
on Forecasting 
By:  Hendry, David F; Hubrich, Kirstin 
Abstract:  We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over 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; factor models; forecast model selection; predictability; VAR 
JEL:  C51 C53 E31 
Date:  2006–01 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:5485&r=for 
By:  Philippe HUBER (University of Geneva, HEC and FAME); Olivier SCAILLET (University of Geneva, HEC and FAME); MariaPia VICTORIAFESER (University of Geneva, HEC and FAME) 
Abstract:  In this paper we develop a structural equation model with latent variables in an ordinal setting which allows us to test brokerdealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a Laplace approximation, and show its consistency and asymptotic normality. Monte Carlo experiments reveal that both the estimation method and the testing procedure perform well in small samples. An empirical illustration is given for midterm forecasts simultaneously made by two brokerdealers for several countries. 
Keywords:  structural equation model, latent variable, generalised linear model, factor analysis, multinomial logit, forecasts, LAMLE, canonical correlation 
JEL:  C12 C13 C30 C51 C52 C53 G10 
Date:  2005–10 
URL:  http://d.repec.org/n?u=RePEc:fam:rpseri:rp159&r=for 
By:  Warwick J. McKibbin 
Abstract:  The world is in the midst of a significant demographic transition with important implications for the macroeconomic performance of the global economy. This paper summarizes the key features of the current and projected future demographic change that are likely to have macroeconomic effects. It then applies a new ten region global model (an extended version of the MSGCubed model) incorporating demographic dynamics, to examine the consequences of projected global demographic change on the world economy from 2005 to 2050. A distinction is made between the effects on each country of its own demographic transition and the effects on each country of the equally large demographic changes occurring in the rest of the world. 
Date:  2005–10 
URL:  http://d.repec.org/n?u=RePEc:pas:camaaa:200606&r=for 
By:  Hilke Brockmann (Max Planck Institute for Demographic Research, Rostock, Germany); Jutta Gampe (Max Planck Institute for Demographic Research, Rostock, Germany) 
Abstract:  Forecasts are always wrong. Still, they paint potential future scenarios and provide a platform for policy decisions today. This is what gives forecast such a high salience in political debates about the effects of population aging. The paper aims at gauging the effect of population aging on hospital expenses in Germany. We use a probabilistic forecast model comprising a stochastic demographic component that exploits historical mortality trends, a stochastic cost component based on typical hospital costs over the lifecourse, and a quality measure of medical progress, which builds on past advances in hospital treatment. Three different scenarios are constructing, yielding 3 important results. Firstly, there is an increase in overall hospital expenditure until the German baby boomers will die out (2040 to 2050). Secondly, the increase is comparably moderate because the average individual costs are likely to decline as elderly health improves and since medical progress has an ambiguous influence on hospital expenditures. Finally, the cost increase varies significantly by gender and disease. 
JEL:  J1 Z0 
Date:  2005–03 
URL:  http://d.repec.org/n?u=RePEc:dem:wpaper:wp2005007&r=for 
By:  Simone Manganelli (European Central Bank, Kaiserstrasse 29, Postfach 16 03 19, 60066 Frankfurt am Main, Germany.) 
Abstract:  This paper argues that forecast estimators should minimise the loss function in a statistical, rather than deterministic, way. We introduce two new elements into the classical econometric analysis: a subjective guess on the variable to be forecasted and a probability reflecting the confidence associated to it. We then propose a new forecast estimator based on a test of whether the first derivatives of the loss function evaluated at the subjective guess are statistically different from zero. We show that the classical estimator is a special case of this new estimator, and that in general the two estimators are asymptotically equivalent. We illustrate the implications of this new theory with a simple simulation, an application to GDP forecast and an example of meanvariance portfolio selection. 
Keywords:  Decision under uncertainty; estimation; overfitting; asset allocation 
JEL:  C13 C53 G11 
Date:  2006–01 
URL:  http://d.repec.org/n?u=RePEc:ecb:ecbwps:20060584&r=for 
By:  Gianni Amisano; Raffaella Giacomini 
Abstract:  We propose a test for comparing the outofsample accuracy of competing density forecasts of a variable. The test is valid under general conditions: the data can be heterogeneous and the forecasts can be based on (nested or nonnested) parametric models or produced by semi parametric, nonparametric or Bayesian estimation techniques. The evaluation is based on scoring rules, which are loss functions de?ned over the density forecast and the realizations of the variable. We restrict attention to the logarithmic scoring rule and propose an outofsample ?weighted likelihood ratio?test that compares weighted averages of the scores for the competing forecasts. The userde?ned weights are a way to focus attention on di¤erent regions of the distribution of the variable. For a uniform weight function, the test can be interpreted as an extension of Vuong (1989)?s likelihood ratio test to time series data and to an outofsample testing framework. We apply the tests to evaluate density forecasts of US in?ation produced by linear and Markov Switching Phillips curve models estimated by either maximum likelihood or Bayesian methods. We conclude that a Markov Switching Phillips curve estimated by maximum likelihood produces the best density forecasts of in?ation. 
URL:  http://d.repec.org/n?u=RePEc:ubs:wpaper:ubs0504&r=for 