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on Forecasting |
| By: | Marine Carrasco; Barbara Rossi |
| Abstract: | This paper considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension reduction devices: principal components, Ridge, Landweber Fridman, and Partial Least Squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross- validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods out- perform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors' forecasting ability. |
| Keywords: | Forecasting, regularization methods, factor models, Ridge, partial least squares, principal components, sparsity, large datasets, variable selection, GDP forecasts, inflation forecasts |
| JEL: | C22 C52 C53 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1530&r=for |
| By: | Hua Liao; Jia-Wei Cai; Dong-Wei Yang; Yi-Ming Wei (Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology) |
| Abstract: | Medium-to-long term energy prediction plays a widely-acknowledged role in guiding national energy strategy and policy but could also lead to serious economic and social chaos when poorly executed. A consequent issue may be the effectiveness of these predictions, and sources that errors can be traced back to. The International Energy Agency (IEA) has published its annual World Energy Outlook (WEO) concerning energy demand based on its long term world energy model (WEM) under specific assumptions towards uncertainties such as population, macro economy, energy price and technology etc. Unfortunately, some of its predictions succeeded while others failed. We in this paper attempts to decompose the leading source of these errors quantitatively. Results suggest that GDP acts as the leading source of demand forecasting errors while fuel price comes thereafter, which requires extra attention in forecasting. Gas, among all fuel types witness the most biased projections. Ignoring the catch-up effect of acquiring rapid economic growth in developing countries such as China will lead to huge mistake in predicting global energy demand. Finally, asymmetric cost of under- and over-estimation of GDP suggests a potentially less conservative stance in the future. |
| Keywords: | energy demand; Medium-to-long term prediction; forecast error; social development |
| JEL: | Q54 Q40 |
| Date: | 2016–04–15 |
| URL: | http://d.repec.org/n?u=RePEc:biw:wpaper:92&r=for |
| By: | Albarrán, Irene; Marín, J. Miguel; Alonso, Pablo J.; Benchimol, A. |
| Abstract: | Forecasting mortality rates has become a key task for all who are concerned with payments for non-active people, such as Social Security or life insurance firms managers. The non-ending process of reduction in the mortality rates is forcing to continuously improve the models used to project these variables. Traditionally, actuaries have selected just one model, supposing that this model were able to generate the observed data. Most times the results have driven to a set of questionable decisions linked to those projections. This way to act does not consider the model uncertainty when selecting a specific one. This drawback can be reduced through model assembling. This technique is based on using the results of a set of models in order to get better results. In this paper we introduce two approaches to ensemble models: a classical one, based on the Akaike information criterion (AIC), and a Bayesian model averaging method. The data are referred to a Spanish male population and they have been obtained from the Human Mortality Database. We have used four of the most widespread models to forecast mortality rates (Lee-Carter, Renshaw-Haberman, Cairns-Blake-Dowd and its generalization for including cohort effects) together with their respective Bayesian specifications. The results suggest that using assembling models techniques gets more accurate predictions than those with the individual models. |
| Keywords: | Renshaw-Haberman model; projected life tables; longevity risk; Lee-Carter model; Cairns-Blake-Dowd model; bootstrap; Bayesian model averaging; AIC model averaging |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:23434&r=for |