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on Forecasting |
| By: | Libero Monteforte (Bank of Italy); Gianluca Moretti (Bank of Italy) |
| Abstract: | We present a mixed-frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real-time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed-frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. |
| Keywords: | forecasting inflation, real time forecasts, dynamic factor models, MIDAS regression, economic derivatives |
| JEL: | C13 C51 C53 E37 G19 |
| Date: | 2010–07 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_767_10&r=for |
| By: | Mehmet Balcilar (Eastern Mediterranean University); Rangan Gupta (University of Pretoria); Anandamayee Majumdar (Arizona State University); Stephen M. Miller (University of Connecticut and University of Nevada, Las Vegas) |
| Abstract: | This paper provides out-of-sample forecasts of Nevada gross gaming revenue and taxable sales using a battery of linear and non-linear forecasting models and univariate and multivariate techniques. The linear models include vector autoregressive and vector error-correction models with and without Bayesian priors. The non-linear models include non-parametric and semi-parametric models, smooth transition autoregressive models and artificial neural network autoregressive models. In addition to gross gaming revenue and taxable sales, we employ recently constructed coincident and leading employment indexes for Nevada's economy. We conclude that non-linear models generally outperform linear models in forecasting future movements in gross gaming revenue and taxable sales. |
| Keywords: | Forecasting, Linear and non-linear models, Nevada gross gaming revenue, Nevada taxable sales |
| JEL: | C32 R31 |
| Date: | 2010–07 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2010-21&r=for |
| By: | Paolo Angelini (Bank of Italy); Giovanni Guazzarotti (Bank of Italy) |
| Abstract: | Recent theoretical papers suggest that high uncertainty about firms’ economic prospects can explain delays in the adjustment of their stock prices to economic news. Using analyst forecast revisions and earnings announcements as proxies of news, we find mixed evidence in support of this hypothesis. We confirm that stocks of firms whose prospects are highly uncertain display a relatively large delayed price reaction (so-called continuation) after the release of news, but we argue that this evidence does not necessarily imply a slower adjustment speed. Indeed, for these stocks the immediate reaction to news is also relatively strong. In fact, the magnitude of the delayed price reaction (the price continuation) depends both on the degree of price sluggishness and on the “scale” of the news hitting the stock. We therefore consider both the delayed and immediate responses, and compute measures of adjustment speed that do not depend on the “scale” of the news. We then compare these measures across portfolios of stocks characterized by different degrees of uncertainty. Our findings indicate that: (i) stock prices characterized by high uncertainty tend to adjust to bad news more sluggishly than those characterized by low uncertainty; (ii) the opposite holds true in the case of good news; (iii) stock prices characterized by high uncertainty tend to adjust to bad news more sluggishly than to good news. Previous empirical literature focuses on price continuation patterns but neglects to control for the “scale” of the news, reaching erroneous conclusions. |
| Keywords: | stock price continuation, price adjustment speed, news, earnings announcements, analysts forecasts, post-earnings announcement drift, post-analyst forecast revisions drift, managers incentives |
| JEL: | G11 G14 |
| Date: | 2010–07 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_765_10&r=for |
| By: | Voudouris, V; Di Maio , C |
| Abstract: | he ACEGES (Agent-based Computational Economics of the Global Energy System) 1.0 model is an agent-based model of conventional oil production for 93 countries. The model accounts for four key uncertainties, namely Estimated Ultimate Recovery (EUR), estimated growth in oil demand, estimated growth in oil production and assumed peak/decline point. This documentation provides an overview of the ACEGES model capabilities and an example of how it can be used for long-term (discrete and continuous) scenarios of conventional oil production. |
| Keywords: | oil production; ACEGES; agent-based model; energy scenarios; oil forecasting |
| JEL: | Q41 C14 C63 C1 |
| Date: | 2010–08–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:24269&r=for |