Search

Content Types

Research Topics

JEL Codes

Locations

Departments

Authors

Sources

Statuses

Published After

Published Before

132 Results

Tail Index Estimation: Quantile-Driven Threshold Selection

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.

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.

Monetary Policy Uncertainty: A Tale of Two Tails

Staff Working Paper 2018-50 Tatjana Dahlhaus, Tatevik Sekhposyan
We document a strong asymmetry in the evolution of federal funds rate expectations and map this observed asymmetry into measures of monetary policy uncertainty. We show that periods of monetary policy tightening and easing are distinctly related to downside (policy rate is higher than expected) and upside (policy rate is lower than expected) uncertainty.

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 Research Topic(s): Financial stability JEL Code(s): C, C0, C01, C1, C14, C5, C58

A Look Inside the Box: Combining Aggregate and Marginal Distributions to Identify Joint Distributions

Staff Working Paper 2018-29 Marie-Hélène Felt
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.

Bitcoin Awareness and Usage in Canada: An Update

Staff Analytical Note 2018-23 Christopher Henry, Kim Huynh, Gradon Nicholls
The results of our 2017 Bitcoin Omnibus Survey (December 12 to 15, 2017) when compared with those from 2016 show that Bitcoin “awareness” increased from 64 to 85 per cent, while ownership increased from 2.9 to 5.0 per cent. Most Bitcoin purchasers are using the cryptocurrency as an investment and not as a means of payment for goods or services.

Bootstrapping Mean Squared Errors of Robust Small-Area Estimators: Application to the Method-of-Payments Data

Staff Working Paper 2018-28 Valéry Dongmo Jiongo, Pierre Nguimkeu
This paper proposes a new bootstrap procedure for mean squared errors of robust small-area estimators. We formally prove the asymptotic validity of the proposed bootstrap method and examine its finite sample performance through Monte Carlo simulations.
Content Type(s): Staff research, Staff working papers Research Topic(s): Bank notes, Econometric and statistical methods JEL Code(s): C, C1, C13, C15, C8, C83, E, E4, E41

On the Evolution of the United Kingdom Price Distributions

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).
Content Type(s): Staff research, Staff working papers Research Topic(s): Econometric and statistical methods, Inflation and prices JEL Code(s): C, C1, C14, C8, C83, E, E3, E31, E37

Noisy Monetary Policy

Staff Working Paper 2018-23 Tatjana Dahlhaus, Luca Gambetti
We introduce limited information in monetary policy. Agents receive signals from the central bank revealing new information (“news") about the future evolution of the policy rate before changes in the rate actually take place. However, the signal is disturbed by noise.

State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models

Staff Working Paper 2018-14 Luis Uzeda
Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood.
Go To Page