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51 Results

Inference in Games Without Nash Equilibrium: An Application to Restaurants’ Competition in Opening Hours

Staff Working Paper 2018-60 Erhao Xie
This paper relaxes the Bayesian Nash equilibrium (BNE) assumption commonly imposed in empirical discrete choice games with incomplete information. Instead of assuming that players have unbiased/correct expectations, my model treats a player’s belief about the behavior of other players as an unrestricted unknown function. I study the joint identification of belief and payoff functions.

Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects

Staff Working Paper 2019-16 Kerem Tuzcuoglu
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.

Risk-Cost Frontier and Collateral Valuation in Securities Settlement Systems for Extreme Market Events

Staff Working Paper 2006-17 Alejandro García, Ramazan Gençay
The authors examine how the use of extreme value theory yields collateral requirements that are robust to extreme fluctuations in the market price of the asset used as collateral.

Can Media and Text Analytics Provide Insights into Labour Market Conditions in China?

The official Chinese labour market indicators have been seen as problematic, given their small cyclical movement and their only-partial capture of the labour force. In our paper, we build a monthly Chinese labour market conditions index (LMCI) using text analytics applied to mainland Chinese-language newspapers over the period from 2003 to 2017.

Understanding the Systemic Implications of Climate Transition Risk: Applying a Framework Using Canadian Financial System Data

Our study aims to gain insight on financial stability and climate transition risk. We develop a methodological framework that captures the direct effects of a stressful climate transition shock as well as the indirect—or systemic—implications of these direct effects. We apply this framework using data from the Canadian financial system.

Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.
May 13, 2014

Bank of Canada Review - Spring 2014

The five articles in this issue present research and analysis by Bank staff covering a variety of topics: the growth of Canadian-dollar-denominated assets in official foreign reserves; the emergence of platform-based digital currencies; methods of forecasting the real price of oil; measures of uncertainty in monetary policy; and the recent performance of the labour market in Canada and the United States.

The Role of International Financial Integration in Monetary Policy Transmission

Staff Working Paper 2024-3 Jing Cynthia Wu, Yinxi Xie, Ji Zhang
We propose an open-economy New Keynesian model with financial integration that allows financial intermediaries to hold foreign long-term bonds. We study the implications of financial integration on monetary policy transmission. Among various aspects of financial integration, the bond duration plays a major role. These results hold for conventional and unconventional monetary policies.

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.
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