Forecasting papers 2008-03-06

In this issue we have: Forecasting inflation with dynamic factor model – the case of Poland ; Forecasting chaotic systems : the role of local Lyapunov exponents ; Multivariate Forecast Evaluation And Rationality Testing ; Are sectoral stock prices useful for predicting euro area GDP?

  • Forecasting inflation with dynamic factor model – the case of Poland
    Date: 2008-02-24
    By: Jacek Kotlowski (Warsaw School of Economics, National Bank of Poland)
    The purpose of the article is to evaluate the forecasting performance of dynamic factor models in forecasting inflation in the Polish economy. The factor models are based on the assumption that the behavior of most macroeconomic variables can be well described by several unobservable factors, which are often interpreted as the driving factors in the economy. Such models are very often successfully used for forecasting. Employing several factors instead of a large number of explanatory variables may increase the number of degrees of freedom with the same information content. In the article we compare forecast accuracy of dynamic factor models with the forecast accuracy of three competitive models: univariate autoregressive model, VAR model and the model with leading indicator from the business survey. We have used 92 monthly time series from the Polish and world economy to conduct the out-of-sample real time foreca! sts of inflation (consumer price index). The results are encouraging. The dynamic factor model outperforms other models for both 1-step ahead and 3-step ahead forecast. The advantage of factor models is more straightforward for 1-month than for 3-month horizon.
    Keywords: inflation, forecasting, factor models
    JEL: C22 C53 E31 E37
  • Forecasting chaotic systems : the role of local Lyapunov exponents
    Date: 2008-02
    By: Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I, Ecole d'économie de Paris – Paris School of Economics – Université Panthéon-Sorbonne – Paris I)
    Justin Leroux (Institute for Applied Economics – HEC MONTRÉAL, CIRPEE – Centre Interuniversitaire sur le Risque, les Politiques Economiques et l'Emploi)
    We propose a novel methodology for forecasting chaotic systems which is based on the nearest-neighbor predictor and improves upon it by incorporating local Lyapunov exponents to correct for its inevitable bias. Using simulated data, we show that gains in prediction accuracy can be substantial. The general intuition behind the proposed method can readily be applied to other non-parametric predictors.
    Keywords: Chaos theory, Lyapunov exponent, logistic map, Monte Carlo simulations.
  • Multivariate Forecast Evaluation And Rationality Testing
    Date: 2007-11-01
    By: Ivana Komunjer (University of California – San Diego)
    MICHAEL OWYANG (Federal Reserve Bank of Saint Louis)
    In this paper, we propose a new family of multivariate loss functions that can be used to test the rationality of vector forecasts without assuming independence across individual variables. When only one variable is of interest, the loss function reduces to the flexible asymmetric family recently proposed by Elliott, Komunjer, and Timmermann (2005). Following their methodology, we derive a GMM test for multivaariate forecast rationality that allows the forecast errors to be dependent, and takes into account forecast estimation ucertainty. We use our test to study the rationality of macroeconomic vector forecasts in the growth rate in nominal output, the CPI inflation rate, and a short-term interest rate.
    Keywords: multivariate forecast rationality, multivariate loss, asymmetries, Fed Transparency,
  • Are sectoral stock prices useful for predicting euro area GDP?
    Date: 2008-02
    By: Magnus Andersson (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Antonello D'Agostino (Central Bank and Financial Services Authority of Ireland, Economic Analysis and Research Department, PO Box 559, Dame Street, Dublin 2, Ireland.)
    This paper evaluates how well sectoral stock prices forecast future economic activity compared to traditional predictors such as the term spread, dividend yield, exchange rates and money growth. The study is applied to euro area financial asset prices and real economic growth, covering the period 1973 to 2006. The paper finds that the term spread is the best predictor of future growth in the period leading up to the introduction of Monetary Union. After 1999, however, sectoral stock prices in general provide more accurate forecasts than traditional asset price measures across all forecast horizons. JEL Classification: C52, C53.
    Keywords: Forecasting Models, Asset Prices.
  • Taken from the NEP-FOR mailing list edited by Rob Hyndman.