This paper evaluates linear and non-linear forecast-combination methods. Among the non-linear methods, we propose a nonparametric kernel-regression weighting approach that allows maximum flexibility of the weighting parameters. A Monte Carlo simulation study is performed to compare the...

This paper examines the business cycle linkages that propagate industry-specific business cycle shocks throughout the economy in a way that (sometimes) generates aggregated cycles. The transmission of sectoral business cycles is modelled through a multivariate Markov-switching model, which is...

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 models postulate meanreverting prices with continuous and random changes in their level and trend, and are estimated using...

A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real time. The method conveniently deals with the path dependence...

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 class includes analogues of the well-known Diebold and Mariano (1995) parametric and...

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. Empirical evidence reported in the paper suggests that economic activity in the...