nep-for New Economics Papers
on Forecasting
Issue of 2006‒09‒30
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Hierarchical estimation as basis for hierarchical forecasting By Strijbosch,L.W.G.; Heuts,R.M.J.; Moors,J.J.A.
  2. Regime transplants in GDP growth forecasting: A recipe for better predictions? By Lennard van Gelder; Ad Stokman
  3. Forecasting Monthly GDP for Canada By Annabelle Mourougane
  4. Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation By George Kapetanios; Vincent Labhard; Simon Price
  5. Seasonal Cycles in European Agricultural Commodity Prices By Jumah, Adusei; Kunst, Robert M.
  6. Direction-of-Change Forecasts for Asian Equity Markets Based on Conditional Variance, Skewness and Kurtosis Dynamics: Evidence from Hong Kong and Singapore By Peter F. Christoffersen; Francis X. Diebold; Roberto S. Mariano; Anthony S. Tay; Yiu Kuen Tse
  7. How Equilibrium Prices Reveal Information in Time Series Models with Disparately Informed, Competitive Traders By Todd B. Walker
  8. Asset Prices in a Time Series Model with Perpetually Disparately Informed, Competitive Traders By Kenneth Kasa; Todd B. Walker; Charles H. Whiteman
  9. Stochastic Risk vs. Policy Oriented Uncertainties: The Case of the Alpine Crossings By Julien Brunel
  10. What Drives Heterogeneity in Foreign Exchange Rate Expectations : Deep Insights from a New Survey By Christian Dreger; Georg Stadtmann
  11. Empirical Phillips Curves in OECD Countries: Has There Been A Common Breakdown? By Doyle, Matthew
  12. The New Keynesian Phillips Curve for a Small Open Economy By Pål Boug, Ådne Cappelen and Anders Rygh Swensen

  1. By: Strijbosch,L.W.G.; Heuts,R.M.J.; Moors,J.J.A. (Tilburg University, Center for Economic Research)
    Abstract: In inventory management, hierarchical forecasting (HF) is a hot issue : families of items are formed for which total demand is forecasted; total forecast then is broken up to produce forecasts for the individual items. Since HF is a complicated procedure, analytical results are hard to obtain; consequently, most literature is based on simulations and case studies. This paper succeeds in following a more theoretical approach by simplifying the problem : we consider estimation instead of forecasting. So, from a random sample we estimate both total demand and the fraction of this total that individual items take; multiplying these two quantities gives a new estimate of individual demand. Then our research question is: can aggregation of items, followed by fractioning, lead to more accurate estimates of individual demand? Thirdly, a more practical situation is investigated by means of simulation.
    Keywords: hierarchical forecasting;aggregation;top-down approach
    JEL: C53
    Date: 2006
  2. By: Lennard van Gelder; Ad Stokman
    Abstract: Formal testing and estimation of nonlinear relations require a substantial number of observations which are typically lacking in annual models. In this paper, a novel two-step procedure is introduced to model nonlinearities in yearly asset-price based leading indicator models for growth. In the first step, quarterly data are explored to test for the presence of regime switches, the identif ication of transition variables and estimation of the accompanying thresholds. In the second step, we implement the quarterly thresholds in the annual indicator models. Results for the US and the Netherlands show that the annual forecasts improve compared to the linear model, despite the poor out-of-sample performance of the quarterly regime switching models.
    Keywords: leading indicators; gdp growth; non-linear models.
    JEL: C53 E37
    Date: 2006–08
  3. By: Annabelle Mourougane
    Abstract: The objective of this paper is to develop a short-term indicator-based model to predict quarterly GDP in Canada by efficiently exploiting all available monthly information. To this aim, monthly forecasting equations are estimated using the GDP series published every month by Statistics Canada as well as other monthly indicators. The procedures are automated and the model can be run whenever major monthly data are released, allowing the appropriate choice of the model according to the information set available. The most important gain from this procedure is for the current-quarter forecast when one or two months of GDP data are available, with all monthly models estimated in the paper outperforming a standard quarterly autoregressive model in terms of size of errors. The use of indicators also appears to improve forecasting performance, especially when an average of indicator-based models is used. Real-time forecasting performance of the average model appear to be good, with an apparent stability of the estimates from one update to the next, despite the extensive use of monthly data. The latter result should nonetheless be interpreted with caution and will need to be re-assessed when more data become available. <P>Prévoir le PIB mensuel au Canada <BR>L’objectif de cet article est de développer un modèle d’indicateurs conjoncturels pour prédire le PIB trimestriel au Canada en utilisant de manière efficace toute l’information mensuelle disponible. À cette fin, des équations mensuelles de prévisions de court terme sont estimées en utilisant la série de PIB publiée chaque mois par Statistique Canada et d’autres indicateurs conjoncturels. Les procédures ont été automatisées et le modèle peut être mis à jour chaque fois qu’une donnée importante est publiée, la spécification du modèle variant ainsi en fonctions de l’ensemble des données disponibles. Le gain le plus important de la procédure développée est obtenue pour les prévisions du trimestre courant quand un ou deux mois de données du PIB mensuel sont disponibles. Dans ce cas, tous les modèles mensuels estimés dans cet article ont des erreurs de prévisions inférieures à celle d’un modèle trimestriel autorégressif standard. L’utilisation d’indicateurs conjoncturels améliore les performances en termes de prévisions, en particulier lorsqu’une moyenne de tous les modèles d’indicateurs conjoncturels est utilisée. Les prévisions réalisées en temps réel en faisant la moyenne des différents modèles d’indicateurs conjoncturels se sont avérées de qualité satisfaisante, avec une stabilité apparente des estimations successives, malgré l’utilisation extensive de données mensuelles. Ces résultats doivent toutefois être interprétés avec prudence et devront être vérifiés quand plus de données seront disponibles.
    Keywords: Canada, Canada, indicator models, modèle d'indicateurs conjoncturels, monthly GDP, short-term forecasts, real-time estimations, PIB mensuel, prévisions de court terme, estimations en temps réel
    JEL: C52 C53 E37
    Date: 2006–09–13
  4. By: George Kapetanios; Vincent Labhard; Simon Price
    Abstract: In recent years there has been increasing interest in forecasting methods that utilise large data sets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is a popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian model averaging methods have been widely employed in this area, but a neglected alternative approach employed in this paper uses information theoretic based weights. We consider the use of model averaging in forecasting UK inflation with a large data set from this perspective. We find that an information theoretic model averaging scheme can be a powerful alternative both to the more widely used Bayesian model averaging scheme and to factor models.
  5. By: Jumah, Adusei (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and Department of Economics, University of Vienna, Austria); Kunst, Robert M. (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and Department of Economics, University of Vienna, Austria)
    Abstract: This paper explores the seasonal cycles of European agricultural commodity prices. We focus on three food crops (barley, soft and durum wheat) and on beef. We investigate whether seasonality is deterministic or unit-root stochastic and whether seasonal cycle for specific agricultural commodities have converged over time. Finally, we develop time-series models that are capable of forecasting agricultural prices on a quarterly basis. Firstly, we find that seasonal cycles in agricultural commodity prices are mainly deterministic and that evidence on common cycles across countries varies over agricultural commodities. The prediction experiments, however, yield a ranking with respect to accuracy that does not always match the statistical in-sample evidence.
    Keywords: Seasonal cycles, Seasonal unit roots, Forecasting, Agricultural commodities
    JEL: C32 C53 Q11
    Date: 2006–09
  6. By: Peter F. Christoffersen (McGill University and CIRANO); Francis X. Diebold (University of Pennsylvania and NBER); Roberto S. Mariano (School of Economics and Social Sciences, Singapore Management University); Anthony S. Tay (School of Economics and Social Sciences, Singapore Management University); Yiu Kuen Tse (School of Economics and Social Sciences, Singapore Management University)
    Abstract: Recent theoretical work has revealed a direct connection between asset return volatility forecastability and asset return sign forecastability. This suggests that the pervasive volatility forecastability in equity returns could, via induced sign forecastability, be used to produce direction-ofchange forecasts useful for market timing. We attempt to do so in the context of two key Asian equity markets, with some success, as assessed by formal probability forecast scoring rules such as the Brier score. An important ingredient is our conditioning not only on conditional variance information, but also conditional skewness and kurtosis information, when forming direction-of-change forecasts.
    Keywords: Volatility, variance, skewness, kurtosis, market timing, asset management, asset allocation, portfolio management.
    JEL: G10 G12
    Date: 2004–07
  7. By: Todd B. Walker (Indiana University Bloomington)
    Abstract: Accommodating asymmetric information in a dynamic asset pricing model is technically challenging due to the problems associated with higher-order expectations. That is, rational investors are forced into a situation where they must forecast the forecasts of other agents. In a dynamic setting, this problem telescopes into the infinite future and the dimension of the relevant state space approaches infinity. By using the frequency domain approach of Whiteman (1983) and Kasa (2000), this paper demonstrates how information structures previously believed to preserve asymmetric information in equilibrium, converge to a symmetric information, rational expectations equilibrium. The revealing aspect of the price process lies in the invertibility of the observed state space, which makes it possible for agents to infer the economically fundamental shocks and thus eliminating the need to forecast the forecasts of others.
    Keywords: Asset Pricing, Asymmetric Information
    JEL: G12 D82
    Date: 2006–09
  8. By: Kenneth Kasa (Simon Fraser University); Todd B. Walker (Indiana University Bloomington); Charles H. Whiteman (University of Iowa)
    Abstract: This paper develops a dynamic asset pricing model with persistent heterogeneous beliefs. The model features competitive traders who receive idiosyncratic signals about an underlying fundamentals process. We adapt Futia’s (1981) frequency domain methods to derive conditions on the fundamentals that guarantee noninvertibility of the mapping between observed market data and the underlying shocks to agents’ information sets. When these conditions are satisfied, agents must ‘forecast the forecasts of others’. The paper provides an explicit analytical characterization of the resulting higher-order belief dynamics. These additional dynamics can explain apparent violations of variance bounds and rejections of cross-equation restrictions.
    Keywords: Asymmetric Information, Blaschke Factors
    JEL: G12 D82
    Date: 2006–09
  9. By: Julien Brunel (LET - Laboratoire d'économie des transports - [CNRS : UMR5593] - [Université Lumière - Lyon II] - [Ecole Nationale des Travaux Publics de l'Etat])
    Abstract: This paper focuses on uncertainties in traffic forecasting. Three major sources of uncertainties are observed for freight demand models. The first one is the model specification itself. We are not interested by it. The second one concerns uncertainties over forecasting hypotheses. A mean to control such uncertainties lies in the introduction of risk in the Costs Benefits Analysis (CBA). Two directions have been taken by this research. The first one is the theoretical framework of CBA under uncertainty mainly developed after Dixit and Pindyck (1994). The second one is more empirical and uses Monte Carlo simulations. Major results of these researches are presented. Then, we apply them to a large transport investment simulation. These tools cannot be used for all kinds of uncertainties. The second part of this paper deals with the third source of uncertainties i. e. policy oriented uncertainties. For them, previous methods are useless. The current Alpine crossings context shows that transport policy is a major determinant of traffics. Furthermore, long term forecasting cannot exclude the possibility of changes in transport policy. This uncertainty should be controlled. It is the role of strategic modeling.
    Keywords: risk ; uncertainty ; traffic forecasting ; Monte Carlo simulation ; transport policy ; Strategic models ; Alpine crossings
    Date: 2006–09–20
  10. By: Christian Dreger; Georg Stadtmann
    Abstract: Foreign exchange rate expectations play a central role in virtually all monetary models for the open economy. Therefore, it is extremely important to gain empirical insights into the expectations formation process. In this paper, we use a unique disaggregated data set to model the expectations of the Yen/USD exchange rate of about 50 leading foreign exchange rate professionals. The survey includes not only forecasts of the exchange rate, but also for macroeconomic fundamentals, like GDP growth, inflation, and interest rates. Different expectations of fundamentals might lead to different views of exchange rate dynamics. Using panel models, we are able to confirm the het-erogeneity of exchange rate expectations often detected by former authors. More impor-tant, we provide strong evidence regarding the likely source of heterogeneity. In line with forward looking models for the exchange rate, expected fundamentals have a sub-stantial impact on exchange rate expectations, thereby challenging the backward look-ing evidence of previous studies. However, the heterogeneity in the expectations of macroeconomic fundamentals is not sufficient to explain the heterogeneity in exchange rate expectations.
    Keywords: Exchange rate expectations, heterogeneity of expectations, expected fundamentals
    JEL: F31 F37 C23
    Date: 2006
  11. By: Doyle, Matthew
    Abstract: Recent work on U.S. data calls into question the ability of simple Phillips curve models to forecast inflation. This paper asks whether there is similar evidence of a breakdown in the forecasting ability of Phillips curve models in other OECD countries. The results suggests that the ability of a Phillips curve to out-forecast simpler models has deteriorated in many OECD countries. The evidence is less clear as to whether this breakdown can be attributed to structural breaks in the parameters of the Phillips curve
    Keywords: Phillips curve, structural breaks, forecast breakdown
    JEL: E3
    Date: 2006–09–25
  12. By: Pål Boug, Ådne Cappelen and Anders Rygh Swensen (Statistics Norway)
    Abstract: The New Keynesian Phillips Curve (NKPC) has become the benchmark model for understanding inflation in modern monetary economics. One reason for the popularity is the microfoundation of the model, which decomposes agents' behaviour into price adjustments and deviations of the price level from its target. The empirical relevance of the NKPC is, however, a matter of debate as recent studies reveal that some supportive evidence depends crucially on the econometric methods applied. We show how to evaluate the features of the model using cointegration techniques and tests based on both single-behavioural equations and cointegrated VAR models. Our results indicate that the forward-looking part of the NKPC is most likely at odds with Norwegian data. By contrast, we establish a well-specified dynamic model interpreted as a standard backward-looking mark-up price equation. We also demonstrate that the dynamic mark-up model forecasts well post-sample and during a major change in the monetary policy regime, which certainly is strong evidence in favour of this model. Consequently, we conclude that taking account of forward-looking behaviour when modelling consumer price inflation in Norway seems unnecessary to arrive at a well-specified model by econometric criteria.
    Keywords: The New Keynesian Phillips Curve; mark-up pricing; single-equation estimation methods; encompassing tests; cointegrated vector autoregressive models and equilibrium correction models.
    JEL: C51 C52 E31 F31
    Date: 2006–05

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