We use a vector error correction model to study the long-term relationship between aggregate expected default frequency and the macroeconomic development, i.e. CPI, industry production and short-term interest rate. The model is used to forecast the median expected default frequency of the...

The present paper offers a careful description of empirical identification of possible multiple changes in regime. We apply recently developed tools designed to select between regime-switching models among a broad class of linear and nonlinear regression models and provide a discussion of the...

Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

This paper compares within-sample and out-of-sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets and Wouters model is the chosen laboratory using quarterly real-time euro area data vintages, covering 2001Q1-2019Q4. The adaptive learning model...

This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses...

Density forecasts of euro area inflation are a fundamental input for a medium-term oriented central bank, such as the European Central Bank (ECB). We show that a quantile regression forest, capturing a general non-linear relationship between euro area (headline and core) inflation and a large...

This paper outlines a strategic plan for the development of the fourth generation of Bank of Canada projection and policy analysis models. The plan features a new Canadian workhorse macroeconomic model as well as a suite of alternative models to better support a risk management approach to...

We provide a versatile nowcasting toolbox that supports three model classes (dynamic factor models, large Bayesian VAR, bridge equations) and offers methods to manage data selection and adjust for Covid-19 observations. The toolbox aims at simplifying two key tasks: creating new nowcasting...