Forecasting risks to the Canadian economic outlook at a daily frequency

In this paper, we estimate the distribution of future inflation and growth in real gross domestic product (GDP) for the Canadian economy at a daily frequency. To do this, we model the conditional moments (mean, variance, skewness and kurtosis) of inflation and GDP growth as moving averages of economic and financial conditions. Then, we translate the conditional moments into conditional distributions using a flexible parametric distribution known as the skewed generalized error distribution. We show that the probabilities of inflation and GDP growth derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022. Our methodology offers dailyfrequency forecasts with flexible forecasting horizons. This is highly useful in an environment of elevated uncertainty surrounding the inflation and growth outlook.