|
on Forecasting |
| By: | Miller, Callum |
| Abstract: | Central banks need to forecast banknote demand. It determines the number of notes they need printed and the future distribution network required. Yet forecasting demand is an inherently complex problem - banknotes are anonymous bearer instruments and so many of the sources of demand are difficult to research. This paper sets out a framework for identifying and assessing drivers likely to influence banknote demand. It presents, for the first time, the findings from an econometric model, looking at the past relationship between demand for Bank of England notes and a range of economic variables and cash industry statistics, to help forecast future demand. But this approach has its limitations. There will be determinants of demand not included in the model. Furthermore, what is to say that past relationships will hold into the future? Perhaps we are now approaching a point of inflection - a paradigm shift in the demand for cash that causes the pre-existing relationships to break down. To account for this, central banks must continue to research cash demand, its current and future drivers, and how significant they might be going forward. They must look for leading indicators that suggest a break with the past, and attempt to understand how, and when, the impact of technological change may significantly change the trajectory of cash use. This paper will set out a structure for capturing all of this information and using it to make judgements on the future of cash. Whilst it might improve central bank’s forecasting capability, and thus the basis for policy decisions, it will not eliminate all uncertainty. Therefore, central banks must retain flexibility, and ensure the wider cash industry does as well. There is a future for cash but we must constantly be alert to events that might change what that future looks like. |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iccp17:162912&r=for |
| By: | Pierre Guérin (Bank of Canada); Danilo Leiva-Leon (Banco de España) |
| Abstract: | This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In the empirical application, we forecast U.S. business cycle turning points with statelevel employment data. We find that forecasts obtained with our best combination scheme provide timely updates of U.S. recessions in that they outperform a notoriously dicult benchmark to beat (the anxious index from the Survey of Professional Forecasters) for short-term forecasts. |
| Keywords: | business cycles, forecast combination, forecasting, Markov-switching, nowcasting |
| JEL: | C53 E32 E37 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1727&r=for |
| By: | Jörg Döpke (Hochschule Merseburg (University of Applied Sciences Merseburg)); Ulrich Fritsche (Universität Hamburg (University of Hamburg)); Gabi Waldhof (Leibniz-Institut für Agrarentwicklung in Transformationsökonomien) |
| Abstract: | The paper reports results of a survey among active forecasters of the German business cycle. Relying on 82 respondents from 37 different institutions, we investigate what models and theories forecasters subscribe to and find that they are pronounced conservative in the sense, that they overwhelmingly rely on methods and theories that have been well-established for a long time, while more recent approaches are relatively unimportant for the practice of business cycle forecasting. DSGE models are mostly used in public institutions. In line with findings in the literature there are tendencies of “leaning towards consensus†(especially for public institutions) and “sticky adjustment of forecasts†with regard to new information. We find little evidence that the behaviour of forecasters has changed fundamentally since the Great Recession but there are signs that forecast errors are evaluated more carefully. Also, a stable relationship between preferred theories and methods and forecast accuracy cannot be established. |
| Keywords: | Forecast error evaluation, questionnaire, survey, business cycle forecast, professional forecaster |
| JEL: | E32 E37 C83 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:hep:macppr:201701&r=for |
| By: | Zeya Zhang; Weizheng Chen; Hongfei Yan |
| Abstract: | This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we construct the features of news based on the positive polar of their words. In consideration of sequential stock prices and continuous news effects, we propose a recurrent neural network model to help predict stock prices. Compared to SVM classifier with price features, we find our proposed method has an over 5% improvement on stock prediction accuracy in experiments. |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1707.07585&r=for |