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2170 Results

Anticipated Technology Shocks: A Re‐Evaluation Using Cointegrated Technologies

Staff working paper 2017-11 Joel Wagner
Two approaches have been taken in the literature to evaluate the relative importance of news shocks as a source of business cycle volatility. The first is an empirical approach that performs a structural vector autoregression to assess the relative importance of news shocks, while the second is a structural-model-based approach.

Networking the Yield Curve: Implications for Monetary Policy

We study how different monetary policies affect the yield curve and interact. Our study highlights the importance of the spillover structure across the yield curve for policy-making.

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.

A Dynamic Factor Model for Commodity Prices

Staff analytical note 2017-12 Doga Bilgin, Reinhard Ellwanger
In this note, we present the Commodities Factor Model (CFM), a dynamic factor model for a large cross-section of energy and non-energy commodity prices. The model decomposes price changes in commodities into a common “global” component, a “block” component confined to subgroups of economically related commodities and an idiosyncratic price shock component.

A Reference Guide for the Business Outlook Survey

Staff discussion paper 2020-15 David Amirault, Naveen Rai, Laurent Martin
The Business Outlook Survey (BOS) has become an important part of monetary policy deliberations at the Bank of Canada and is also well known in Canadian policy and financial circles. This paper compiles more than 20 years of experience conducting the BOS and serves as a comprehensive reference manual.

Trading on Long-term Information

Staff working paper 2020-20 Corey Garriott, Ryan Riordan
Investors who trade based on good research are said to be the backbone of stock markets: They conduct research to discover the value of stocks and, through their trading, guide financial prices to reflect true value. What can make their job difficult is that high-speed, short-term traders could use machine learning and other technologies to infer when informed investors are trading.
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