scholarly journals How to Improve Inflation Forecasting in Canada

2019 ◽  
Vol 2019 (190) ◽  
Author(s):  
Troy Matheson
Keyword(s):  
Real Time ◽  
Inflation Targeting ◽  
Commodity Prices ◽  
Output Gap ◽  
Forecast Errors ◽  
Bank Of Canada ◽  
Core Inflation ◽  
Inflation Forecasts ◽  
Inflation Forecasting ◽  
Gradual Approach

Against the backdrop of an ongoing review of the inflation-targeting framework, this paper examines the real-time inflation forecasts of the Bank of Canada with the aim of identifying potential areas for improvement. Not surprisingly, the results show that errors in forecasting non-core inflation (commodity prices etc.) are found to be the largest contributors to overall inflation forecast errors. Perhaps more importantly, relatively small core inflation forecast errors appear to mask large and offsetting errors related to the output gap and the policy interest rate, partly reflecting a tendency to overestimate the neutral nominal policy rate in real time. Faced with these uncertainties, the Governing Council’s gradual approach to changing its policy settings appears to have served it well.

scholarly journals Coordinating expectations through central bank projections

2020 ◽  
Author(s):  
Fatemeh Mokhtarzadeh ◽  
Luba Petersen
Keyword(s):  
Central Bank ◽  
Rational Expectations ◽  
Inflation Targeting ◽  
Aggregate Stability ◽  
Central Banks ◽  
Output Gap ◽  
Forecast Errors ◽  
Market Participants ◽  
Wide Range ◽  
Ex Ante

AbstractCentral banks are increasingly communicating their economic outlook in an effort to manage the public and financial market participants’ expectations. We provide original causal evidence that the information communicated and the assumptions underlying a central bank’s projection can matter for expectation formation and aggregate stability. Using a between-subject design, we systematically vary the central bank’s projected forecasts in an experimental macroeconomy where subjects are incentivized to forecast the output gap and inflation. Without projections, subjects exhibit a wide range of heuristics, with the modal heuristic involving a significant backward-looking component. Ex-Ante Rational dual projections of the output gap and inflation significantly reduce the number of subjects’ using backward-looking heuristics and nudge expectations in the direction of the rational expectations equilibrium. Ex-Ante Rational interest rate projections are cognitively challenging to employ and have limited effects on the distribution of heuristics. Adaptive dual projections generate unintended inflation volatility by inducing boundedly-rational forecasters to employ the projection and model-consistent forecasters to utilize the projection as a proxy for aggregate expectations. All projections reduce output gap disagreement but increase inflation disagreement. Central bank credibility is significantly diminished when the central bank makes larger forecast errors when communicating a relatively more complex projection. Our findings suggest that inflation-targeting central banks should strategically ignore agents’ irrationalities when constructing their projections and communicate easy-to-process information.

scholarly journals The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time

2005 ◽  
Vol 37 (3) ◽  
pp. 583-601 ◽  
Author(s):  
Athanasios Orphanides ◽  
Simon Van Norden
Keyword(s):  
Real Time ◽  
Output Gap ◽  
Inflation Forecasts

scholarly journals Measuring Output Gap

2020 ◽  
Vol 20 (24) ◽  
Author(s):  
Jiaqian Chen ◽  
Lucyna Gornicka
Keyword(s):  
Real Time ◽  
Structural Var ◽  
Output Gap ◽  
Lower Sensitivity ◽  
Forecast Errors ◽  
Policy Decisions ◽  
Out Of Sample ◽  
Identification Strategy ◽  
Filtering Techniques

We apply a range of models to the U.K. data to obtain estimates of the output gap. A structural VAR with an appropriate identification strategy provides improved estimates of output gap with better real time properties and lower sensitivity to temporary shocks than the usual filtering techniques. It also produces smaller out-of-sample forecast errors for inflation. At the same time, however, our results suggest caution in basing policy decisions on output gap estimates.

scholarly journals TREND-CYCLE DECOMPOSITION OF OUTPUT AND EURO AREA INFLATION FORECASTS: A REAL-TIME APPROACH BASED ON MODEL COMBINATION

2013 ◽  
Vol 19 (2) ◽  
pp. 363-393 ◽  
Author(s):  
Pierre Guérin ◽  
Laurent Maurin ◽  
Matthias Mohr
Keyword(s):  
Real Time ◽  
Euro Area ◽  
Output Gap ◽  
Model Combination ◽  
Cycle Decomposition ◽  
Potential Output ◽  
Inflation Forecasts ◽  
Data Revisions ◽  
Ex Post ◽  
Decomposition Models

This paper estimates univariate and multivariate trend-cycle decomposition models of GDP and considers the novel possibility of regime switches in the growth of potential output. We compute both ex post and real-time estimates of the output gap to check the stability of our estimates to GDP data revisions. We find some evidence of regime changes in the growth of potential output during the recessions experienced by the euro area. We also run a forecasting experiment to evaluate the predictive power of the output gap for inflation. The benchmark autoregressive model tends to obtain the best forecasts for one-quarter-ahead forecasts, but the output gap measures help to forecast inflation for longer horizons.

scholarly journals Short-Run Forecasting of Core Inflation in Ukraine: a Combined ARMA Approach

2019 ◽  
pp. 11-20 ◽  
Author(s):  
Dmytro Krukovets ◽  
Olesia Verchenko
Keyword(s):  
Structural Model ◽  
Arma Model ◽  
Aggregated Data ◽  
Core Inflation ◽  
The Public ◽  
Inflation Forecasts ◽  
Inflation Forecasting ◽  
Empirical Performance ◽  
Short Run

The ability to produce high-quality inflation forecasts is crucial for modern central banks. Inflation forecasts are needed for understanding current and forthcoming inflation trends, evaluating the effectiveness of previous policy actions, making new policy decisions, and building the credibility of a central bank in the eyes of the public. This motivates a constant search for new approaches to producing inflation forecasts. This paper analyses the empirical performance of several alternative inflation forecasting models based on structural vs. data-driven approaches, as well as aggregated vs. disaggregated data. It demonstrates that a combined ARMA model with data-based dummies that uses the disaggregated core inflation data for Ukraine allows to considerably improve the quality of an inflation forecast as compared to the core structural model based on aggregated data.

scholarly journals The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time

2004 ◽  
Vol 2004 (68) ◽  
pp. 1-28
Author(s):  
Athanasios Orphanides ◽  
◽  
Simon Van Norden
Keyword(s):  
Real Time ◽  
Output Gap ◽  
Inflation Forecasts
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