nep-for New Economics Papers
on Forecasting
Issue of 2008‒10‒28
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Exchange Rates with a Large Bayesian VAR By Andrea Carriero; George Kapetanios; Massimiliano Marcellino
  2. Combining Multivariate Density Forecasts using Predictive Criteria By Hugo Gerard; Kristoffer Nimark
  3. Local Lyapunov exponents: Zero plays no role in Forecasting chaotic systems By Dominique Guégan; Justin Leroux
  4. A note on the model selection risk for ANOVA based adaptive forecasting of the EURIBOR swap term structure. By Oliver Blaskowitz; Helmut Herwartz
  5. Estimating the functional form of road traffic maturity By Antonio Núñez

  1. By: Andrea Carriero (Queen Mary, University of London); George Kapetanios (Queen Mary, University of London); Massimiliano Marcellino (European University Institute and Bocconi University)
    Abstract: Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange rates tend to co-move, the use of a large set of them can contain useful information for forecasting. In addition, we adopt a driftless random walk prior, so that cross-dynamics matter for forecasting only if there is strong evidence of them in the data. We produce forecasts for all the 33 exchange rates in the panel, and show that our model produces systematically better forecasts than a random walk for most of the countries, and at any forecast horizon, including at 1-step ahead.
    Keywords: Exchange rates, Forecasting, Bayesian VAR
    JEL: C53 C11 F31
    Date: 2008–10
  2. By: Hugo Gerard; Kristoffer Nimark
    Abstract: This paper combines multivariate density forecasts of output growth, inflation and interest rates from a suite of models. An out-of-sample weighting scheme based on the predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson and Karlsson (2007) is used to combine the models. Three classes of models are considered: a Bayesian vector autoregression (BVAR), a factor-augmented vector autoregression (FAVAR) and a medium-scale dynamic stochastic general equilibrium (DSGE) model. Using Australian data, we find that, at short forecast horizons, the Bayesian VAR model is assigned the most weight, while at intermediate and longer horizons the factor model is preferred. The DSGE model is assigned little weight at all horizons, a result that can be attributed to the DSGE model producing density forecasts that are very wide when compared with the actual distribution of observations. While a density forecast evaluation exercise reveals little formal evidence that the optimally combined densities are superior to those from the best-performing individual model, or a simple equal-weighting scheme, this may be a result of the short sample available.
    Keywords: Density forecasts, combining forecasts, predictive criteria
    Date: 2008–08
  3. By: Dominique Guégan; Justin Leroux (IEA, HEC Montréal)
    Abstract: We propose a novel methodology for forecasting chaotic systems which uses information on local Lyapunov exponents (LLEs) to improve upon existing predictors by correcting for their inevitable bias. Using simulated data on the nearest-neighbor predictor, we show that accuracy gains can be substantial and that the candidate selection problem identified in Guégan and Leroux (2009) can be solved irrespective of the value of LLEs. An important corollary follows: the focal value of zero, which traditionally distinguishes order from chaos, plays no role whatsoever when forecasting deterministic systems.
    Keywords: Chaos theory, Lyapunov exponent, Lorenz attractor Rössler attractor, Monte Carlo Simulations.
    JEL: C15 C22 C53 C65
    Date: 2008–09
  4. By: Oliver Blaskowitz; Helmut Herwartz
    Abstract: The paper proposes a data driven adaptive model selection strategy. The selection crite- rion measures economic ex–ante forecasting content by means of trading implied cash flows. Empirical evidence suggests that the proposed strategy is neither exposed to selection bias nor to the risk of choosing excessively poor models from a parameterized class of candidate specifications.
    Keywords: Model selection, Principal components, Factor analysis, Ex–ante forecasting, EURIBOR swap term structure, Trading strategies.
    JEL: C32 C53 E43 G29
    Date: 2008–10
  5. By: Antonio Núñez (LET - Laboratoire d'économie des transports - CNRS : UMR5593 - Université Lumière - Lyon II - Ecole Nationale des Travaux Publics de l'Etat)
    Abstract: It has been observed that older high traffic motorways experience lower traffic growth than newer ones (ceteris paribus). This phenomenon is known as traffic maturity; however, it is not captured through traditional time-series long-term forecasts, due to constant elasticity to gross domestic product these models assume. In this paper we argue that traffic maturity results from decreasing marginal utility of transport. The elasticity of individual mobility with respect to the revenue tends to decrease when the level of mobility increases. In order to find evidences of decreasing elasticity we analyse a cross-section time-series sample including 40 French motorways' sections. This analysis shows that decreasing elasticity can be observed in the long term. We then propose a decreasing function for the traffic elasticity with respect to the economic growth, which depends on the traffic level on the road. This model provides a good explanation for the observed traffic evolution and gives a rigorous econometric approach to time-series traffic forecasts.
    Keywords: Demand forecast ; Elasticity ; Traffic growth ; Traffic maturity
    Date: 2008

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