Champagne, J, Poulin-Bellisle, G, and R. Sekkel (2018a). “Evaluating the Bank of Canada Staff Economic Projections Using a New Database of Real-Time Data and Forecasts”, Bank of Canada Staff Working Paper, 52.
Champagne, J, Poulin-Bellisle, G, and R. Sekkel (2018b). “The Real-Time Properties of Bank of Canada’s Staff Output Gap Estimates”, Journal of Money, Credit and Banking, 50 (6), 1167–1188.
Appendix I. Definitions and Model
Prepared by Troy Matheson (WHD).
Staff forecasts are a very important part of the analysis presented to the Governing Council every quarter in the weeks leading up to the publication of each Monetary Policy Report.
Both measures exclude the effects of changes in indirect taxes.
The statistics are derived from a vector-autoregressive model (VAR) containing quarterly inflation rates for headline CPI, core CPI, non-core CPI, the output gap (HP-filtered), and the short-term policy rate. The VAR includes 2 lags and is simulated 1000 times using bootstrapping methods.
In the terminology of Champagne and others (2018a), all forecasts have the same ‘jump-off point, period t.
See the appendix for more details on the model and An and Schofheide (2007) for more details on Bayesian estimation.
This analysis uses
The real-time historical shock decompositions of staff forecasts are computed by running a Kalman filter over the observable data contained in the Bank of Canada’s real-time database in each quarter, including the projection period.
The real-time estimates of the output gap, neutral policy rate, and policy gap are estimates for the current quarter in each forecast vintage (e.g. the real-time output gap in 2000Q1 is the estimated output gap in 2000Q1 from the 2000Q1 forecast vintage dataset).
The output gap starting-point error is derived from Bank of Canada staff’s real-time database. The nominal policy-gap starting-point error, on the other hand, is estimated using the neutral nominal rate derived from the data available in each real-time database.
Parameters estimated using Metropolis-Hastings, 1,000,000 draws, with a 50 percent burn.