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56 Results

Allocative Efficiency and the Productivity Slowdown

Staff working paper 2021-1 Lin Shao, Rongsheng Tang
In our analysis of the US productivity slowdown in the 1970s and 2000s, we find that a significant portion of this deceleration can be attributed to a lack of improvement in allocative efficiency across sectors. Our analysis further identifies increased sector-level volatility as a major contributor to this lack of improvement in allocative efficiency.
January 30, 2001

Annual Report 2001

The year that just passed posed many challenges for all Canadians. The slowdown in the global economy became more pronounced as the year went on, and this affected households, businesses, and governments alike. The tragedy of 11 September compounded the economic difficulties and issues facing us all. Through this period of rapidly changing circumstances, the Bank met its responsibilities by responding quickly and vigorously to events in order to underpin confidence and support the economy.
Content Type(s): Publications, Annual Report
January 29, 2000

Annual Report 1999

The Canadian economy regained strong momentum in 1999 as the U.S. economy remained vigorous, the global economy recovered, and commodity prices moved upwards.
Content Type(s): Publications, Annual Report
January 30, 2006

Annual Report 2005

In 2005, the Bank of Canada celebrated its 70th anniversary. Since the Bank opened its doors in March 1935, it has evolved into a national institution at the heart of Canada’s economy. We had a lot to celebrate in 2005—particularly our progress over the past 70 yearsand our continuing contribution to the economic and financial well-being of Canadians.
Content Type(s): Publications, Annual Report

Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects

Staff working paper 2019-16 Kerem Tuzcuoglu
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.
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