncertainty, Bank staff rely on four different approaches to estimate the Canadian neutral rate.

CANVAS: A Canadian Behavioral Agent-Based Model

The Bank of Canada’s current suite of models faces challenges in addressing network effects that integrate household and firm-level heterogeneity and their behaviours. We develop CANVAS, a Canadian behavioural agent-based model to contribute to the Bank’s next-generation modelling effort. CANVAS improves forecasting performance and expands capacity for model-based scenario analysis.
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