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This paper shows point identification in first-price auction models with risk aversion and unobserved auction heterogeneity by exploiting multiple bids from each auction and variation in the number of bidders. The required exclusion restriction is shown to be consistent with a large class of entry models.

This paper takes a full-information model-based approach to evaluate the link between investment-specific technology and the inverse of the relative price of investment. The two-sector model presented includes monopolistic competition where firms can vary the markup charged on their product depending on the number of firms competing.

Our understanding of news shocks is, to a large extent, based on studies that focus empirically on short-run news. This paper brings new insights by analyzing the effects of giant commodity discoveries, which typically materialize over the longer run.

Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.

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.

We study a cross section of carry-trade-generated currency excess returns in terms of their exposure to global fundamental macroeconomic risk.

A model of over-the-counter markets is proposed. Some asset buyers are informed in that they can identify high quality assets. Heterogeneous sellers with private information choose what type of buyers they want to trade with.

The Bank of Canada has a mandate to “promote the economic and financial welfare of Canada,” primarily through the conduct of monetary policy and promotion of a safe, sound and efficient financial system. Understanding the macroeconomic and financial system impacts of climate change and the transition to a low-carbon economy is therefore a priority for the Bank.

News media present competing interpretations of what breaking news implies for the macroeconomy. Recent examples include news reporting on high inflation and yield curve inversions. Do these narratives shape macroeconomic sentiment? In this paper, we highlight the importance of narratives using evidence linking traditional media and social media.

We present a novel database of real-time data and forecasts from the Bank of Canada’s staff economic projections. We then provide a forecast evaluation for GDP growth and CPI inflation since 1982: we compare the staff forecasts with those from commonly used time-series models estimated with real-time data and with forecasts from other professional forecasters and provide standard bias tests.