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Combination Weight (combination + weight)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

Selected Abstracts

Combining forecasts using optimal combination weight and generalized autoregression,

JOURNAL OF FORECASTING, Issue 5 2008
Jeong-Ryeol Kurz-Kim
Abstract In this paper, we consider a combined forecast using an optimal combination weight in a generalized autoregression framework. The generalized autoregression provides not only a combined forecast but also an optimal combination weight for combining forecasts. By simulation, we find that short- and medium-horizon (as well as partly long-horizon) forecasts from the generalized autoregression using the optimal combination weight are more efficient than those from the usual autoregression in terms of the mean-squared forecast error. An empirical application with US gross domestic product confirms the simulation result. Copyright © 2008 John Wiley & Sons, Ltd. [source]

An Approximate Bayesian Algorithm for Combining Forecasts,

DECISION SCIENCES, Issue 3 2001
Kim-Hung Li
Abstract In this paper we propose a consensus forecasting method based on a convex combination of individual forecast densities. The exact Bayesian updating of the convex combination weights is very complex and practically prohibitive. We propose a simple sequential updating alternative method based on function approximation. Several examples illustrate the method. [source]

OPTIMAL FORECAST COMBINATION UNDER REGIME SWITCHING*

INTERNATIONAL ECONOMIC REVIEW, Issue 4 2005
Graham Elliott
This article proposes a new forecast combination method that lets the combination weights be driven by regime switching in a latent state variable. An empirical application that combines forecasts from survey data and time series models finds that the proposed regime switching combination scheme performs well for a variety of macroeconomic variables. Monte Carlo simulations shed light on the type of data-generating processes for which the proposed combination method can be expected to perform better than a range of alternative combination schemes. Finally, we show how time variations in the combination weights arise when the target variable and the predictors share a common factor structure driven by a hidden Markov process. [source]