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66 Results

The (Mis)Allocation of Corporate News

Staff working paper 2024-47 Xing Guo, Alistair Macaulay, Wenting Song
We study how the distribution of information supply by the news media affects the macroeconomy. We find that media coverage focuses particularly on the largest firms, and that firms’ equity financing and investment increase after media coverage. But these equity and investment responses are largest among small, rarely covered firms. Our quantitative studies highlight that the aggregate effects of media coverage depend crucially on how that coverage is allocated.

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.

Exchange Rates, Retailers, and Importing: Theory and Firm-Level Evidence

Staff working paper 2019-34 Alex Chernoff, Patrick Alexander
We develop a model with firm heterogeneity in importing and cross-border shopping among consumers. Exchange-rate appreciations lower the cost of imported goods, but also lead to more cross-border shopping; hence, the net impact on aggregate retail prices and sales is ambiguous.

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.

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.
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