This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every...

The usual index of leading indicators has constant weights on its components and is therefore implicitly premised on the assumption that the dynamical properties of the economy remain the same over time and across phases of the business cycle. We explore the possibility that the business cycle...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors should be serially uncorrelated at the single period horizon with...

A common problem in out-of-sample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pre-test predictors (or bagging for short) as a means of constructing forecasts from multiple regression...

In this paper we incorporate the term structure of interest rates in a standard inflation forecast targeting framework. Learning about the transmission process of monetary policy is introduced by having heterogeneous agents - i.e. the central bank and private agents - who have different...

We propose a new model for the variance between multiple time series, the Regime Switching Dynamic Correlation. We decompose the covariances into correlations and standard deviations and the correlation matrix follow a regime switching model; it is constant within a regime but different across...

Recently financial econometricians have shifted their attention from point and interval forecasts to density forecasts mainly to address the issue of the huge loss of information that results from depicting portfolio risk by a measure of dispersion alone. One of the major problems in this area...

Traditional "Granger-Causality" (henceforth just G-causality) concerned the conditional mean. It required that the causal variable Yt preceded the causal variable Xt+1 in time and also that Yt contained special information about Xt+1 which would be shown in the conditional mean E[Xt+1|Yt]. There...

In real time forecasting, the sample is usually split into an estimation period of R observations and a prediction period of P observations, where T=R+P. Parameters are often estimated in a recursive manner, initially using R observations, then R+1 observations and so on until T-1 observations...

The possibility of confusing long memory behavior with structural changes need to specify what kind of long memory behavior is concerned in literature and applications. One attraction of long memory models is that they imply different long run predictions and effects of shocks to conventional...