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2143 Results

November 17, 2011

Extracting Information from the Business Outlook Survey: A Principal-Component Approach

This article reviews recent work that uses principal-component analysis to extract information common to indicators from the Bank of Canada’s Business Outlook Survey (BOS). The authors use correlation analysis and an out-of-sample forecasting exercise to assess and compare the information content of the principal component with that of responses to key individual survey questions on growth in real gross domestic product and in real business investment. Results suggest that summarizing the common movements among BOS indicators may provide useful information for forecasting near-term growth in business investment. For growth in real gross domestic product, however, the survey’s balance of opinion on future sales growth appears to be more informative.

Crowdfunding and Risk

Staff working paper 2023-28 David Cimon
Crowdfunding may enable unique products to reach the consumer market. I model a crowdfunding technology that publicly screens consumer demand early in the production process. In this model, entrepreneurs like crowdfunding for risky projects where demand is uncertain, but not for large, safe projects or for projects where production costs are uncertain.

The Financial Origins of Non-fundamental Risk

Staff working paper 2022-4 Sushant Acharya, Keshav Dogra, Sanjay Singh
We explore the idea that the financial sector can be a source of non-fundamental risk to the rest of the economy. We also consider whether policy can be used to reduce this risk—either by increasing the supply of publicly backed safe assets or by reducing the demand for safe assets.
August 15, 2013

Big Data Analysis: The Next Frontier

The formulation of monetary policy at the Bank of Canada relies on the analysis of a broad set of economic information. Greater availability of immediate and detailed information would improve real-time economic decision making. Technological advances have provided an opportunity to exploit “big data” - the vast amount of digital data from business transactions, social media and networked computers. Big data can be a complement to traditional information sources, offering fresh insight for the monitoring of economic activity and inflation.
Content Type(s): Publications, Bank of Canada Review articles JEL Code(s): C, C5, C53, C6, C63, C8, C80

Unpacking Moving: A Quantitative Spatial Equilibrium Model with Wealth

Staff working paper 2023-34 Elisa Giannone, Qi Li, Nuno Paixão, Xinle Pang
We propose a model to understand low observed migration rates by considering the interaction between location and wealth decisions. We look at different policies and find that temporary moving vouchers only slightly increase welfare, while lower housing regulations can decrease the welfare gap by lowering house prices nationwide.

Survival Analysis of Bank Note Circulation: Fitness, Network Structure and Machine Learning

Staff working paper 2020-33 Diego Rojas, Juan Estrada, Kim Huynh, David T. Jacho-Chávez
Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash.

A Portfolio-Balance Model of Inflation and Yield Curve Determination

Staff working paper 2020-6 Antonio Diez de los Rios
How does the supply of nominal government debt affect the macroeconomy? To answer this question, we propose a portfolio-balance model of the yield curve in which inflation is determined through an interest rate rule.
August 15, 2000

Restructuring in the Canadian Economy: A Survey of Firms

Towards the end of the 1980s and into the early 1990s, the Canadian economy experienced a number of structural changes. These included free trade agreements (both the FTA and NAFTA), significant technological advances, deregulation in many sectors of the economy, the arrival of large, U.S.-based retailers, and the introduction of the GST.

Partial Identification of Heteroskedastic Structural Vector Autoregressions: Theory and Bayesian Inference

Staff working paper 2025-14 Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak
We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions.
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