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The U.S. Energy Information Administration regularly publishes short-term forecasts of the price of crude oil.

The authors study the implications of fiscal policy behaviour for sovereign risk in a framework that determines a country’s fiscal limit, the point at which, for economic or political reasons, taxes and spending can no longer adjust to stabilize debt.

The paper explores the macroeconomic consequences of fiscal consolidations whose timing and composition - either tax- or spending-based - are uncertain.

Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date.

Canada’s Short-Term Indicator (CSI) is a new model that exploits the information content of 32 indicators to produce daily updates to forecasts of quarterly real GDP growth. The model is a data-intensive, judgment-free approach to short-term forecasting. While CSI’s forecasts at the start of the quarter are not very accurate, the model’s accuracy increases appreciably as more information becomes available. CSI is the latest addition to a wide range of models and information sources that the Bank of Canada uses, combined with expert judgment, to produce its short-term forecasts.

This article examines whether combining forecasts of real GDP from different models can improve forecast accuracy and considers which model-combination methods provide the best performance. In line with previous literature, the authors find that combining forecasts generally improves forecast accuracy relative to various benchmarks. Unlike several previous studies, however, they find that, rather than assigning equal weights to each model, unequal weighting based on the past forecast performance of models tends to improve accuracy when forecasts across models are substantially different.

The Bank of Canada uses several short-term forecasting models for the monitoring of key foreign economies - the United States, the euro area, Japan and China. The design of the forecasting models used for each region is influenced by the level of detail required, as well as the timeliness and volatility of data. Forecasts from different models are typically combined to mitigate model uncertainty, and judgment is applied to the model forecasts to incorporate information that is not directly reflected in the most recent indicators.

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.

Rising consumer prices may reflect shifts by consumers to new higher-priced products, mostly for durable and semi-durable goods. I apply Bils’ (2009) methodology to newly available Canadian consumer price data for non-shelter goods and services to estimate how price increases can be divided between quality growth and price inflation.

We examine the implications of increased unconventional crude oil production in North America. This production increase has been made possible by the existence of alternative oil-recovery technologies and persistently elevated oil prices that make these technologies commercially viable.