This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (2002) to allow for time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional...

Coibion and Gorodnichenko (2015) provide a useful framework to test the null hypothesis of full-information rational expectations against two popular classes of information rigidities, sticky information (SI) and noisy information (NI). However, the observational equivalence of SI and NI in...

We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents...

This article investigates what determines the price dynamics of the main cereals: barley, maize, rice and wheat. Using an extensive dataset of monthly time series covering the years 1980 - 2019, we extract four different common factors explaining the dynamics of commodity prices, exchange rates,...

We explore the historical relationship between financial conditions and real economic growth for quarterly U.S. data from 1875 to 2017 with a flexible empirical copula modelling methodology. We compare specifications with both linear and non-linear dependence, and with both Gaussian and...

I introduce the essential aspects of the eigensystem vector autoregression (EVAR), which allows VARs to be specified and estimated directly in terms of their eigensystem, using univariate examples for clarity. The EVAR guarantees non-explosive dynamics and, if included, non-redundant...

We propose a state space modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and...

Empirical work in macroeconometrics has been mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and...

Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a hundred variables has proved challenging due to computational considerations and over-parameterization concerns. The existing literature either works with homoskedastic models or...