Abstract. We develop fast methods for conditional forecasting and structural scenario analysis with high-dimensional Bayesian vector autoregressions (VARs). Our general framework features a factor structure on the reduced-form errors, which enables fast and order-invariant equation-by-equation estimation; suitably identified factors admit a structural interpretation. The scenarios are defined through separate distributional restrictions on observables, structural shocks and idiosyncratic components. The computational cost of our proposed algorithm is cubic only in the number of restrictions, while the dimension of the forecasted system enters linearly. In our application with $33$ macroeconomic and financial variables and ten set-identified structural shocks for the US, we compute counterfactual predictions for oil price scenarios in the context of the 2026 closure of the Strait of Hormuz. The same oil price path is consistent with outcomes ranging from a mostly benign episode to pronounced stagflation, depending on which structural and idiosyncratic shocks are allowed to deliver it.

Abstract. We propose a dynamic factor model (DFM) where the latent factors are linked to observed variables with unknown and potentially nonlinear functions. The key novelty and source of flexibility of our approach is a nonparametric observation equation, specified via Gaussian Process (GP) priors for each series. Factor dynamics are modeled with a standard vector autoregression (VAR), which facilitates computation and interpretation. We discuss a computationally efficient estimation algorithm and consider two empirical applications. First, we forecast key series from the FRED-QD dataset and show that the model yields improvements in predictive accuracy relative to linear benchmarks. Second, we extract driving factors of global inflation dynamics with the GP-DFM, which allows for capturing international asymmetries.

Abstract. We develop Bayesian machine learning methods for mixed frequency data. This involves handling frequency mismatches and specifying functional relationships between (possibly many) predictors and low frequency dependent variables. We use Gaussian Processes (GPs) in direct nonlinear predictive regressions, and compress higher frequency variables in a structured way. This yields a set of kernels for GPs with distinct properties and implications. We evaluate the proposed framework in an out-of-sample exercise focusing on quarterly US GDP growth and inflation. Our approach leverages high-dimensional mixed frequency data in a computationally efficient way, and offers robustness and gains in predictive accuracy along several dimensions.

An earlier version circulated as Nowcasting with mixed frequency data using Gaussian processes.

Abstract. We study domestic and international drivers of long-term interest rates using newly compiled financial market data for Switzerland starting in 1852. We use a time-varying parameter vector autoregressive model to estimate long-term trends in nominal interest rates, exchange rate growth, and inflation. We then decompose the Swiss long-term interest rate trend into various drivers using an interest rate accounting framework. The decline in long-term interest rates since 1970 is mainly driven by a decline in the level of inflation. Comparing Switzerland with the rest of the world, we show that while Swiss real interest rates were higher during the 19th century, the pattern reversed after WorldWar 2 with Swiss nominal and real rates becoming lower than foreign ones. However, this Swiss "low interest rate island" has disappeared in recent years. We document a connection between inflation risk and the Swiss term spread, as well between relative inflation risk and the difference between Swiss and foreign real interest rates.