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 consider the forecasting performance of a well-defined class of flexible models, the so-called single hidden-layer feedforward neural network models. A major aim of our study is to find out whether they, due to their flexibility, are as useful tools in economic forecasting as...

Nonlinear time series models have become fashionable tools to describe and forecast a variety of economic time series. A closer look at reported empirical studies, however, reveals that these models apparently fit well in-sample, but rarely show a substantial improvement in out-of-sample...

In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real-time monetary aggregate M3 (1977-2000), and...

We evaluate various models' relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models' relative performance can be varying over time. We show that the models' relative performance has, in fact,...

Inflation is a far from homogeneous phenomenon, a fact often neglected in modelling consumer price inflation. This study, the first of its kind for an emerging market country, investigates gains to inflation forecast accuracy by aggregating weighted forecasts of the sub-component price indices,...

Models for the twelve-month-ahead US rate of inflation, measured by the chain weighted consumer expenditure deflator, are estimated for 1974-99 and subsequent pseudo out-of-sample forecasting performance is examined. Alternative forecasting approaches for different information sets are compared...

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...

We evaluate various economic modelsí relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the modelsí relative performance can be varying over time. We show that the modelsí relative performance has,...

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and...