We propose a new method for medium-term forecasting using exogenous information. We first show how a shifting-mean autoregressive model can be used to describe characteristic features in inflation series. This implies that we decompose the inflation process into a slowly moving nonstationary...

This paper tests the expectations hypothesis (EH) of the term structure of interest rates in US data, using spectral regression techniques that allow us to consider different frequency bands. We find a positive relation between the term spread and the change in the long-term interest rate in a...

This paper attempts to extract market expectations about the Japanese economy and the BOJ's policy stance from the yen yield curves augmented by money market interest rates, during the period from the end of the quantitative easing policy in March 2006. We use (i) the swap yield curves augmented...

In this paper, we study the dynamics and drivers of sovereign bond yields in euro area countries using a factor model with time-varying loading coefficients and stochastic volatility, which allows for capturing changes in the pricing mechanism of bond yields. Our key contribution is exploring...

In this paper we compare the in-sample fit and out-of-sample forecasting performance of no-arbitrage quadratic and essentially affine term structure models, as well as the dynamic Nelson-Siegel model. In total eleven model variants are evaluated, comprising five quadratic, four affine and two...

We use Bayesian time-varying parameters VARs with stochastic volatility to investigate changes in the marginal predictive content of the yield spread for output growth in the United States and the United Kingdom, since the Gold Standard era, and in the Eurozone, Canada, and Australia over the...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...