Inflation targeting (IT) had originally been introduced as a device to bring inflation down and stabilize it at low levels. Given the current environment of persistently weak inflation in many advanced economies, IT central banks must now bring inflation up to target. In this paper, the author...

This paper studies the formation of consumers' inflation expectations using micro-level data from the Michigan Survey. It shows that beyond the well-established socioeconomic determinants of inflation expectations such as gender, income or education, other characteristics such as the households'...

We propose alternative single-equation semi-structural models for forecasting inflation in Canada, whereby structural New Keynesian models are combined with time-series features in the data. Several marginal cost measures are used, including one that in addition to unit labour cost also...

This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data. The model is then used to generate nowcasts, predictions of the recent past and...

We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of both lagged volatility and returns. The model has several advantages: it is parsimonious; it does not require imposing parameter restrictions; and, it...

A covariance-stationary vector of variables has a Wold representation whose coefficients can be semi-parametrically estimated by local projections (Jordà, 2005). Substituting the Wold representations for variables in model expressions generates restrictions that can be used by the method of...

We incorporate quantile regressions into a structural vector autoregression model to empirically assess how monetary and fiscal policy influence risks around future GDP growth. Using a panel of six developed countries, we find that both policy instruments affect the location of the distribution...

Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as...

This paper provides a framework for the early assessment of current U.S. nominal GDP growth, which has been considered a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy-makers have available at the time predictions are made....