nep-for New Economics Papers
on Forecasting
Issue of 2010‒07‒31
six papers chosen by
Rob J Hyndman
Monash University

  1. Alternative methods for forecasting GDP. By Dominique Guegan; Patrick Rakotomarolahy
  2. A Wavelet Approach for Factor-Augmented Forecasting By António Rua
  3. Short-Term Congestion Forecasting in Wholesale Power Markets By Zhou, Qun; Tesfatsion, Leigh; Liu, Chen-Ching
  4. Predicting bank loan recovery rates with neural networks By Joao A. Bastos
  5. Dynamic factor analysis of carbon allowances prices : From classic Arbitrage Pricing Theory to Switching Regimes. By Marius-Cristian Frunza; Dominique Guegan; Antonin Lassoudière
  6. Monetary Policy Analysis and Forecasting in the Group of Twenty: A Panel Unobserved Components Approach By Francis Vitek

  1. By: Dominique Guegan (Centre d'Economie de la Sorbonne - Paris School of Economics); Patrick Rakotomarolahy (Centre d'Economie de la Sorbonne)
    Abstract: An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the estimation of economic indicators plugged in the bridge equations, we get more accurate forecasts when using nearest neighbor method. We prove also the asymptotic normality of the multivariate k-nearest neighbor regression estimator for dependent time series, providing confidence intervals for point forecast in time series.
    Keywords: Forecast, economic indicators, GDP, Euro area, VAR, multivariate k-nearest neighbor regression, asymptotic normality.
    JEL: C22 C53 E32
    Date: 2010–07
  2. By: António Rua
    Abstract: It has been acknowledged that wavelets can constitute a useful tool for forecasting in economics. Through a wavelet multiresolution analysis, a time series can be decomposed into different time-scale components and a model can be fitted to each component to improve the forecast accuracy of the series as a whole. Up to now, the literature on forecasting with wavelets has mainly focused on univariate modelling. On the other hand, in a context of growing data availability, a line of research has emerged on forecasting with large datasets. In particular, the use of factor-augmented models have become quite widespread in the literature and among practitioners. The aim of this paper is to bridge the two strands of the literature. A wavelet approach for factor-augmented forecasting is proposed and put to test for forecasting GDP growth for the major euro area countries. The results show that the forecasting performance is enhanced when wavelets and factor-augmented models are used together.
    JEL: C22 C40 C53
    Date: 2010
  3. By: Zhou, Qun; Tesfatsion, Leigh; Liu, Chen-Ching
    Abstract: Short-term congestion forecasting is highly important for market participants in wholesale power markets that use Locational Marginal Prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning. This study proposes a new short-term congestion forecasting algorithm based on the concept of system patterns—combinations of status flags for transmission lines and generating units. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error.  Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm.
    Keywords: wholesale power market; locational marginal price; Congestion forecasting; load partitioning; convex hull algorithm; LMP forecasting; system patterns
    JEL: C1 C53 C6 D4 L1 Q4
    Date: 2010–07–19
  4. By: Joao A. Bastos (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)
    Abstract: This study evaluates the performance of feed-forward neural networks to model and forecast recovery rates of defaulted bank loans. In order to guarantee that the predictions are mapped into the unit interval, the neural networks are implemented with a logistic activation function in the output neuron. The statistical relevance of explanatory variables is assessed using the bootstrap technique. The results indicate that the variables which the neural network models use to derive their output coincide, to a great extent, with those that are significant in parametric fractional regression models. Out-of-sample estimates of prediction errors are evaluated. The results suggest that neural networks may have better predictive ability than fractional regression models, provided the number of observations is sufficiently large.
    Keywords: Loss given default, Recovery rate, Forecasting, Bank loan, Fractional regression, Neural network
    JEL: G21 G33
    Date: 2010–07
  5. By: Marius-Cristian Frunza (Centre d'Economie de la Sorbonne et Sagacarbon - Caisse des Dépôts); Dominique Guegan (Centre d'Economie de la Sorbonne - Paris School of Economics); Antonin Lassoudière (Sagacarbon - Caisse des dépôts)
    Abstract: The aim of this paper is to identify the fundamental factors that drive the allowances market and to built an APT-like model in order to provide accurate forecasts for CO2. We show that historic dependency patterns emphasis energy, natural gas, oil, coal and equity indexes as major factors driving the carbon allowances prices. There is strong evidence that model residuals are heavily tailed and asymmetric, thereby generalized hyperbolic distribution provides with the best fit results. Introducing dynamics inside the parameters of the APT model via a Hidden Markov Chain Model outperforms the results obtained with a static approach. Empirical results clearly indicate that this model could be used for price forecasting, that it is effective in and out of sample producing consisten results in allowances futures price prediction.
    Keywords: Carbon, EUA, energy, Arbitrage Pricing Theory, switching regimes, hidden Markov Chain model, forecast.
    Date: 2010–06
  6. By: Francis Vitek
    Abstract: This paper develops a panel unobserved components model of the monetary transmission mechanism in the world economy, disaggregated into twenty national economies along the lines of the Group of Twenty. This structural macroeconometric model features extensive linkages between the real and financial sectors, both within and across economies. A variety of monetary policy analysis and forecasting applications of the estimated model are demonstrated, based on a Bayesian framework for conditioning on judgment.
    Date: 2010–06–29

This nep-for issue is ©2010 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.