Business fluctuations and cycles
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17 November 2011 Extracting Information from the Business Outlook Survey: A Principal-Component Approach
This article reviews recent work that uses principal-component analysis to extract information common to indicators from the Bank of Canada’s Business Outlook Survey (BOS). The authors use correlation analysis and an out-of-sample forecasting exercise to assess and compare the information content of the principal component with that of responses to key individual survey questions on growth in real gross domestic product and in real business investment. Results suggest that summarizing the common movements among BOS indicators may provide useful information for forecasting near-term growth in business investment. For growth in real gross domestic product, however, the survey’s balance of opinion on future sales growth appears to be more informative. -
Inventories, Markups and Real Rigidities in Sticky Price Models of the Canadian Economy
Recent New Keynesian models of macroeconomy view nominal cost rigidities, rather than nominal price rigidities, as the key feature that accounts for the observed persistence in output and inflation. Kryvtsov and Midrigan (2010a,b) reassess these conclusions by combining a theory based on nominal rigidities and storable goods with direct evidence on inventories for the U.S. -
The Impact of the Global Business Cycle on Small Open Economies: A FAVAR Approach for Canada
Building on the growing evidence on the importance of large data sets for empirical macroeconomic modeling, we use a factor-augmented VAR (FAVAR) model with more than 260 series for 20 OECD countries to analyze how global developments affect the Canadian economy. -
Financial Spillovers Across Countries: The Case of Canada and the United States
The authors investigate financial spillovers across countries with an emphasis on the effect of shocks to financial conditions in the United States on financial conditions and economic activity in Canada. These questions are addressed within a global vector autoregression model.