nep-for New Economics Papers
on Forecasting
Issue of 2010‒03‒13
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Term structure forecasting using macro factors and forecast combination By Michiel de Pooter; Francesco Ravazzolo; Dick van Dijk
  2. Coexistence and dynamics of overconfidence and strategic incentives. By Bosquet, Katrien; de Goeij, Peter; Smedts, Kristien
  3. GDP nowcasting with ragged-edge data: a semi-parametric modeling By Laurent Ferrara; Dominique Guegan; Patrick Rakotomarolahy
  4. Forecasting Macroeconomic Variables Using Large Datasets: Dynamic Factor Model versus Large-Scale BVARs By Rangan Gupta; Alain Kabundi
  5. Forecasting with Factor-augmented Error Correction By Anindya Banerjee; Massimiliano Marcellino; Igor Masten
  6. A Large Factor Model for Forecasting Macroeconomic Variables in South Africa By Rangan Gupta; Alain Kabundi
  8. The Properties of Survey-Based Inflation Expectations in Sweden By Jonsson, Thomas; Österholm, Pär
  9. "Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets" By Chialin Chang; Michael McAleer; Roengchai Tansuchat
  10. Robust exponential smoothing of multivariate time series. By Croux, Christophe; Gelper, Sarah; Mahieu, Koen
  11. Prediction and error propagation in innovation diffusion models, with applications to demographic processes By Mikko Myrskylä; Joshua R. Goldstein
  12. Exploring the bullwhip effect by means of spreadsheet simulation. By Boute, Robert; Lambrecht, Marc

  1. By: Michiel de Pooter (federal Reserve Board); Francesco Ravazzolo (Norges Bank (Central Bank of Norway)); Dick van Dijk (Erasmus University, Rotterdam)
    Abstract: We examine the importance of incorporating macroeconomic information and, in particular, accounting for model uncertainty when forecasting the term structure of U.S.interest rates. We start off by analyzing and comparing the forecast performance of several individual term structure models. Our results confirm and extend results found in previous literature that adding macroeconomic information, through factors extracted from a large number of individual series, tends to improve interest rate forecasts. We then show, however, that the predictive power of individual models varies over time significantly. Models with macro factors are the more accurate in and around recession periods. Models without macro factors do particularly well in low-volatility subperiods such as the late 1990s. We demonstrate that this problem of model uncertainty can be mitigated by combining individual model forecasts. Combining forecasts leads to encouraging gains in predictability, especially for longer-dated maturities, and importantly, these gains are consistent over time.
    Keywords: Term structure of interest rates, Nelson-Siegel model, Affine term structure model, macro factors, forecast combination, Model Confidence Set
    JEL: C5 C11 C32 E43 E47
    Date: 2010–03–01
  2. By: Bosquet, Katrien; de Goeij, Peter; Smedts, Kristien
    Abstract: We present a two-stage model for the decision making process of financial analysts when issuing earnings forecasts. In the first stage, financial analysts perform a fundamental earnings analysis in which they are, potentially, subject to a behavioral bias. In the second stage analysts can adjust their earnings forecast in line with their strategic incentives. The paper analyzes this decision process throughout the forecasting period and explains the underlying drivers. Using quarterly earnings forecasts, we document that throughout the entire forecasting period financial analysts overweight their private information. At the same time, financial analysts behave strategically. They issue initial optimistic forecasts by strategically inflating their forecast. In their last revision, they become pessimistic and strategically deflate their earnings forecast, which creates the possibility of a positive earnings surprise. This analysis of the dynamics of the decision process pro- vides empirical evidence on the coexistence of overconfidence and strategic incentives.
    Keywords: financial analysts; earnings forecasts; overconfidence; conflicts of interest;
    Date: 2009–10
  3. By: Laurent Ferrara (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, Banque de France - Business Conditions and Macroeconomic Forecasting Directorate); Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Patrick Rakotomarolahy (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I)
    Abstract: This paper formalizes the process of forecasting unbalanced monthly datasets in order to obtain robust nowcasts and forecasts of quarterly gross domestic product (GDP) growth rate through a semi-parametric modeling. This innovative approach lies in the use of non-parametric methods, based on nearest neighbors and on radial basis function approaches, to forecast the monthly variables involved in the parametric modeling of GDP using bridge equations. A real-time experience is carried out on euro area vintage data in order to anticipate, with an advance ranging from 6 to 1 months, the GDP flash estimate for the whole zone.
    Keywords: euro area GDP • real-time nowcasting • forecasting • non-parametric methods
    Date: 2010
  4. By: Rangan Gupta; Alain Kabundi
    Date: 2009
  5. By: Anindya Banerjee; Massimiliano Marcellino; Igor Masten
    Abstract: As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor- augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latters speci…cation in dfferences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simula- tions and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.
    Keywords: Forecasting with Factor-augmented Error Correction
    JEL: C32 E17
    Date: 2010–01
  6. By: Rangan Gupta; Alain Kabundi
    Date: 2009–04
  7. By: Maria M. De Mello (CEF.UP, Faculdade de Economia, Universidade do Porto)
    Abstract: This paper assesses the forecast performance of a set of VAR models under a growing number of restrictions. With a maximum forecast horizon of 12 years, we show that the farther the horizon is, the more structured and restricted VAR models have to be to produce accurate forecasts. Indeed, unrestricted VAR models, not subjected to integration or cointegration, are poor forecasters for both short and long run horizons. Differenced VAR models, subject to integration, are reliable predictors for one-step horizons but ineffectual for multi-step horizons. Cointegrated VAR models including appropriate structural breaks and exogenous variables, as well as being subjected to over-identifying theory consistent restrictions, are excellent forecasters for both short and long run horizons. Hence, to obtain precise forecasts from VAR models, proper specification and cointegration are crucial for whatever horizons are at stake, while integration is relevant only for short run horizons.
    Keywords: VAR demand systems; structural breaks, exogenous regressors, integration; cointegration; forecast accuracy.
    JEL: C32 C53
    Date: 2009–10
  8. By: Jonsson, Thomas (National Institute of Economic Research); Österholm, Pär (National Institute of Economic Research)
    Abstract: This paper assesses the properties of survey-based inflation expectations in Sweden. The survey is conducted by Prospera once every quarter and consists of respondents from businesses and labour-market organisa-tions. The paper shows that inflation expectations measured in this sur-vey tend to be biased and inefficient forecasts of future inflation. Results also indicate that long-run inflation expectations are overly adaptive with respect to actual inflation. Finally, evaluations of forecast accuracy show that these inflation expectations are worse predictors of inflation than those of a professional forecasting institution and also typically outper-formed by a simple autoregressive model. Overall, our results indicate that economic agents’ expectations formation process is suboptimal and/or the survey fails to capture the true inflation expectations.
    Keywords: Survey data; Inflation targeting
    JEL: E52
    Date: 2009–12–01
  9. By: Chialin Chang (Department of Applied Economics, National Chung Hsing University); Michael McAleer (Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute); Roengchai Tansuchat (Faculty of Economics, Maejo University)
    Abstract: Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets.
    Date: 2010–02
  10. By: Croux, Christophe; Gelper, Sarah; Mahieu, Koen
    Abstract: Multivariate time series may contain outliers of different types. In presence of such outliers, applying standard multivariate time series techniques becomes unreliable. A robust version of multivariate exponential smoothing is proposed. The method is affine equivariant, and involves the selection of a smoothing parameter matrix by minimizing a robust loss function. It is shown that the robust method results in much better forecasts than the classic approach in presence of outliers, and performs similar when the data contain no outliers. Moreover, the robust procedure yields an estimator of the smoothing parameter less subject to downward bias. As a byproduct, a cleaned version of the time series is obtained, as is illustrated by means of a real data example.
    Keywords: Data cleaning; Exponential smoothing; Forecasting; Multivariate time series; Robustness;
    Date: 2009–08
  11. By: Mikko Myrskylä (Max Planck Institute for Demographic Research, Rostock, Germany); Joshua R. Goldstein (Max Planck Institute for Demographic Research, Rostock, Germany)
    Abstract: We study prediction and error propagation in Hernes, Gompertz, and logistic models for innovation diffusion. We develop a unifying framework in which the models are linearized with respect to cohort age and predictions are derived from the underlying linear process. We develop and compare methods for deriving the predictions and show how Monte Carlo simulation can be used to estimate prediction uncertainty for a wide class of underlying linear processes. For an important special case, random walk with, we develop an analytic prediction variance estimator. Both the Monte Carlo method and the analytic variance estimator allow the forecasters to make precise the level of within-model prediction uncertainty in innovation diffusion models. Empirical applications to first births, first marriages and cumulative fertility illustrate the usefulness of these methods.
    JEL: J1 Z0
    Date: 2010–02
  12. By: Boute, Robert; Lambrecht, Marc
    Abstract: One of the main supply chain deficiencies is the bullwhip effect: demand fluctuations increase as one moves up the supply chain from retailer to manufacturer. The Beer Distribution Game is widely known for illustrating these supply chain dynamics in class. In this paper we present a spreadsheet application, exploring the two key causes of the bullwhip effect: demand forecasting and the type of ordering policy. We restrict our attention to a single product two-echelon system and illustrate how tuning the parameters of the replenishment policy induces or reduces the bullwhip effect. We also demonstrate how bullwhip reduction (dampening the order variability) may have an adverse impact on inventory holdings and/or customer service. As such, the spreadsheets can be used as an educational tool to gain a clear insight into the use of inventory control policies and forecasting in relation to the bullwhip effect and customer service.
    Keywords: Bullwhip effect; Replenishment rules; Forecasting techniques; Spreadsheet simulation; Beer distribution game;
    Date: 2009–09

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.