New Forecasting Papers – 2008-06-24

In this issue we have: An Hourly Periodic State Space Model for Modelling French National Electricity Load ; Forecasting Random Walks Under Drift Instability ; Short-term forecasting of GDP using large monthly datasets – a pseudo real-time forecast evaluation exercise ;Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails ; Diachronic Behaviour of the OECD Forecasts for Greece ; and more.

  • An Hourly Periodic State Space Model for Modelling French National Electricity Load
    Date: 2008-01-17
    By: V. Dordonnat (VU University Amsterdam)
    S.J. Koopman (VU University Amsterdam)
    M. Ooms (VU University Amsterdam)
    A. Dessertaine (Electricité de France, Clamart, France)
    J. Collet (Electricité de France, Clamart, France)
    We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for each hour of the day. Dependence between the equations is introduced by covariances between disturbances that drive the time-varying processes. The equations are estimated simultaneously. Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects including nonlinear functions for heating effects. The implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework and is applied to national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 AM, but forecasting results are presented for all twenty-four hours. Given the time series length of nine years of hourly observations, many features of our model can be readily estimated including yearly patterns and their time-varying nature. The empirical analysis involves an out-of sample forecasting assessment up to seven days ahead. The one-day ahead forecasts from fourty-eight bivariate models are compared with twenty-four univariate models for all hours of the day. We find that the implied forecasting function strongly depends on the hour of the day.
    Keywords: Kalman filter; Maximum likelihood estimation; Seemingly Unrelated Regression Equations; Unobserved Components; Time varying parameters; Heating effect
    JEL: C22 C32 C52 C53
  • Forecasting Random Walks Under Drift Instability
    Date: 2008-03
    By: Pesaran, M.H.
    Pick, A.
    This paper considers forecast averaging when the same model is used but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to using forecasts based on a single estimation window, averaging over estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks. Similar results are also obtained when observations are exponentially down-weighted, although in this case the performance of forecasts based on exponential down-weighting critically depends on the choice of the weighting coefficient. The forecasting techniques are applied to monthly inflation series of 21 OECD countries and it is found that average forecasting methods in general perform better than using forecasts based on a single estimat ion window.
    Keywords: Forecast combinations, averaging over estimation windows, exponentially down-weighting observations, structural breaks.
    JEL: C22 C53
  • Short-term forecasting of GDP using large monthly datasets – a pseudo real-time forecast evaluation exercise
    Date: 2008-04
    By: Karim Barhoumi
    Szilard Benk
    Riccardo Cristadoro
    Ard Den Reijer
    Audrone Jakaitiene
    Piotr Jelonek
    António Rua
    Gerhard Rünstler (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Karsten Ruth
    Christophe Van Nieuwenhuyze
    This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best. JEL Classification: E37, C53.
    Keywords: Bridge models, Dynamic factor models, real-time data flow.
  • Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails
    Date: 2008
    By: Dijk, D. van (Erasmus Universiteit Rotterdam)
    Diks, C.G.H. (Universiteit van Amsterdam)
    Panchenko, V. (University of New South Wales)
    We propose new scoring rules based on partial likelihood for assessing the relative out-of-sample predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. By construction, existing scoring rules based on weighted likelihood or censored normal likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased towards such densities. Our novel partial likelihood-based scoring rules do not suffer from this problem, as illustrated by means of Monte Carlo simulations and an empirical application to daily S\&P 500 index returns.
  • A Note on the Diachronic Behaviour of the OECD Forecasts for Greece
    Date: 2008
    By: Dikaios Tserkezos (Department of Economics, University of Crete, Greece)
    George Xanthos (Technical Institute of Crete)
    Eva Pitikaki (Greek Econometric Institute – University of Crete)
    In this short paper a Gamma distributed lags model is used to study the diachronic responses between the actual data and the forecasts supplied by OECD the last 27 years for the case of the Greek Economy. According to our results we verified the potentials of the OECD to improve its forecasts as the size of the foreseeable period decreases. Irrespective of how good are the OECD's forecasts, there is certainly much room for further improvement.
    Keywords: OECD Forecasting Accuracy, Greek Economy, Gamma Distributed Lags Model
    JEL: E17 E37 F17 F47
  • Incorporating judgement with DSGE models
    Date: 2008-06
    By: Jaromír Beneš
    Andrew Binning
    Kirdan Lees (Reserve Bank of New Zealand)
    Central bank policymakers often cast judgement about macroeconomic forecasts in reduced form terms, basing this on off-model information that is not easily mapped to a structural DSGE framework. We show how to compute forecasts conditioned on policymaker judgement that are the most likely conditional forecasts from the perspective of the DSGE model, thereby maximising the influence of the model structure on the forecasts. We suggest using a simple implausibility index to track the magnitude and type of policymaker judgement. This is based on the structural shocks required to return policymaker judgement. We show how to use the methods for practical use in the policy environment and also apply the techniques to condition DSGE model forecasts on: (i) the long history of published forecasts from the Reserve Bank of New Zealand; (ii) constant interest rate forecasts; and (iii) inflation forecasts from a Bayesian VAR cu rrently used in the policy environment at the Reserve Bank of New Zealand.
    Keywords: DSGE models; monetary policy; conditional forecasts
    JEL: C51 C53
  • Forecasting Newspaper Demand with Censored Regression
    Date: 2008-04
    By: Kiygi Calli M.
    Weverbergh M.
    Newspaper circulation has to be determined at the level of the individual retail outlets for each of the editions to be sold through such outlets. Traditional forecasting methods provide no insight into the impact of the service level defined as the probability that no out-of-stock will occur. The service level results in out-of stock situations, causing missed sales and oversupply or returns. In our application management sets a policy aiming at a 97 percent service level. The forecasting system developed provides estimates for excess deliveries and for the expected shortages. The results compare favorably to the traditional moving average approach previously employed by the publisher. Censored regression is a natural approach to the newspaper problem. It provides information on key policy variables and it is relatively simple to integrate into the distribution policy, with only small adaptations to the existing f orecasting and distribution policy.
  • Predicting elections from politicians' faces
    Date: 2008-06-16
    By: Armstrong, J. Scott
    Green, Kesten C.
    Jones, Randall J.
    Wright, Malcolm
    Prior research found that people's assessments of relative competence predicted the outcome of Senate and Congressional races. We hypothesized that snap judgments of "facial competence" would provide useful forecasts of the popular vote in presidential primaries before the candidates become well known to the voters. We obtained facial competence ratings of 11 potential candidates for the Democratic Party nomination and of 13 for the Republican Party nomination for the 2008 U.S. Presidential election. To ensure that raters did not recognize the candidates, we relied heavily on young subjects from Australia and New Zealand. We obtained between 139 and 348 usable ratings per candidate between May and August 2007. The top-rated candidates were Clinton and Obama for the Democrats and McCain, Hunter, and Hagel for the Republicans; Giuliani was 9th and Thompson was 10th. At the time, the leading candidates in the Democr atic polls were Clinton at 38% and Obama at 20%, while Giuliani was first among the Republicans at 28% followed by Thompson at 22%. McCain trailed at 15%. Voters had already linked Hillary Clinton's competent appearance with her name, so her high standing in the polls met our expectations. As voters learned the appearance of the other candidates, poll rankings moved towards facial competence rankings. At the time that Obama clinched the nomination, Clinton was ahead in the popular vote in the primaries and McCain had secured the Republican nomination with a popular vote that was twice that of Romney, the next highest vote-getter.
    Keywords: accuracy; appearance; forecasting methods; snap judgments
    JEL: C53 D81 D72 C42
  • "Analysts' Earnings Forecasts and the Value Relevance of Earnings"(in Japanese)
    Date: 2008-06
    By: Takashi Obinata (Faculty of Economics, University of Tokyo)
    The value relevance of earnings information depends on the information environments that investors face. In general, under the highly uncertain circumstances, information is not completely nor instantly reflected in stock prices and so the value relevance of earnings is low. The purpose of this paper is to estimate the uncertainty of information environments by data of analysts' earnings forecasts and to investigate the relationship between the uncertainty and the relevance of earnings in JP and US market. In US, though the dispersion of forecasts causes subsequent positive returns, it makes the value relevance of earnings higher. However, it seems that the divergence of opinion does not determine the information environments in JP. In JP, we cannot find the evident relationship between the dispersion and the relevance of earnings. On the other hand, analyst coverage does not affect the relevance of earnings in US while the earnings is more value relevant for firms covered by analysts than firms uncovered in JP. Moreover, the effect of optimistic forecast errors on the value relevance of earnings differs between JP and US. Our results show that earnings is value relevant and earnings information is almost efficiently reflected in stock prices while the subsequent anomalous returns concerning analysts' forecasts exist. Empirical evidence indicates that the information environments in JP are different from that in US and that the relationship between environments and the relevance of earnings also differs. This paper provides the valuable evidence against prior international comparative studies that neglect the differences in information environments.
  • Likelihood-based Analysis for Dynamic Factor Models
    Date: 2008-01-17
    By: Borus Jungbacker (VU University Amsterdam)
    Siem Jan Koopman (VU University Amsterdam)
    We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and parameter estimation by maximum likelihood and Bayesian methods. An illustration is provided for the analysis of a large panel of macroeconomic time series.
    Keywords: EM algorithm; Kalman Filter; Forecasting; Latent Factors; Markov chain Monte Carlo; Principal Components; State Space
    JEL: C33 C43
  • Taken from the NEP-FOR mailing list edited by Rob Hyndman.