On the uncertainty and risks of macroeconomic forecasts: combining judgements with sample and model information

Abstract

Institutions which publish macroeconomic forecasts usually do not rely on a single econometric model to generate their forecasts. The combination of judgements with information from different models complicates the problem of characterizing the predictive density. This article proposes a parametric approach to construct the joint and marginal densities of macroeconomic forecasting errors, combining judgements with sample and model information. We assume that the relevant variables are linear combinations of latent independent two-piece normal variables. The baseline point forecasts are interpreted as the mode of the joint distribution, which has the convenient feature of being invariant to judgments on the balance of risks.

Optimizing the Paths of Trains Formed at the Loading Area in a Multi-loop Rail Network

“…Because the train flow needs to be considered in this paper, some studies on the well-known "maximal flow problem" in transport are also reviewed. Two algorithms were put forward to solve the problem of maximum flow assignment/distribution in [25] and [26]. András et al [27] used maximal flow and shortest route algorithms to choose edges in a transportation network.…”

Fan Chart A Tool for NBP's Monetary Policy Making

“…The methodology employed to estimate the final fan chart variance is based on the procedure described by Blix and Sellin (1998) and Pinheiro and Esteves (2010).…”

“…The procedure of estimating projection variance is similar to that proposed by Blix and Sellin (1998) and Pinheiro and Esteves (2010). We assume that after a history of projection exercises we have data on projection errors for each forecasted variable and each period of the projection .…”

“…) 11. The HPD intervals are centered around the mode, hence the most likely value of the forecast in the case of the fan chart, by construction, always lies within a specified interval.…”

Asymptotic expansions for moments of skew-normal extremes

“…Its applications are too many to list. Some applications of the skew normal distribution that have appeared in the past year alone include: the distribution of threshold voltage degradation in nanoscale transistors by using reaction-diffusion and percolation theory (Islam and Alam, 2011); population structure of Schima superba in Qingliangfeng National Nature Reserve (Liu et al, 2011); rain height models to predict fading due to wet snow on terrestrial links (Paulson and Al-Mreri, 2011); modeling of seasonal rainfall in Africa (Siebert and Ward, 2011); modeling of HIV viral loads (Bandyopadhyay et al, 2012); multisite flooding hazard assessment in the Upper Mississippi River (Ghizzoni et al, 2012); modeling of diabetic macular Edema data (Mansourian et al, 2012); risks of macroeconomic forecasts (Pinheiro and Esteves, 2012); modeling of current account balance data (Saez et al, 2012); automated neonatal EEG classification (Temko et al, 2012).…”