This paper examines the ability of linear and nonlinear models to replicate features of real Canadian GDP. We evaluate the models using various business-cycle metrics.
This paper evaluates the performance of static and dynamic factor models for forecasting Canadian real output growth and core inflation on a quarterly basis. We extract the common component from a large number of macroeconomic indicators, and use the estimates to compute out-of-sample forecasts under a recursive and a rolling scheme with different window sizes.
The authors investigate the behaviour of core inflation in Canada to analyze three key issues: (i) homogeneity in the response of various price indexes to demand or real exchange rate shocks relative to the response of aggregate core inflation; (ii) whether using disaggregate data helps to improve the forecast of core inflation; and (iii) whether using monthly data helps to improve quarterly forecasts.
The author proposes and evaluates econometric models that try to explain and forecast real quarterly housing expenditures in Canada. Structural and leading-indicator models of the Canadian housing sector are described.
In this paper, the author describes reduced-form linear and non-linear econometric models developed to forecast and analyze quarterly data on output growth in the Canadian manufacturing sector from 1981 to 2003.
Phillips curves are generally estimated under the assumption of linearity and parameter constancy. Linear models of inflation, however, have recently been criticized for their poor forecasting performance.
The authors develop simple econometric models to analyze and forecast two components of the Bank of Canada commodity price index: the Bank of Canada non-energy (BCNE) commodity prices and the West Texas Intermediate crude oil price. They present different methodologies to identify transitory and permanent components of movements in these prices.
