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.
The authors examine the evidence presented by Galí and Gertler (1999) and Galí, Gertler, and Lopez-Salido (2001, 2003) that the inflation dynamics in the United States can be well-described by the New Keynesian Phillips curve (NKPC).
The authors test the statistical significance of Pindyck's (1999) suggested class of econometric equations that model the behaviour of long-run real energy prices.
The author proposes a class of exact tests of the null hypothesis of exchangeable forecast errors and, hence, of the hypothesis of no difference in the unconditional accuracy of two competing forecasts.
The authors examine evidence of long- and short-run co-movement in Canadian sectoral output data. Their framework builds on a vector-error-correction representation that allows them to test for and compute full-information maximum-likelihood estimates of models with codependent cycle restrictions.
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.