|
on Forecasting |
| By: | Christopher G. Gibbs (School of Economics, UNSW Business School, UNSW); Andrey L. Vasnev (University of Sydney) |
| Abstract: | In applied forecasting, there is a trade-off between in-sample fit and out-of-sample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, optimal combination weights should minimize conditional mean squared error, or a conditional loss function, rather than the unconditional variance as in the commonly used framework of Bates and Granger (1969). We prove that our conditionally optimal weights lead to better forecast performance. The conditionally optimal weights support other forward-looking approaches to combining forecasts, where the forecast weights depend on the expected model performance. We show that forward-looking |
| Keywords: | Forecast combination, conditionally optimal weights, forecast combination puzzle, inflation, Phillips curve |
| JEL: | C18 C53 E31 |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:swe:wpaper:2017-10&r=for |
| By: | Florian Ziel; Rick Steinert |
| Abstract: | The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity prices for a short period of time provided new opportunities for market participants. However, the electricity price literature seem to be very scarce on the issue of medium- to long-term price forecasting, which is mandatory for investment and political decisions. Our paper closes this gap by introducing a new approach to simulate electricity prices with hourly resolution for several months up to three years. Considering the uncertainty of future events we are able to provide probabilistic forecasts which are able to detect probabilities for price spikes even in the long-run. As market we decided to use the EPEX day-ahead electricity market for Germany and Austria. Our model extends the X-Model which mainly utilizes the sale and purchase curve for electricity day-ahead auctions. By applying our procedure we are able to give probabilities for the due to the EEG practical relevant event of six consecutive hours of negative prices. We find that using the supply and demand curve based model in the long-run yields realistic patterns for the time series of electricity prices and leads to promising results considering common error measures. |
| Date: | 2017–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1703.10806&r=for |
| By: | Alexey Porshakov (Bank of Russia, Russian Federation); Elena Deryugina (Bank of Russia, Russian Federation); Alexey Ponomarenko (Bank of Russia, Russian Federation); Andrey Sinyakov (Bank of Russia, Russian Federation) |
| Abstract: | Real-time assessment of quarterly GDP growth rates is crucial for evaluating an economy's current prospects given that the relevant data are normally subject to substantial delays in publication by the national statistical agencies. Large information sets of real-time indicators which could be used to approximate GDP growth rates in the quarter of interest are characterized by unbalanced data, mixed frequencies, systematic data revisions, as well as a more general curse of dimensionality problem. The latter issues could, however, be practically resolved by means of dynamic factor model-ing, which has recently been recognized as a useful tool to evaluate current economic conditions by means of higher frequency indicators. Our main results show that the performance of dynamic factor models in predicting Russian GDP dynamics appears to be superior to other common alternative specifications. At the same time, we empirically show that the arrival of new data seems to consistently improve DFM’s predictive accuracy throughout sequential nowcast vintages. We also intro-duce an analysis of nowcast evolution resulting from the gradual expansion of the dataset of explanatory variables, as well as the framework for estimating contributions of different blocks of predictors into nowcasts of Russian GDP. |
| Keywords: | GDP nowcast, dynamic factor models, principal components, Kalman filter, nowcast evolution |
| JEL: | C53 C82 E17 |
| URL: | http://d.repec.org/n?u=RePEc:bkr:wpaper:wps2&r=for |
| By: | Chakravartti, Parma (National Institute of Public Finance and Policy); Mundle, Sudipto (National Institute of Public Finance and Policy) |
| Abstract: | Building on the early work of Mitchell and Burns (1938,1946), the automatic leading indica-tor (ALI) approach has been developed over the last few decades by Geweke (1977), Sargent and Sims (1977), Stock and Watson (1988), Camba-Mendez et al. (1999) , Mongardini and Sedik (2003), Duo-Qin et al. (2006), Grenouilleau (2006) and others. It has come to be widely accepted as one of the most effective methods for macroeconomic forecasting. This paper uses the ALI approach to forecast aggregate and sectoral GDP growth for 2016-17. The approach uses a dy-namic factor model (DFM) in the form of state space representation to extract factors from a pool of variables and then the factors are incorporated into a VAR model to generate the forecast series. Three alternate models have been tried: demand side, supply side and combined model. The model with the lowest RMSE is selected for the forecast. Real GDP growth is forecast at 6.7% for 2016-17 without factoring in the impact of demonetisation. Incorporating that impact reduces the forecast to 6.1%. |
| Keywords: | Growth Rate ; Forecasting ; Automatic Leading Indicator ; Dynamic Factor Model ; Agriculture ; Industry ; Services ; GDP ; Demonetization |
| JEL: | C32 C5 O4 |
| Date: | 2017–03 |
| URL: | http://d.repec.org/n?u=RePEc:npf:wpaper:17/193&r=for |