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...

One of the key inputs for inflation targeting regime is the right identification of inflationary or disinflationary pressures. These pressures are usually approximated by the output gap. In this paper we provide an estimation of the Peruvian output gap using a multivariate unobserved component...

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...

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...

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...

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...

Filtering techniques are often applied to the estimation of dynamic latent variable models. However, these techniques are often based on a set assumptions which restrict models to be specified in a linear state-space form. Numerical filtering techniques have been propsed that avoid invoking such...