We compare a number of methods that have been proposed in the literature for obtaining h-step ahead minimum mean square error forecasts for SETAR models. These forecasts are compared to those from an AR model. The comparison of forecasting methods is made using Monte Carlo simulation. The Monte Carlo method of calculating SETAR forecasts is generally at least as good as that of the other method...

This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et a...

A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfalltriggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather for...

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal a...

Global ensemble forecasts from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) are used to quantify the magnitude of moisture transport into North America ahead of recurving tropical cyclones (TCs). Two cases in which a predecessor rain event (PRE) occurred ahead of the recurving TC—Erin (2007) and Ike (2008)—are analyzed, with ens...

We study the predictive power of autoregressive moving average models when forecasting demand in two shared computational networks, PlanetLab and Tycoon. Demand in these networks is very volatile, and predictive techniques to plan usage in advance can improve the performance obtained drastically. Our key finding is that a random walk predictor performs best for one-step-ahead forecasts, whereas...

Using forecasts from Consensus Economics Inc., we provide evidence on the efficiency of real GDP growth forecasts by testing if forecast revisions are uncorrelated. As the forecast data used are multi-dimensional—18 countries, 24 monthly forecasts for the current and the following year and 16 target years—the panel estimation takes into account the complex structure of the variance covariance m...

Analysts often provide forecasts of one-year ahead earnings, earnings two-year ahead, as well as long-term earnings growth rates. In our attempts to understand the properties of these contemporaneous multi-period earnings forecasts, we begin by examining whether analysts’ earnings forecasts can be described using the linear information dynamics (LID). We find that LID is an appropriate descript...

VAR models are popular to forecast macroeconomic time series. However, the model, parameters, and error distribution rarely known without uncertainty, so bootstrap methods applied deal with these sources of uncertainties. In this paper, performance Bonferroni cubes based on Gaussian method variants procedure that incorporate distribution, parameter bias correction, lag order uncertainty compare...

Analysts often provide forecasts of one-year ahead earnings, earnings two-year ahead, as well as long-term earnings growth rates. In our attempts to understand the properties of these contemporaneous multi-period earnings forecasts, we begin by examining whether analysts’ earnings forecasts can be described using the linear information dynamics (LID). We find that LID is an appropriate descript...