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on Forecasting |
| By: | Eva A. Arnold (Universität Hamburg, School of Business, Economics and Social Sciences, Department of Socioeconomics) |
| Abstract: | This paper aims at evaluating individual expectation accuracy of professional forecasters for 57 U.S., European, and German macroeconomic indicators over the period 1999-2010. The empirical analysis shows that initial announcements are partly considerably revised, and that some revisions occur systematically. Taking into account whether announcements are revised systematically and whether economists (assumingly) aim at forecasting the initial release or the latest revision, significant differences can be observed with regard to forecasters’ expectation errors. In general, forecasters that are (assumingly) aiming to predict the latest revisions of German indicators are able to form better forecasts if these indicators are revised systematically. Though to a lower extent, this relationship is also observable regarding U.S. indicators. Forecasters’ disagreement about fundamentals is higher during recessions and when stock markets are volatile. |
| Keywords: | Rational expectations; Macroeconomic indicators; Disagreement; Survey analysis; Real-time data |
| JEL: | D81 D84 E17 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:nbp:nbpmis:153&r=for |
| By: | Dalu Zhang (University of East Anglia); Peter Moffatt (University of East Anglia) |
| Abstract: | This paper examines the existence of time series non-linearity in the real output growth / recession-term spread relationship. Vector Autoregression (VAR), Threshold VAR (TVAR), Structural break VAR (SBVAR), Structural break threshold VAR (SBTVAR) are applied in the analysis. The in-sample results indicate there are non-linear components in this relationship. And this non-linearity tend to be caused by structural breaks. The best in-sample model also shows its robustness on arrival of new information in the out-of-sample tests. We find evidence the model with only structural break non-linearity outperform linear models in 1-quarter, 3-quarter and 4-quarter ahead forecasting. |
| Date: | 2013–06 |
| URL: | http://d.repec.org/n?u=RePEc:uea:aepppr:2012_47&r=for |
| By: | Brown, Molly; Higgins, Nathaniel; Hintermann, Beat |
| Abstract: | In this article we specify a model of millet prices in the three West African countries of Burkina Faso, Mali, and Niger. Using data obtained from USAID’s Famine Early Warning Systems Network (FEWS NET) we present a unique regional cereal price forecasting model that takes advantage of the panel nature of our data, and accounts for the flow of millet across markets. Another novel aspect of our analysis is our use of the Normalized Difference Vegetation Index (NDVI) to detect and control for variation in conditions for productivity. The average absolute out-of-sample prediction error for 4- month-ahead millet prices is about 20 %. |
| Keywords: | Millet, cereal, West Africa, price forecasting, remote sensing, NDVI, regional panel data, Crop Production/Industries, Demand and Price Analysis, International Relations/Trade, O13, O18, Q11, Q13, Q17, R32, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:150714&r=for |
| By: | Dicembrino , Claudio; Trovato, Giovanni |
| Abstract: | Insights about electricity demand dynamics is fundamental for investment capacity, optimal energy policies, and a balanced electricity system. This paper presents an empirical analysis of the monthly Italian electricity demand since January 2001 to June 2012. In the first section we conduct the analysis of structural breaks in the electricity demand finding that the series has two structural breaks in August 2002 and August 2004 as market liberalization effects on consumption. In the second part of the paper we estimate demand price elasticities both for residential and industrial sector. As expected from the electricity economics literature concerning elasticities estimates, we find that the long run price and income elasticities are more price elastic than the short run both in industrial and residential consumption. In the third and last section, we compare two different forecasting models: the Hidden Markov Models (HMM) and the Holt Winters (H-W) seasonal smoothing method. Considering the Mean Absolute Percentage Error (MAPE), the HMM approach seems to show a superiority in forecasting the monthly electricity demand compared to the H-W methodology. |
| Keywords: | Electricity Demand, Price and Income Elasticity, Hidden Markov Models, Holt-Winters Seasonal Filter Smoothing |
| JEL: | C53 Q41 Q47 R21 |
| Date: | 2013–06–14 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:47653&r=for |
| By: | Patton, Douglas; Bergstrom, John; Moore, Rebecca; Covich, Alan |
| Keywords: | Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:150559&r=for |
| By: | Jérôme Massiani (Department of Economics, University of Venice Cà Foscari) |
| Abstract: | Stated Preferences are, together with Bass diffusion and, to a lesser extent, Total Cost of Ownership, the most popular methods to forecast the future diffusion of electric and alternative fuel vehicles. In this contribution, we compare the merits and limitations of SP relative to other methods. We also review the empirical results provided by SP surveys and assess their validity for modeling market diffusion. We also propose a meta-analysis-based Synthetic Utility Function that consolidates results across various studies and can be used, for simulation purpose, in a Discrete Choice Model context. Such an approach makes the simulation results less dependent of single surveys’ idiosyncrasies, and hence is helpful for the formulation of robust policy recommendations. |
| Keywords: | Stated Preferences, Alternative fuel vehicle, market diffusion |
| JEL: | C53 O33 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2013:12&r=for |
| By: | Rajsic, Predrag; Weersink, Alfons; Navabi, Alireza; Pauls, Peter |
| Abstract: | We provide a general framework for quantifying the effects of genotypic selection prediction accuracy and varying cost ratios of phenotyping to genotyping on the economic performance of genotypic selection relative to traditional phenotypic selection. Economic performance is measured using normalized average cost per unit of genetic gain. This method allows for comparing the cost-effectiveness of breeding strategies in terms of relative rather than absolute costs, and it also allows for comparing strategies with different budgets and different levels of genetic gain. We assess prediction accuracy as a function of trait heritability, number of quantitative trait loci (QTL) and training population size. In addition, we set up a method for determining the economically optimal size of the training population under varying cost scenarios for traits that differ with respect to heritability and the number of QTL. Our results provide generally applicable quantitative estimates of the economic performance of genotypic selection relative to the conventional phenotypic selection under a wide range of scenarios. The economic performance of genotypic selection declines with (1) trait heritability, (2) relative cost of genotyping, and (3) the number of QTL affecting the trait. The benefits of increasing the training population size tend to be higher for traits with low heritability and traits affected by a larger number of QTL. The economically optimal sizes of the training population tend to be larger than the sizes that are typically used in current plant breeding programs. |
| Keywords: | genotypic selection, phenotypic selection, marker assisted selection, genomic selection, genome wide selection, Crop Production/Industries, Production Economics, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies, Risk and Uncertainty, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:149806&r=for |
| By: | Juchems, Elizabeth M.; Schoengold, Karina; Brozovic, Nicholas |
| Abstract: | Previous research on water trading has focused on surface water trading and theoretical approaches to analyzing groundwater trading. Empirical analysis of groundwater trading is a new area of study due in part to the previous lack of recorded usage, trade data and binding constraints on groundwater use by landowners. Groundwater trading can help move water from low-value to high-value areas of use for the benefit of the participating traders and general public. The paper predicts participation in groundwater trading and the directions of trades among participants. Specifically, the paper considers both formal and informal trading of groundwater used for crop irrigation purposes and attempts to identify those characteristics that predict the probability of trade participation and whether an individual is a buyer or seller of groundwater rights. Results from this research indicate a strong desire to participate in trades, but high transactions costs have limited the number of trades that have occurred. Utilizing empirical models improves the accuracy of predicting trade participation and direction, and therefore the accuracy of models of trade effects on water supplies and stream flows used in policy and decision making. |
| Keywords: | Land Economics/Use, Research and Development/Tech Change/Emerging Technologies, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:150511&r=for |