
on Forecasting 
By:  Patrizio Pagano (Bank of Italy, Economic Research Department); Massimiliano Pisani (Bank of Italy, Economic Research Department) 
Abstract:  This paper documents the existence of a significant forecast error on crude oil futures, particularly evident since the mid1990s, which is negative on average and displays a nontrivial cyclical component (risk premium). We show that the forecast error on oil futures could have been explained in part by means of realtime US business cycle indicators, such as the degree of utilized capacity in manufacturing. An outofthesample prediction exercise reveals that futures which are adjusted to take into account this timevarying component produce significantly better forecasts than those of the unadjusted futures and random walk, particularly at horizons of more than 6 months. 
Keywords:  Oil, Forecasting, Futures 
JEL:  E37 E44 G13 Q4 
Date:  2006–03 
URL:  http://d.repec.org/n?u=RePEc:bdi:wptemi:td_585_06&r=for 
By:  Costas Milas (Keele University, Centre for Economic Research and School of Economic and Management Studies); Ilias Lekkos (Research Department, Eurobank Ergasias, Greece); Theodore Panagiotidis (Department of Economics, Loughborough University, UK) 
Abstract:  This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a nonlinear framework. We reject linearity for the US and UK swap spreads in favour of a regimeswitching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a nonlinear nearestneighbours model as well as that of linear AR and VAR models.We find some evidence that the nonlinear models predict better than the linear ones. At short horizons, the nearestneighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models. 
Keywords:  Interest rate swap spreads, term structure of interest rates, factor models, regime switching, smooth transition models, nearestneighbours, forecasting. 
JEL:  C51 C52 C53 E43 
Date:  2006–04 
URL:  http://d.repec.org/n?u=RePEc:kee:kerpuk:2006/05&r=for 
By:  Jan F. Qvigstad (Norges Bank (Central Bank of Norway)) 
Abstract:  Svensson (2004) suggested that a monetary policy committee of a central bank (MPC) should “find an instrumentrate path such that projections of inflation and output gap ‘look good’.” Academic literature on monetary policy gives guidance as to what the words “look good” means. However, there is a need for a translation of the theoretical framework into concrete criteria when an MPC shall evaluate interest rate paths in practice. Six criteria for an appropriate interest rate path are presented. In the November 2005 Inflation Report, Norges Bank presented for the first time an optimal interest rate path including a fan chart illustrating the uncertainty of the forecast using these criteria. Examples used in explaining the criteria are drawn from Norwegian experiences. 
Keywords:  Forecasts, flexible inflation targeting, optimal monetary policy 
JEL:  E42 E52 E58 
Date:  2006–05–09 
URL:  http://d.repec.org/n?u=RePEc:bno:worpap:2006_05&r=for 
By:  Minh HaDuong (CIRED  Centre International de Recherche sur l'Environnement et le Développement  http://www.centrecired.fr  [CNRS : UMR8568]  []  [Ecole des Hautes Etudes en Sciences Sociales][Ecole Nationale du Génie Rural des Eaux et des Forêts][Ecole Nationale des Ponts et Chaussées]) 
Abstract:  This paper provides an introduction to the mathematical theory of possibility, and examines how this tool can contribute to the analysis of far distant futures. The degree of mathematical possibility of a future is a number between O and 1. It quantifies the extend to which a future event is implausible or surprising, without implying that it has to happen somehow. Intuitively, a degree of possibility can be seen as the upper bound of a range of admissible probability levels which goes all the way down to zero. Thus, the proposition `The possibility of X is Pi(X) can be read as `The probability of X is not greater than Pi(X).<br /><br />Possibility levels offers a measure to quantify the degree of unlikelihood of far distant futures. It offers an alternative between forecasts and scenarios, which are both problematic. Long range planning using forecasts with precise probabilities is problematic because it tends to suggests a false degree of precision. Using scenarios without any quantified uncertainty levels is problematic because it may lead to unjustified attention to the extreme scenarios.<br /><br />This paper further deals with the question of extreme cases. It examines how experts should build a set of two to four well contrasted and precisely described futures that summarizes in a simple way their knowledge. Like scenario makers, these experts face multiple objectives: they have to anchor their analysis in credible expertise; depict thoughprovoking possible futures; but not so provocative as to be dismissed outofhand. The first objective can be achieved by describing a future of possibility level 1. The second and third objective, however, balance each other. We find that a satisfying balance can be achieved by selecting extreme cases that do not rule out equiprobability. For example, if there are three cases, the possibility level of extremes should be about 1/3. 
Keywords:  Futures, futurible, scenarios, possibility, imprecise probabilities, uncertainty, fuzzy logic 
Date:  2006–04–27 
URL:  http://d.repec.org/n?u=RePEc:hal:papers:halshs00003925_v2&r=for 
By:  Jörg Polzehl; Vladimir Spokoiny 
Abstract:  GARCH models are widely used in financial econometrics. However, we show by mean of a simple simulation example that the GARCH approach may lead to a serious model misspecification if the assumption of stationarity is violated. In particular, the well known integrated GARCH effect can be explained by nonstationarity of the time series. We then introduce a more general class of GARCH models with time varying coefficients and present an adaptive procedure which can estimate the GARCH coefficients as a function of time. We also discuss a simpler semiparametric model in which the betaparameter is fixed. Finally we compare the performance of the parametric, time varying nonparametric and semiparametric GARCH(1,1) models and the locally constant model from Polzehl and Spokoiny (2002) by means of simulated and real data sets using different forecasting criteria. Our results indicate that the simple locally constant model outperforms the other models in almost all cases. The GARCH(1,1) model also demonstrates a relatively good forecasting performance as far as the short term forecasting horizon is considered. However, its application to long term forecasting seems questionable because of possible misspecification of the model parameters. 
Keywords:  varying coefficient GARCH, adaptive weights 
JEL:  C14 C22 C53 
Date:  2006–04 
URL:  http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2006033&r=for 
By:  Enzo Weber 
Abstract:  This paper addresses the question of macroeconomic integration in the Asian Pacific region. Economically, the analysis is based on the notions of stochastic longrun convergence and business cycle coherence. The econometric procedure consists of tests for cointegration, the examination of vector error correction models, several variants of common cycle tests and forecast error variance decompositions. Results in favour of cyclical synchrony can be partly established, and are even exceeded by the broad evidence for equilibrium relations. In these domains, several leading countries are identified. 
Keywords:  Real Convergence, Cointegration, Common Cycles, Asia Pacific 
JEL:  E32 F15 C32 
Date:  2006–05 
URL:  http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2006039&r=for 
By:  Monica Billio (Department of Economics, University Of Venice Cà Foscari); Silvestro Di Sanzo (Departamento de Fundamentos del Analisis Economico, Universidad de Alicante) 
Abstract:  In this paper we propose a new parametrisation of transition probabilities that allows us to characterize and test Grangercausality in Markov switching models by means of an appropriate specification of the transition matrix. Test for independence are also provided. We illustrate our methodology with an empirical application. In particular, we investigate the causality and interdependence between financial and economic cycles using a bivariate Markov switching model. When applied to U.S. data, we find that financial variables are useful for forecasting the direction of aggregate economic activity, and vice versa. 
Keywords:  Granger Causality, Markov Chains, Switching Models 
JEL:  C53 C32 
Date:  2006 
URL:  http://d.repec.org/n?u=RePEc:ven:wpaper:20_06&r=for 