
on Econometric Time Series 
By:  Anthony Garratt (School of Economics, Mathematics & Statistics, Birkbeck College); Donald Robertson; Stephen Wright (School of Economics, Mathematics & Statistics, Birkbeck College) 
Abstract:  Any nonstationary series can be decomposed into permanent (or "trend") and transitory (or "cycle") components. Typically some atheoretic prefiltering procedure is applied to extract the permanent component. This paper argues that analysis of the fundamental underlying stationary economic processes should instead be central to this process. We present a new derivation of multivariate BeveridgeNelson permanent and transitory components, whereby the latter can be derived explicitly as a weighting of observable stationary processes. This allows far clearer economic interpretations. Different assumptions on the fundamental stationary processes result in distinctly different results; but this reflects deep economic uncertainty. We illustrate with an example using Garratt et al's (2003a) small VECM model of the UK economy. Any nonstationary series can be decomposed into permanent (or "trend") and transitory (or "cycle") components. Typically some atheoretic prefiltering procedure is applied to extract the permanent component. This paper argues that analysis of the fundamental underlying stationary economic processes should instead be central to this process. We present a new derivation of multivariate BeveridgeNelson permanent and transitory components, whereby the latter can be derived explicitly as a weighting of observable stationary processes. This allows far clearer economic interpretations. Different assumptions on the fundamental stationary processes result in distinctly different results; but this reflects deep economic uncertainty. We illustrate with an example using Garratt et al's (2003a) small VECM model of the UK economy. 
Keywords:  Multivariate BeveridgeNelson, VECM, Economic Fundamentals, Decomposition. 
JEL:  C1 C32 E0 E32 E37 
Date:  2005–02 
URL:  http://d.repec.org/n?u=RePEc:bbk:bbkefp:0501&r=ets 
By:  Anthony Garratt (School of Economics, Mathematics & Statistics, Birkbeck College); Shaun P Vahey 
Abstract:  We characterise the relationships between preliminary and subsequent measurements for 16 commonlyused UK macroeconomic indicators drawn from two existing realtime data sets and a new nominal variable database. Most preliminary measurements are biased predictors of subsequent measurements, with some revision series affected by multiple structural breaks. To illustrate how these findings facilitate realtime forecasting, we use a vector autoregression to generate realtime onestepahead probability event forecasts for 1990Q1 to 1999Q2. Ignoring the predictability in initial measurements understates considerably the probability of above trend output growth. 
Keywords:  realtime data, structural breaks, probability event forecasts 
JEL:  C22 C82 E00 
Date:  2005–02 
URL:  http://d.repec.org/n?u=RePEc:bbk:bbkefp:0502&r=ets 
By:  JeanMarie Dufour; Abdeljelil Farhat; Marc Hallin 
Abstract:  We consider the problem of testing whether the observations X1, · · ·, Xn of a time series are independent with unspecified (possibly nonidentical) distributions symmetric about a common known median. Various bounds on the distributions of serial correlation coefficients are proposed: exponential bounds, Eatontype bounds, Chebyshev bounds and BerryEsséenZolotarev bounds. The bounds are exact in finite samples, distributionfree and easy to compute. The performance of the bounds is evaluated and compared with traditional serial dependence tests in a simulation experiment. The procedures proposed are applied to U.S. data on interest rates (commercial paper rate). <P>Nous étudions le problème qui consiste à tester l’hypothèse que des observations X1, · · ·, Xn d’une série chronologique sont indépendantes avec des distributions non spécifiées (possiblement non identiques) symétriques autour d’une médiane connue. Nous proposons plusieurs bornes sur les distributions des coefficients d’autocorrélation : bornes exponentielles, bornes de type Eaton, bornes de Chebyshev et bornes de BerryEsséenZolotarev. Les bornes sont exactes dans les échantillons finis, non paramétriques et faciles à calculer. Nous évaluons par simulation la performance des bornes et comparons celleci à celle de tests d’autocorrélation traditionnels. Les procédures proposées sont appliquées à des données de taux d’intérêt américaines (“commercial paper rate”). 
Keywords:  autocorrelation; serial dependence; nonparametric test; distributionfree test; heterogeneity; heteroskedasticity; symmetric distribution; robustness; exact test; bound; exponential bound; large deviations; Chebyshev inequality; BerryEsséen; interest rates, autocorrelation; serial dependence; nonparametric test; distributionfree test; heterogeneity; heteroskedasticity; symmetric distribution; robustness; exact test; bound; exponential bound; large deviations; Chebyshev inequality; BerryEsséen; interest rates 
JEL:  C14 C22 C12 C32 E4 
Date:  2005–02–01 
URL:  http://d.repec.org/n?u=RePEc:cir:cirwor:2005s04&r=ets 
By:  A.H.J. den Reijer 
Abstract:  This paper applies large scale factor models to Dutch quarterly data inorder to generate forecasts of GDP growth rates for an horizon up to 8 quarters ahead. The data set consists of the series underlying the cen tral bank´s macroeconomic structural model for the Netherlands sup plemented with leading indicator variables. In a pseudo outofsample forecasting context, we select optimal models in the time dimension and the optimal size of the ordered data set in the crosssectional dimension. The main empirical ?ndings of this paper are that the crosssectional opti mization substantially improves the forecasting performance of the factor models. However, only the dynamic factor model systematically outper forms and encompasses the autoregressive benchmark model with an op timal subset of the data of around 110 series. The forecasting gains in terms of mean squared errors range from 10% to 30% for forecast horizons up to 6 quarters ahead. 
Keywords:  Factor models; Forecasting; Leading Indicators. 
JEL:  C43 C51 E32 
Date:  2005–02 
URL:  http://d.repec.org/n?u=RePEc:dnb:dnbwpp:028&r=ets 
By:  Hiroaki Chigira 
Date:  2005–01 
URL:  http://d.repec.org/n?u=RePEc:hst:hstdps:d0469&r=ets 
By:  Wiliam Branch (University of Californis  Irvine); George W. Evans (University of Oregon Economics Department) 
Abstract:  We compare the performance of alternative recursive forecasting models. A simple constant gain algorithm, used widely in the learning literature, both forecasts well out of sample and also provides the best fit to the Survey of Professional Forecasters. 
Keywords:  constant gain, recursive learning, expectations 
JEL:  E37 D84 D83 
Date:  2005–02–01 
URL:  http://d.repec.org/n?u=RePEc:ore:uoecwp:20053&r=ets 
By:  Stephen G. Donald (University of Texas at Austin); Natércia Fortuna (CEMPRE, Faculdade de Economia do Porto); Vladas Pipiras (University of North Carolina at Chapel Hill) 
Abstract:  We focus on the problem of rank estimation in an unknown symmetric matrix based on a symmetric, asymptotically normal estimator of the matrix. The related positive definite limit covariance matrix is assumed to be estimated consistently, and to have either a Kronecker product or an arbitrary structure. These assumptions are standard although they also exclude the case when the matrix estimator is positive or negative semidefinite. We adapt and reexamine here some available rank tests, and introduce a new rank test based on the eigenvalues of the matrix estimator. We discuss several applications where rank estimation in symmetric matrices is of interest, and also provide a small simulation study and an application. 
Keywords:  rank, symmetric matrix, eigenvalues, matrix decompositions, estimation, asymptotic normality, consistency 
JEL:  C12 C13 
Date:  2005–02 
URL:  http://d.repec.org/n?u=RePEc:por:fepwps:167&r=ets 
By:  Yoichi Arai (Faculty of Economics, University of Tokyo); Eiji Kurozumi (Department of Economics, Hitotsubashi University) 
Abstract:  In this paper we propose residualbased tests for the null hypothesis of cointegration with structural breaks against the alternative of no cointegration. The Lagrange Multiplier test is proposed and its limiting distribution is obtained for the case in which the timing of a structural break is known. Then the test statistic is extended in two ways to deal with a structural break of unknown timing. The first test statistic, a plugin version of the test statistic for known timing, replaces the true break point by the estimated one. We also propose a second test statistic where the break point is chosen to be most favorable for the null hypothesis. We show the limiting properties of both statistics under the null as well as the alternative. Critical values are calculated for the tests by simulation methods. Finitesample simulations show that the empirical size of the test is close to the nominal one unless the regression error is very persistent and that the test rejects the null when no cointegrating relationship with a structural break is present. 
Date:  2005–02 
URL:  http://d.repec.org/n?u=RePEc:tky:fseres:2005cf319&r=ets 