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The results of our 2017 Bitcoin Omnibus Survey (December 12 to 15, 2017) when compared with those from 2016 show that Bitcoin “awareness” increased from 64 to 85 per cent, while ownership increased from 2.9 to 5.0 per cent. Most Bitcoin purchasers are using the cryptocurrency as an investment and not as a means of payment for goods or services.

This paper empirically determines why, during the 1990s, inflation in Canada was consistently more stable than predicted by the fixed-coefficients Phillips curve. A time-varying-coefficient model, where all the parameters adjust simultaneously, shows that the behaviour of expectations was probably a major contributing factor.

We use controlled laboratory experiments to test the causal effects of central bank communication on economic expectations and to distinguish the underlying mechanisms of those effects. In an experiment where subjects learn to forecast economic variables, we find that central bank communication has a stabilizing effect on individual and aggregate outcomes and that the size of the effect varies with the type of communication.

Our paper employs smartphone data to construct an improved cash access metric by accounting for both spatial agglomeration and households’ travel patterns. We find that incorporating trip-chaining into the travel metric could show that travel costs are from 15 to 25% less than not incorporating trip-chaining and that the biggest decrease is driven by rural residents.

Building on the growing evidence on the importance of large data sets for empirical macroeconomic modeling, we use a factor-augmented VAR (FAVAR) model with more than 260 series for 20 OECD countries to analyze how global developments affect the Canadian economy.

In this paper, we use an economics decision-making experiment to test a key assumption underpinning the efficacy of price-level targeting relative to inflation targeting for business cycle stabilization and mitigating the effects of the zero lower bound on nominal interest rates.

A longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging.

In this paper two shocks are analysed using Canadian data: a money-supply shock ("M-shock") and an interest-rate shock ("R-shock"). Money-supply shocks are derived using long-run restrictions based on long-run propositions of monetary theory. Thus, an M-shock is represented by an orthogonalized innovation in the trend shared by money and prices.

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.

The purpose of this paper is to make a quantitative contribution to the inflation versus price level targeting debate. It considers a policy-maker that can set policy either through an inflation targeting rule or a price level targeting rule to minimize a quadratic loss function using the actual projection model of the Bank of Canada (ToTEM).