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on Forecasting |
| By: | Senra, Eva; Espasa Terrades, Antoni |
| Abstract: | The Bulletin of EU & US Inflation and Macroeconomic Analysis (BIAM) is a monthly publication that has been reporting real time analysis and forecasts for inflation and other macroeconomic aggregates for the Euro Area, the US and Spain since 1994. The BIAM inflation forecasting methodology stands on working with useful disaggregation schemes, using leading indicators when possible and applying outliers' correction. The paper relates this methodology to corresponding topics in the literature and discusses the design of disaggregation schemes. It concludes that those schemes would be useful if they were formulated according to economic, institutional and statistical criteria aiming to end up with a set of components with very different statistical properties for which valid single-equation models could be built. The BIAM assessment, which derives from a new observation, is based on (a) an evaluation of the forecasting errors (innovations) at the components' level. It provides information on which sectors they come from and allows, when required, for the appropriate correction in the specific models. (b) In updating the path forecast with its corresponding fan chart. Finally, we show that BIAM real time Euro Area inflation forecasts compare successfully with the consensus from the ECB Survey of Professional Forecasters, one and two years ahead. |
| Keywords: | Outliers; Indirect forecast; Disaggregation |
| JEL: | C13 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:24678&r=for |
| By: | Jaqueson K Galimberti (KOF Swiss Economic Institute, ETH Zurich, Switzerland) |
| Abstract: | We evaluate the usefulness of satellite-based data on nighttime lights for the prediction of annual GDP growth across a global sample of countries. Going beyond traditional measures of luminosity, such as the sum of lights within a country’s borders, we propose several innovative distribution- and location-based indicators attempting to extract new predictive information from the night lights data. Whereas our ?ndings are generally favorable to the use of the night lights data to improve the accuracy of simple autoregressive model-based forecasts, we also ?nd a substantial degree of heterogeneity across countries on the estimated relationships between light emissions and economic activity: individually estimated models tend to outperform pooled speci?cations, even though the latter provide more ef?cient estimates for out-of-sample forecasting. The estimation uncertainty affecting the country-speci?c estimates tends to be more pronounced for low and lower middle income countries. We conduct bootstrapped inference in order to evaluate the statistical signi?cance of our results. |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:17-427&r=for |
| By: | Oskar Knapik (Aarhus University and CREATES) |
| Abstract: | For risk management traders in the electricity market are mainly interested in the risk of negative (drops) or of positive (spikes) price jumps, i.e. the sellers face the risk of negative price jumps while the buyers face the risk of positive price jumps. Understanding the mechanism that drive extreme prices and forecasting of the price jumps is crucial for risk management and market design. In this paper, we consider the problem of the impact of fundamental price drivers on forecasting of price jumps in NordPool intraday market. We develop categorical time series models which take into account i) price drivers, ii) persistence, iii) seasonality of electricity prices. The models are shown to outperform commonly-used benchmark. The paper shows how crucial for price jumps forecasting is to incorporate additional knowledge on price drivers like loads, temperature and water reservoir level as well as take into account the persistence in the jumps occurrence process. |
| Keywords: | autoregressive order probit model, categorical time series, seasonality, electricity prices, Nord Pool power market, forecasting, autoregressive multinomial model, fundamental price drivers |
| JEL: | C1 C5 C53 Q4 |
| Date: | 2017–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2017-07&r=for |
| By: | Francesco Ravazzolo; Joaquin Vespignani |
| Abstract: | In this paper we propose a new indicator of monthly global real economic activity, named world steel production. We use world steel production, OECD industrial production index and Kilian’s rea index to forecast world real GDP, and key commodity prices. We find that world steel production generates large statistically significant gains in forecasting world real GDP and oil prices, relative to an autoregressive benchmark. A forecast combination of the three indices produces statistically significant gains in forecasting world real GDP, oil, natural gas, gold and fertilizer prices, relative to an autoregressive benchmark. |
| Keywords: | Global real economic activity, World steel production, Forecasting |
| JEL: | E1 E3 C1 C5 C8 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2017-42&r=for |
| By: | Stella Moisan (Universidad de Talca, Chile); Rodrigo Herrera (Universidad de Talca, Chile); Adam Clements (QUT) |
| Abstract: | A methodology based on a system of dynamic multiple linear equations is proposed that incorporates hourly, daily and annual seasonal characteristics to predict hourly pm2.5 pollution concentrations for 11 meteorological stations in Santiago, Chile. It is demonstrated that the proposed model has the potential to match or even surpass the accuracy of other linear and nonlinear forecasting models in terms of fit and predictive ability. In addition, the model is successful in predicting various categories of high concentration events, up to 76% of mid-range and 100% of extreme-range events as an average across all stations. This forecasting model is considered a useful tool for government authorities to anticipate critical episodes of air quality so as to avoid the detrimental impacts economic and health impacts of extreme pollution levels. |
| Keywords: | Air quality, Particulate matter, Dynamic multiple equations |
| Date: | 2017–04–11 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2017_01&r=for |
| By: | Simionescu, Mihaela |
| Abstract: | The main aim of this paper is to provide forecast intervals for inflation and unemployment rate in Romania, bringing methodological novelties in the construction and evaluation of the prediction intervals. Considering the period 2004-2017 as forecast horizon, only few intervals included the registered values on the variables, but in the last stage when all the prior information has been used, the forecast intervals were very short. The proposed Bayesian technique for assessing prediction intervals was better than traditional approaches based on statistic tests. |
| Keywords: | forecast interval,Bayesian interval,inflation,unemployment |
| JEL: | C11 C13 C53 E37 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:82&r=for |