To Combine Forecasts or to Combine Information?

When the objective is to forecast a variable of interest but with many explanatory variables available, one could possibly improve the forecast by carefully integrating them. There are generally two directions one could proceed: combination of forecasts (CF) or combination of information (CI). CF combines forecasts generated from simple models each incorporating a part of the whole information set, while CI brings the entire information set into one super model to generate an ultimate forecast. Through linear regression analysis and simulation, we show the relative merits of each, particularly the circumstances where forecast by CF can be superior to forecast by CI, when CI model is correctly specified and when it is misspecified, and shed some light on the success of equally weighted CF. In our empirical application on prediction of monthly, quarterly, and annual equity premium, we compare the CF forecasts (with various weighting schemes) to CI forecasts (with principal component approach mitigating the problem of parameter proliferation). We find that CF with (close to) equal weights is generally the best and dominates all CI schemes, while also performing substantially better than the historical mean.Equally weighted combination of forecasts, Equity premium, Factor models, Fore- cast combination, Forecast combination puzzle, Information sets, Many predictors, Principal components, Shrinkage

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance

How to combine diagrammatic logics

This paper is a submission to the contest: How to combine logics? at the World Congress and School on Universal Logic III, 2010. We claim that combining "things", whatever these things are, is made easier if these things can be seen as the objects of a category. We define the category of diagrammatic logics, so that categorical constructions can be used for combining diagrammatic logics. As an example, a combination of logics using an opfibration is presented, in order to study computational side-effects due to the evolution of the state during the execution of an imperative program

Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?

To combine or not to combine? Though not a question of the same gravity as the Shakespeare’s to be or not to be, it is examined in this paper in the context of a hybrid neuro-fuzzy pattern classifier design process. A general fuzzy min-max neural network with its basic learning procedure is used within six different algorithm independent learning schemes. Various versions of cross-validation, resampling techniques and data editing approaches, leading to a generation of a single classifier or a multiple classifier system, are scrutinised and compared. The classification performance on unseen data, commonly used as a criterion for comparing different competing designs, is augmented by further four criteria attempting to capture various additional characteristics of classifier generation schemes. These include: the ability to estimate the true classification error rate, the classifier transparency, the computational complexity of the learning scheme and the potential for adaptation to changing environments and new classes of data. One of the main questions examined is whether and when to use a single classifier or a combination of a number of component classifiers within a multiple classifier system

Communities' Satisfaction toward Housing Rehabilitation and Reconstruction Program after 30 September 2009 Earthquake in West Sumatra Province

The government of Indonesia had provided the Rehabilitation and Reconstruction (RR) assistance to 194,636 houses for communities after 30 September 2009 earthquake in West Sumatra province. The community-based development model was adopted in this housing program aid. This study aims to explore into what extent people's satisfaction with the RR program and to identify the difference of satisfaction level between urban area and rural area. The research was carried out by conducting a questionnaire survey to 200 communities. The beneficiaries were invited to determine their satisfaction level based on 5 Likert scale from 1 as ‘not satisfied at all’ to 5 as ‘absolutely satisfied’. The satisfaction level was assessed by two factors, process of reconstruction and result of reconstruction. It was found that the level of communities’ satisfaction is at average level. The satisfaction level of urban communities is 2.75; while the rural communities is slightly higher at 2.88. The satisfaction level of urban and rural communities with the reconstruction process are at 2.63 and 2.75 respectively; while the satisfaction level with the results of the program is at 2.84 and at 2.95 respectively. Communities’ satisfaction with the result of reconstruction is higher than that with the process of reconstruction. Keywords: post-disaster reconstruction, housing reconstruction, community-based, satisfaction level