nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒09‒30
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Time series filtering techniques in Stata By Kit Baum
  2. Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation By George Kapetanios; Vincent Labhard; Simon Price
  3. Hierarchical estimation as basis for hierarchical forecasting By Strijbosch,L.W.G.; Heuts,R.M.J.; Moors,J.J.A.
  4. Regime transplants in GDP growth forecasting: A recipe for better predictions? By Lennard van Gelder; Ad Stokman
  5. Panel Cointegration and the Neutrality of Money By Westerlund, Joakim; Costantini, Mauro
  6. Forecasting Monthly GDP for Canada By Annabelle Mourougane
  7. Nonlinearities in Cross-Country Growth Regressions: A Bayesian Averaging of Thresholds (BAT) Approach By Jesus Crespo Cuaresma; Gernot Doppelhofer
  8. Seasonal Cycles in European Agricultural Commodity Prices By Jumah, Adusei; Kunst, Robert M.
  9. 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
  10. Comment on “Realized Variance and Market Microstructure Noise” by Peter R. Hansen and Asger Lunde By Peter C. B. Phillips; Jun Yu

  1. By: Kit Baum (Boston College)
    Abstract: I will describe a number of time series filtering techniques, including the Hodrick-Prescott, Baxter-King and bandpass filters and variants, and present new Mata-coded versions of these routines which are considerably more efficient than previous ado-code routines. Applications to an economic time series will be discussed.
    Date: 2006–09–18
  2. 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.
  3. 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
  4. 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
  5. By: Westerlund, Joakim (Department of Economics, Lund University); Costantini, Mauro (Department of Public Economics)
    Abstract: Most econometric methods for testing the proposition of long-run monetary neutrality rely on the assumption that money and real output do not cointegrate, a result that is usually supported by the data. This paper argues that these results can be attributed in part to the low power of univariate tests, and that a violation of the noncointegration assumption is likely to result in a nonrejection of the neutrality proposition. To alleviate this problem, two new and more powerful panel cointegration tests are proposed that can be used under very general conditions. The tests are then applied to a panel covering 10 countries between 1870 and 1986. The results suggest money and real output are cointegrated, and that the neutrality proposition therefore must be rejected.
    Keywords: Monetary Neutrality; Panel Cointegration Testing
    JEL: C12 C22 C23 E30 E50
    Date: 2006–08–09
  6. 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
  7. By: Jesus Crespo Cuaresma; Gernot Doppelhofer
    Abstract: We propose a framework for assessing the existence and quantifying the effect of threshold effects in cross-country growth regressions in the presence of model uncertainty. The method is based on Bayesian model averaging tech- niques and generalizes the Bayesian Averaging of Classical Estimates (BACE) method put forward by Sala-i-Martin, Doppelhofer, and Miller (2004). We ap- ply the method presented in this paper to a set of 21 variables that have been found to be robustly related to economic growth in a cross-section of 88 coun- tries. We find no evidence of robust threshold effects generated by the initial level of GDP per capita. However, we find that the proportion of years a country has been open to trade is an important source of nonlinear effects on economic growth.
    JEL: C11 C15 O20 O50
    Date: 2006–09
  8. 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
  9. 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
  10. By: Peter C. B. Phillips (Yale University); Jun Yu (School of Economics and Social Sciences, Singapore Management University)
    Abstract: We find ourselves very much in agreement with the thrust of HL’s message concerning the complexity induced by microstructure noise. In particular, we agree that noise is time dependent and correlated with the efficient price - features that in our view are a necessary consequence of the observed form of market transactions, as we have argued above - and that the properties of noise inevitably evolve over time, again just as the efficient price is itself evolutionary. We further agree that microstructure noise cannot be accommodated by simple specifications. Since microstructure noise at ultra high infill sampling frequencies often off-sets the actual transactions data to the latent efficient price, the complexity of microstructure noise includes local nonstationarity and perfect correlation with the efficient price. These are properties that are not permitted in the models and methods presently used in the literature. However, there are empirical procedures that are capable of addressing these additional complexities as we have indicated in parts of our discussion. We join the authors in saying there is still much to do in this exciting field and we look forward to further developments that build on the work they and others have done recently.
    Date: 2005–09

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