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Literaturverz. S. 9 - 10

AbstractThis article presents new results which should be useful in finding production decisions while solving the dynamic lot sizing problem of Wagner–Whitin on a rolling horizon basis. In a rolling horizon environment, managers obtain decisions for the first period (or the first few periods) by looking at the forecasts for several periods. This article develops procedures to find optimal decisions for any specified number of initial periods (called planning horizon in the article) by using the forecast data for the minimum possible number of future periods. Computational results comparing these procedures with the other procedures reported in the literature are very encouraging.

In this paper it is advocated to select a model only if it significantly contributes to the accuracy of a combined forecast. Using hold-out-data forecasts of individual models and of the combined forecast, a useful test for equal forecast accuracy can be designed. An illustration for real-time forecasts for GDP in the Netherlands shows its ease of use.

Literaturverz. S. 8 - 9

Effective determination of trends in sulfur dioxide emissions facilitates national efforts to draft an appropriate policy that aims to lower sulfur dioxide emissions, which is essential for reducing atmospheric pollution. However, to reflect the current situation, a favorable emission reduction policy should be based on updated information. Various forecasting methods have been developed, but their applications are often limited by insufficient data. Grey system theory is one potential approach for analyzing small data sets. In this study, an improved modeling procedure based on the grey system theory and the mega-trend-diffusion technique is proposed to forecast sulfur dioxide emissions in China. Compared with the results obtained by the support vector regression and the radial basis function network, the experimental results indicate that the proposed procedure can effectively handle forecasting problems involving small data sets. In addition, the forecast predicts a steady decline in China&rsquo ; s sulfur dioxide emissions. These findings can be used by the Chinese government to determine whether its current policy to reduce sulfur dioxide emissions is appropriate.

Effective determination of trends in sulfur dioxide emissions facilitates national efforts to draft an appropriate policy that aims to lower sulfur dioxide emissions, which is essential for reducing atmospheric pollution. However, to reflect the current situation, a favorable emission reduction policy should be based on updated information. Various forecasting methods have been developed, but their applications are often limited by insufficient data. Grey system theory is one potential approach for analyzing small data sets. In this study, an improved modeling procedure based on the grey system theory and the mega-trend-diffusion technique is proposed to forecast sulfur dioxide emissions in China. Compared with the results obtained by the support vector regression and the radial basis function network, the experimental results indicate that the proposed procedure can effectively handle forecasting problems involving small data sets. In addition, the forecast predicts a steady decline in China's sulfur dioxide emissions. These findings can be used by the Chinese government to determine whether its current policy to reduce sulfur dioxide emissions is appropriate.

"This essay argues that experience from more than three decades of labour market forecasting shows that forecasting helps greasing the wheels of labour markets. Applied correctly - not in the sense of old fashioned manpower planning models - sufficiently disaggregated employment outlooks support individuals in making better informed decisions on human capital investments, guide policy makers, and alert firms of upcoming skill shortages. That forecasts are necessary at all follows mainly from nowadays widely acknowledged market failure arguments." (author's abstract)

The objective of this paper is to analyze the effects of uncertainty on density forecasts of stationary linear univariate ARMA models. We consider three specific sources of uncertainty: parameter estimation, error distribution, and lag order. Depending on the estimation sample size and the forecast horizon, each of these sources may have different effects. We consider asymptotic, Bayesian, and bootstrap procedures proposed to deal with uncertainty and compare their finite sample properties. The results are illustrated constructing fan charts for UK inflation. ; We thank the Spanish Government, research projects ECO2015–237033–C2–2–R and ECO2015–65701–P(MINECO/FEDER), for financial suppor

"The labour-market policy-mix in Germany is increasingly being decided on
a regional level. This requires additional knowledge about the regional development
which (disaggregated) national forecasts cannot provide.
Therefore, we separately forecast employment for the 176 German labour-
market districts on a monthly basis. We first compare the prediction
accuracy of standard time-series methods: autoregressive integrated
moving averages (ARIMA), exponentially weighted moving averages
(EWMA) and the structural-components approach (SC) in these small spatial
units. Second, we augment the SC model by including autoregressive
elements (SCAR) in order to incorporate the influence of former periods of
the dependent variable on its current value. Due to the importance of spatial
interdependencies in small labour-market units, we further augment
the basic SC model by lagged values of neighbouring districts in a spatial
dynamic panel (SCSAR).
The prediction accuracies of the models are compared using the mean absolute
percentage forecast error (MAPFE) for the simulated out-of-sample
forecast for 2005. Our results show that the SCSAR is superior to the
SCAR and basic SC model. ARIMA and EWMA models perform slightly better
than SCSAR in many of the German labour-market districts. This reflects
that these two moving-average models can better capture the trend
reversal beginning in some regions at the end of 2004. All our models
have a high forecast quality with an average MAPFE lower than 2.2 percent." [authors abstract]

"We forecast unemployment for the 176 German labour-market districts on a monthly basis. Because of their small size, strong spatial interdependencies exist between these regional units. To account for these as well as for the heterogeneity in the regional development over time, we apply different versions of an univariate spatial GVAR model. When comparing the forecast precision with univariate time-series methods, we find that the spatial model does indeed perform better or at least as well. Hence, the GVAR model provides an alternative or complementary approach to commonly used methods in regional forecasting which do not consider regional interdependencies." (author's abstract)