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686 Results

Global Demand and Supply Sentiment: Evidence from Earnings Calls

Staff Working Paper 2023-37 Temel Taskin, Franz Ulrich Ruch
This paper quantifies global demand, supply and uncertainty shocks and compares two major global recessions: the 2008–09 Great Recession and the COVID-19 pandemic. We use two alternate approaches to decompose economic shocks: text mining techniques on earnings calls transcripts and a structural Bayesian vector autoregression model.

Credit Card Minimum Payment Restrictions

Staff Working Paper 2024-26 Jason Allen, Michael Boutros, Benedict Guttman-Kenney
We study a government policy that restricts repayment choices with the aim of reducing credit card debt and estimate its effects by applying a difference-in-differences methodology to comprehensive credit-reporting data about Canadian consumers. We find the policy has trade-offs: reducing revolving debt comes at a cost of reducing credit access, and potentially increasing delinquency.

Empirical Evidence on the Cost of Adjustment and Dynamic Labour Demand

Staff Working Paper 1995-3 Robert Amano
In this paper the author examines whether there is significant evidence of the effect of adjustment costs on Canadian labour demand. This is an important question, as sluggish adjustment of labour demand resulting from significant adjustment costs may be one factor that could help explain some of the unemployment persistence found in Canadian data. The […]
Content Type(s): Staff research, Staff working papers Research Topic(s): Labour markets

Behavioral Learning Equilibria in New Keynesian Models

Staff Working Paper 2022-42 Cars Hommes, Kostas Mavromatis, Tolga Özden, Mei Zhu
We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

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.
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