|
on Forecasting |
| By: | Sebastian J. Dietz; Philipp Kneringer; Georg J. Mayr; Achim Zeileis |
| Abstract: | Low-visibility conditions at airports can lead to capacity reductions and therefore to delays or cancelations of arriving and departing flights. Accurate visibility forecasts are required to keep the airport capacity as high as possible. We generate probabilistic nowcasts of low-visibility procedure (lvp) states, which determine the reduction of the airport capacity due to low-visibility. The nowcasts are generated with tree-based statistical models based on highly-resolved meteorological observations at the airport. Short computation times of these models ensure the instantaneous generation of new predictions when new observations arrive. The tree-based ensemble method "boosting" provides the highest benefit in forecast performance. For lvp forecasts with lead times shorter than one hour variables with information of the current lvp state, ceiling, and horizontal visibility are most important. With longer lead times visibility information of the airport's vicinity, humidity, and climatology also becomes relevant. |
| Keywords: | aviation meteorology, visibility, nowcast, decision tree, bagging, random forest, boosting |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:inn:wpaper:2017-22&r=for |
| By: | Cobb, Marcus P A |
| Abstract: | Abstract In terms of aggregate accuracy, whether it is worth the effort of modelling a disaggregate process, instead of forecasting the aggregate directly, depends on the properties of the data. Forecasting the aggregate directly and forecasting each of the components separately, however, are not the only options. This paper develops a framework to forecast an aggregate that dynamically chooses groupings of components based on the properties of the data to benefit from both the advantages of aggregation and disaggregation. With this objective in mind, the dimension of the problem is reduced by selecting a subset of possible groupings through the use of agglomerative hierarchical clustering. The definitive forecast is then produced based on this subset. The results from an empirical application using CPI data for France, Germany and the UK suggest that the grouping methods can improve both aggregate and disaggregate accuracy. |
| Keywords: | Forecasting economic aggregates; Bottom-up forecasting; Hierarchical forecasting; Hierarchical Clustering; |
| JEL: | C38 C53 E37 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:81585&r=for |
| By: | Nobuyuki Hanaki (Université Nice Sophia Antipolis; GREDEG-CNRS; IUF); Eizo Akiyama (University of Tsukuba, Japan); Ryuichiro Ishikawa (University of Tsukuba, Japan) |
| Abstract: | In this study, we investigate (a) whether eliciting future price forecasts influences market outcomes and (b) whether differences in the way in which subjects are incentivized to submit ``accurate'' price forecasts influence market outcomes as well as the forecasts in an experimental asset market. We consider four treatments: one without forecast elicitation and three with forecast elicitation. In two of the treatments with forecast elicitation, subjects are paid based on their performance in both forecasting and trading, while in the other treatment with forecast elicitations, they are paid based on only one of those factors, which is chosen randomly at the end of the experiment. We found no significant effect of forecast elicitation on market outcomes in the latter case. Thus, to avoid influencing the behavior of subjects and market outcomes by eliciting price forecasts, paying subjects based on either forecasting or trading performance is better than paying them based on both. |
| Keywords: | Price forecast elicitation, Experimental asset markets |
| JEL: | B41 B26 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2017-26&r=for |
| By: | Clark, Todd E. (Federal Reserve Bank of Cleveland); McCracken, Michael W. (Federal Reserve Bank of St. Louis); Mertens, Elmar (Bank for Inernational Settlements) |
| Abstract: | We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee’s Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts. |
| Keywords: | Stochastic volatility; survey forecasts; fan charts; |
| JEL: | C53 E37 |
| Date: | 2017–09–25 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwp:1715&r=for |
| By: | Niematallah Elamin (Graduate School of Economics, Osaka University); Mototsugu Fukushige (Graduate School of Economics, Osaka University) |
| Abstract: | This paper presents an interaction forecasting framework with a focus on short-term load forecasting. It proposes a seasonal autoregressive integrated moving average model with the inclusion of exogenous variables (SARIMA: main effects) and interaction variables (cross effects) to forecast short-term electricity load using hourly load data from Tokyo Electric Power Company. The main effects and cross effects are measured through an iterative process of plotting, interpreting, and testing. Interactions of weather variables and calendar variables, as well as interactions of seasonal patterns and intraday dependencies, are analyzed, tested, and added to the model. We compare the SARIMAX model, which contains only main effects, with the Interaction-SARIMAX model, which includes cross effects in addition to the main effects. Our proposed SARIMAX-with-interactions model is shown to produce smaller errors than its competitors. Thus, including interaction effects of the exogenous variables into the SARIMAX model can potentially improve the model forecasting performance. Although the model is built using data of a specific region in Japan, the method is completely generic and therefore applicable to any load forecasting problem. |
| Keywords: | Cross effects, forecast accuracy, load forecasting, load modeling, SARIMAX |
| JEL: | C53 Q4 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:osk:wpaper:1728&r=for |
| By: | Philipp Kneringer; Sebastian J. Dietz; Georg J. Mayr; Achim Zeileis |
| Abstract: | Airport operations are sensitive to visibility conditions. Low-visibility events may lead to capacity reduction, delays and economic losses. Different levels of low-visibility procedures (lvp) are enacted to ensure aviation safety. A nowcast of the probabilities for each of the lvp categories helps decision makers to optimally schedule their operations. An ordered logistic regression (OLR) model is used to forecast these probabilities directly. It is applied to cold season forecasts at Vienna International Airport for lead times of 30-min out to two hours. Model inputs are standard meteorological measurements. The skill of the forecasts is accessed by the ranked probability score. OLR outperforms persistence, which is a strong contender at the shortest lead times. The ranked probability score of the OLR is even better than the one of nowcasts from human forecasters. The OLR-based nowcasting system is computationally fast and can be updated instantaneously when new data become available. |
| Keywords: | aviation meteorology, low visibility, probabilistic nowcasting, statistical forecasts, ordered logistic regression |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:inn:wpaper:2017-21&r=for |
| By: | Frantisek Cech (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic); Jozef Barunik (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic) |
| Abstract: | This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogeneity among financial assets. Direct benefits of the proposed approach are revealed in the portfolio Value-at-Risk forecasting application, where our modeling strategy performs significantly better than several benchmark models according to both statistical and economic comparison. In particular Panel Quantile Regression Model for Returns consistently outperforms all the competitors in the 5% and 10% quantiles. Sound statistical performance translates directly into economic gains which is demonstrated in the Global Minimum Value-at-Risk Portfolio and Markowitz-like comparison. Overall results of our research are important for correct identification of the sources of systemic risk, and are particularly attractive for high dimensional applications. |
| Keywords: | panel quantile regression, realized measures, Value-at-Risk |
| JEL: | C14 C23 G17 G32 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2017_20&r=for |