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We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.

A low level of the neutral rate of interest increases the likelihood that a central bank’s policy rate will reach its effective lower bound (ELB) in future economic downturns. In a low neutral rate environment, using an extended monetary policy toolkit including forward guidance helps address the ELB challenge. Using the Bank’s Terms-of-Trade Economic Model, we assess the benefits and limitations of a state-contingent forward guidance implemented within a flexible inflation targeting framework.

We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

When countries are hit by supply shocks, central banks often face the dilemma of either looking through such shocks or reacting to them to ensure that inflation expectations remain anchored. In this paper, we propose a tractable framework to capture this dilemma and then explore optimal policy under a range of assumptions about how expectations are formed.

This paper derives a calculation for the effective degrees of freedom of a forecast combination under a set of general conditions for linear models. Computing effective degrees of freedom shows that the complexity cost of a forecast combination is driven by the parameters in the weighting scheme and the weighted average of parameters in the auxiliary models.

How do households respond when they receive unanticipated income, such as an inheritance or government stimulus cheque? This paper studies these windfall income shocks through a model of household behaviour that generates a realistic consumption response for households along the entire distribution of wealth.

We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a disaggregated industrial production series for the US economy. We identify unexpected changes in durable goods uncertainty as drivers of downturns, while unexpected hikes in non-durable goods uncertainty are expansionary.

Using a nonlinear New Keynesian model with a financial accelerator, we show that financial frictions generate large state-dependent amplification effects. Shocks propagate more strongly in periods of financial stress. We propose an endogenous regime-switching DSGE framework for efficient estimation and improved model fit.

How should central banks design monetary policy in stable times and during recessions? We run a horse race between five monetary policy frameworks in an experimental laboratory to assess how well the different approaches can manage the public’s expectations and stabilize the economy.

We use graphic processing unit computing to compare Bayesian and sample theory semiparametric binary response models. Our findings show that optimal bandwidth does not outperform regular bandwidth in binary semiparametric models.