Error measures for the evaluation of forecasts are usually based on the size of the forecast errors. Common measures are e.g. the Mean Squared Error (MSE), the Mean Absolute Deviation (MAD) or the Mean Absolute Percentage Error (MAPE). Alternative measures for the comparison of forecasts are...

This paper applies combining forecasts of air travel demand generated from the same model but over different estimation windows. The combination approach used resorts to Pesaran and Pick (2011), but the empirical application is extended in several ways. The forecasts are based on a seasonal...

In this paper we use 4 different time series models to forecast sales in a goods management system. We use a variety of forecast combining techniques and measure the forecast quality by applying symmetric and asymmetric forecast quality measures. Simple, rank-, and criteria-based combining...

Most of the literature on combination of forecasts deals with the assumption of unbiased individual forecasts. Here, we consider the case of biased forecasts and discuss two different combination techniques resulting in an unbiased forecast. On the one hand we correct the individual forecasts,...

We analyze macroeconomic data using univariate and multivariate forecast combining techniques. We simulate forecast errors with different variance-covariance structures. The simulations are used to compare the performance of univariate and multivariate combining techniques.

We simulate forecast errors with different variance-covariance structures based on macroeconomic data. The simulations are used to compare the performance of different forecast combining techniques.

If there are various forecasts for the same random variable, it is common practice to combine these forecasts in order to obtain a better forecast. But an important question is how to perform the combination, especially if the system under investigation is subject to structural changes and...

We use the Pitman-closeness criterion to evaluate the performance of multivariate forecasting methods and we also calculate optimal matrices of weights for the linear combination of multivariate forecasts. These weights are identical with the optimal weights under the matrix-MSE criterion.

The result of Clemen (1986) shows that the combination of unbiased forecasts by means of a LS-regression model without an intercept and with the constraint that coefficients sum to one gives less spread prediction than the general regression model. Here the result is generalized for the M...

The paper analyzes the circumstances in which the combination of forecasts yields better results than the use of the forecasts separately. We propose a method of combining forecasts based on their efficiency on long and medium-term using as benchmarks the combination of forecasts based on...