In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily hours of sunshine (insolation duration). The...

This paper applies time distance, suggested by Granger and Jeon (1997), to models of in ation. The commonly used metric of forecast performance, mean squared forecasting error, measures how vertically close the forecast is to the series to be predicted; time distance instead measures the leading and lagging properties, often leading to significantly di erent conclusions about relative performan...

Using an ensemble of model forecasts to describe forecast error covariance extends linear sequential data assimilation schemes to nonlinear applications. This approach forms the basis of the Ensemble Kalman Filter and derivative filters such as the Ensemble Square Root Filter. While ensemble data assimilation approaches are commonly reported in the scientific literature, clear guidelines for ef...

Understanding of the stability of deterministic and stochastic dynamical systems has evolved recently from a traditional grounding in the system’s normal modes to a more comprehensive foundation in the system’s propagator and especially in an appreciation of the role of non-normality of the dynamical operator in determining the system’s stability as revealed through the propagator. This set of ...

Extended-range (<35 day) predictions of area-averaged convection over northern Australia are investigated with the Bureau of Meteorology’s Predictive Ocean-Atmosphere Model for Australia (POAMA). Hindcasts from 1980-2011 are used, initialized on the 1st, 11th, and 21st of each month, with a 33-member ensemble. The measure of convection is outgoing longwave radiation (OLR) averaged over the box ...

Permanent and widespread psychological biases affect both the subjective probability of future economic events and their retrospective interpretation. They may give rise to a systematic gap between (over-critical) judgments and (over-optimistic) expectations the “forecast” error. When things go bad, then, psychology suggests that people tend to become particularly optimistic, amplifying the for...

The purpose of this paper is to demonstrate that the success of the Litterman prior in VAR forecasting is not due to the realism of the prior, but rather because the prior conveniently reduces forecast error variance in common cases of misspecification. Specifically, it is shown that the imposition of a random walk prior reduces forecast error variance in misspecifications involving (1) time-va...

Recent evidence suggests that many economic time series are subject to structural breaks, yet little is known about the properties of alternative forecasting methods for such data. This paper proposes a new method for determining the window size that explores the trade-off between bias and forecast error variance to minimize the mean squared forecast error in the presence of breaks in autoregre...

We implement several Bayesian and classical models to forecast employment for eight sectors of the US economy. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of 143 additional monthly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two approa...