In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econometrics are presented and some of their properties discussed. This includes two models based on universal approximators: the Kolmogorov-Gabor polynomial model and...

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty...

This empirical study has provided interpretive outcome from a univariate forecast using Box-Jenkins ARIMA methodology. The HCPI_SA seasonally adjusted data for Sierra Leone shows a robust model outcome with three months ahead prediction based on the STATIC method result. Test results like...

We assess point and density forecasts from a mixed-frequency vector autoregression (VAR) to obtain intra-quarter forecasts of output growth as new information becomes available. The econometric model is specified at the lowest sampling frequency; high frequency observations are treated as...

A practice that has become widespread is that of comparing forecasts of financial return variability obtained from discrete time models against high frequency estimates based on continuous time theory. In explanatory financial return variability modelling this raises several methodological and...

In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to examine MGARCH...

In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined...

In this paper we investigate whether accounting for non-pervasive shocks improves the forecast of a factor model. We compare four models on a large panel of US quarterly data: factor models, factor models estimated on selected variables, Bayesian shrinkage, and factor models together with...

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...