Tony Chernis is a principal economist in the Survey and Economic Intelligence Division of the Canadian Economic Analysis department. His research interests are empirical macroeconomics. Specific topics include, forecasting, model combinations and decision-making with multiple models. Tony holds a master’s degree in economics from Simon Fraser University and is a part-time PhD student at the University of Strathclyde.
Because the Bank of Canada has started withdrawing monetary stimulus, monitoring the transmission of these changes to monetary policy will be important. Subcomponents of consumption and housing will likely respond differently to a monetary policy tightening, both in terms of the aggregate effect and timing.
This paper introduces the Business Leaders’ Pulse, a new online survey conducted each month. It is designed to provide timely and flexible input into the Bank of Canada’s monetary policy decision making by asking firms about their sales and employment growth expectations, the risks to their business outlook, and topical questions that address specific information needs of the Bank.
We present a tool for creating density nowcasts for Canadian real GDP growth. We demonstrate that the combined densities are a reliable and accurate tool for assessing the state of the economy and risks to the outlook.
This paper studies short-term forecasting of Canadian real GDP and its expenditure components using combinations of nowcasts from different models. Starting with a medium-sized data set, we use a suite of common nowcasting tools for quarterly real GDP and its expenditure components.
This paper estimates a three‐frequency dynamic factor model for nowcasting Canadian provincial gross domestic product (GDP). Canadian provincial GDP is released by Statistics Canada on an annual basis only, with a significant lag (11 months).
Canadian exports have often disappointed since the Great Recession. The apparent disconnect between exports and the Bank of Canada’s current measure of foreign demand has created an impetus to search for an alternative.
We use Bayesian predictive decision synthesis to formalize monetary policy decision-making. We develop a case-study of monetary policy decision-making of an inflation-targeting central bank using multiple models in a manner that considers decision goals, expectations and outcomes.
This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability.
I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.
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.
