The Canadian Phillips Curve and Regime Shifting

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 author investigates the linearity and constancy assumptions of a standard reduced-form Phillips curve for Canada using two different techniques: (i) the methodology proposed by Bai and Perron (1998), which allows for an unknown number of breaks at unknown dates, and (ii) a three-regimes Markov-switching regression model. Both methodologies strongly reject the linearity and parameter constancy assumptions. The author finds that the output-inflation relationship does not hold under the current monetary policy of inflation targeting, with its low and stable inflation. Since the inflation-control targets were adopted, inflation expectations appear to be more forward looking and well anchored at 2 per cent, the official target rate. Core inflation exhibits very low persistence and there do not appear to be significant asymmetries in the inflation response to output-gap shocks within regimes. Generalized impulse responses are computed to illustrate some properties of the Markov-switching Phillips curve model.

Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation

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. Forecasts from factor models are compared with those from AR(p) models as well as IS- and Phillips-curve models. We find that factor models can improve the forecast accuracy relative to standard benchmark models, for horizons of up to 8 quarters. Forecasts from our proposed factor models are also less prone to committing large errors, in particular when the horizon increases. We further show that the choice of the sampling-scheme has a large influence on the overall forecast accuracy, with smallest rolling-window samples generating superior results to larger samples, implying that using "limited-memory" estimators contribute to improve the quality of the forecasts.Econometric and statistical methods

The Canadian Business Cycle: A Comparison of Models

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. From the 9 data generating processes designed, none can completely accommodate every business-cycle metric under consideration. Richness and complexity do not guarantee a close match with Canadian data. Our findings for Canada are consistent with Piger and Morley's (2005) study of the United States data and confirms the contradiction of their results with those reported by Engel, Haugh, and Pagan (2005): nonlinear models do provide an improvement in matching business-cycle features. Lastly, the empirical results suggest that investigating the merits of forecast combination would be worthwhile.Business fluctuations and cycles; Econometric and statistical methods

Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?

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 authors show that the response of inflation to output-gap or real exchange rate shocks varies considerably across the components, although the average response remains low; they also show that the average response has decreased over time. To forecast monthly inflation, the use of disaggregate data is a significant improvement over the use of aggregate data. However, the improvements in forecasts of quarterly rates of inflation are only minor. Overall, it remains difficult to properly model and forecast monthly core inflation in Canada.Econometric and statistical methods; Inflation and prices

Forecasting and Analyzing World Commodity Prices

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. A structural vector autoregressive model is used for real BCNE prices and a multiple structural-break technique is employed for real crude oil prices. The authors use these transitory and permanent components to develop forecasting models. They assess various aspects of the models' performance. Their main results indicate that: (i) the world economic activity and real U.S.-dollar effective exchange rate explain much of the cyclical variation of real BCNE prices, (ii) real crude oil prices have two structural breaks over the sample period, and recently their link with the world economic activity has been quite strong, and (iii) the models outperform benchmark models, namely a vector autoregressive model, an autoregressive model, and a random-walk model, in terms of out-of-sample forecasting.Econometric and statistical methods

Prévision et analyse de la production manufacturière au Canada : comparaison de modèles linéaires et non linéaires

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 United States and the real exchange rate are the main factors that influence output in the manufacturing sector in Canada. Although the real exchange rate has a significant impact, the response of output to exchange rate shocks is highly asymmetric. In addition, non-linear Markov switching models appear to better explain the growth of output and provide more accurate out-of-sample forecasts. Because of the asymmetry, empirical evidence shows the need to take into account conditional rather than unconditional elasticities to the state of production in the manufacturing sector, especially when analyzing the response of output to real exchange rate shocks.Econometric and statistical methods; Business fluctuations and cycles