Corinne Luu was appointed Principal Economist in the Canadian Economic Analysis (CEA) department in November 2017. In this capacity she is involved in research and analysis on the Canadian economy, with a focus on Canadian labour market developments.
Ms. Luu first joined the Bank in 2006 as an economist in the Asia-Europe Division of the International Department and subsequently moved to the U.S.-Mexico Division in 2008. She joined the Canadian Projection and Policy Analysis division of the Canadian Economic Analysis department 2009 as a Senior Analyst. In 2014, she moved to the Organisation for Economic Co-operation and Development (OECD) in Paris in as an economist in the Economics Department.
Born in Lethbridge, Alberta, Ms. Luu received a Master’s degree in Economics from the University of British Columbia (2006) and a Bachelor of Arts with First Class Honours in Economics from the University of Calgary (2005).
Underlying wage growth has fallen short of what would be consistent with an economy operating with little or no slack. While many factors could explain this weakness, the availability of additional labour resources from informal (“gig”) work—not fully captured in standard measures of employment and hours worked—may play a role.
The literature highlights that labour market churn, including job-to-job transitions, is a key element of wage growth. Using microdata from the Labour Force Survey, we compute measures of labour market churn and compare these with pre-crisis averages to assess implications for wage growth.
Because the Bank of Canada has started withdrawing monetary stimulus, monitoring the transmission of these changes to monetary policy will be important. Subcomponents of consumption and housing will likely respond differently to a monetary policy tightening, both in terms of the aggregate effect and timing.
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 financial crisis of 2007–09 has highlighted the importance of developments in financial conditions for real economic activity. The authors estimate the effect of current and past shocks to financial variables on U.S. GDP growth by constructing two growthbased financial conditions indexes (FCIs) that measure the contribution to quarterly (annualized) GDP growth from financial conditions.