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on Forecasting |
| By: | David F. Hendry (Department of Economics, Oxford University, Manor Road Building, Manor Road, Oxford, OX1 3UQ, United Kingdom); Kirstin Hubrich (European Central Bank, Kaiserstrasse 29, Postfach 16 03 19, 60066 Frankfurt am Main, Germany.) |
| Abstract: | We suggest an alternative use of disaggregate information to forecast the aggregate variable of interest, that is to include disaggregate information or disaggregate variables in the aggregate model as opposed to first forecasting the disaggregate variables separately and then aggregating those forecasts or, alternatively, using only lagged aggregate information in forecasting the aggregate. We show theoretically that the first method of forecasting the aggregate should outperform the alternative methods in population. We investigate whether this theoretical prediction can explain our empirical findings and analyse why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of euro area and US inflation in some situations, but not in others. |
| Keywords: | Disaggregate information; predictability; forecast model selection; VAR; factor models |
| JEL: | C51 C53 E31 |
| Date: | 2006–02 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20060589&r=for |
| By: | Hilde C. Bjørnland (University of Oslo and Norges Bank (Central Bank of Norway)); Leif Brubakk (Norges Bank (Central Bank of Norway)); Anne Sofie Jore (Norges Bank (Central Bank of Norway)) |
| Abstract: | The output gap (measuring the deviation of output from its potential) is a crucial concept in the monetary policy framework, indicating demand pressure that generates inflation. The output gap is also an important variable in itself, as a measure of economic fluctuations. However, its definition and estimation raise a number of theoretical and empirical questions. This paper evaluates a series of univariate and multivariate methods for extracting the output gap, and compares their value added in predicting inflation. The multivariate measures of the output gap have by far the best predictive power. This is in particular interesting, as they use information from data that are not revised in real time. We therefore compare the predictive power of alternative indicators that are less revised in real time, such as the unemployment rate and other business cycle indicators. Some of the alternative indicators do as well, or better, than the multivariate output gaps in predicting inflation. As uncertainties are particularly pronounced at the end of the calculation periods, assessment of pressures in the economy based on the uncertain output gap could benefit from being supplemented with alternative indicators that are less revised in real time. |
| Keywords: | Output gap, real time indicators, forecasting, Phillips curve |
| JEL: | C32 E31 E32 E37 |
| Date: | 2006–03–17 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2006_02&r=for |
| By: | Áron Gereben (Magyar Nemzeti Bank); Klára Pintér (Magyar Nemzeti Bank) |
| Abstract: | Market analysts and central banks often use the implied volatility of FX options as an indicator of expected exchange rate uncertainty. The aim of our study is to investigate the limits of this statistic. We present some key factors that may deviate the value of implied volatility from the exchange rate variability expected by the market. These biasing factors are linked to the simplifying assumptions of the Black-Scholes option pricing model. Our empirical results show that forint/euro implied volatilities carry useful information about future exchange rate uncertainty when the forecast horizon is shorter than one month. However, implied volatility provides a biased estimate, and does not encompass the information included in other (GARCH, ARMA) predictors of volatility calculated from historical exchange rate data. These results are in line with the findings of similar analyses of other currency pairs. |
| Keywords: | option, volatility, exchange rate. |
| JEL: | G13 |
| Date: | 2005 |
| URL: | http://d.repec.org/n?u=RePEc:mnb:opaper:2005/39&r=for |
| By: | Crescenzio Gallo; Giancarlo De Stasio; Cristina Di Letizia |
| Abstract: | The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological “processing”. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to “suggest” how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non-linear models to do forecasting), while in multipopulation models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent. |
| Keywords: | artificial neural network, financial modelling, population model, market equilibrium. |
| JEL: | C53 C69 C90 D58 |
| Date: | 2006–01 |
| URL: | http://d.repec.org/n?u=RePEc:ufg:qdsems:02-2006&r=for |
| By: | Rosegrant, Mark W.; Cline, Sarah A.; Li, Weibo; Sulser, Timothy B.; Valmonte-Santos, Rowena A. |
| Abstract: | "Sub-Saharan Africa is the only developing region in the world where food insecurity has worsened instead of improved in recent decades. In this discussion paper, Mark W. Rosegrant, Sarah A. Cline, Weibo Li, Timothy B. Sulser, and Rowena A. Valmonte-Santos show that this discouraging trend need not be a blueprint for the future. The research contained in this discussion paper was conducted in preparation for the IFPRI 2020 Africa conference “Assuring Food and Nutrition Security in Africa by 2020: Prioritizing Actions, Strengthening Actors, and Facilitating Partnerships,” held in Kampala, Uganda, April 1–3, 2004. The authors examine the implications of several different policy scenarios based on IFPRI's International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT). This model, developed at IFPRI in the early 1990s, has been continually updated to incorporate more food sectors and geographic regions. In this paper, the authors use IMPACT to assess the consequences of a wide range of policy and investment choices for Africa, including a business as usual scenario (continuation of current policy and investment trends through 2025), a pessimistic scenario (declining trends in key investments and in agricultural productivity), and a vision scenario (improving trends in investments and hence in agricultural productivity and human capital), as well as scenarios for more effective use of rainfall in agriculture, reduced marketing margins, and three different scenarios for trade liberalization. The wide variation in results reveals how much these choices will matter. For example, the number of malnourished children under five years old in Sub-Saharan Africa in 2025 is projected to be 38.3 million under business as usual, 55.1 million under the pessimistic scenario, and 9.4 million under the vision scenario. It is our hope that this research will clarify the steps needed to help stimulate the actions contributing to approaching the vision scenario. " From Foreword by Joachim von Braun |
| Keywords: | Impact model ,Food insecurity ,Forecasting ,Agricultural productivity ,Human capital ,Malnutrition in children , |
| Date: | 2005 |
| URL: | http://d.repec.org/n?u=RePEc:fpr:2020dp:41&r=for |