The predictive power of the European Economic Sentiment Indicator.

Economic sentiment surveys are carried out by all European Union member states on a monthly basis. The survey outcomes are used to obtain early insight into future economic evolutions and often receive extensive press coverage. Based on these surveys, the European Commission constructs an aggregate European Economic Sentiment Indicator (ESI). This paper compares the ESI with more sophisticated aggregation schemes based on two statistical methods: dynamic factor analysis and partial least squares. We compare the aggregate sentiment indicators and the weights used in their construction. Afterwards a comparison of their forecast performance for two real economic series, industrial production growth and unemployment, follows. Our findings are twofold. First it is found that the ESI, although constructed in a rather ad hoc way, can compete with the indicators constructed according to statistical principles. Secondly, the predictive power of the sentiment indicators, as tested for in an out-of sample Granger causality framework, is limited.Common indicators; Dimension reduction methods; Economic sentiment indicator; Forecasting;

Multivariate out-of-sample tests for Granger causality.

A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in the univariate setting, much less is known for the multivariate case. In this paper we propose multivariate out-of-sample tests for Granger causality. The performance of the out-of-sample tests is measured by a simulation study and graphically represented by Size-Power plots. It emerges that the multivariate regression test is the most powerful among the considered possibilities. As a real data application, we investigate whether the consumer confidence index Granger causes retail sales in Germany, France, the Netherlands and Belgium.Consumer Sentiment, Granger Causality, Multivariate Time Series,Out-of-sample TestsBelgium; Consumer confidence; Consumer Confidence Index; Consumer sentiment; Data; Forecasting; Germany; Granger causality; Indexes; Multivariate time series; Out-of-sample tests; Performance; Power; Regression; Research; Sales; Simulation; Studies; Tests; Time; Time series;

Multivariate out-of-sample tests for Granger causality.

A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in the univariate setting, much less is known for the multivariate case. Multivariate out-of-sample tests for Granger causality are proposed and their performance is measured by a simulation study. The results are graphically represented by size-power plots. It emerges that the multivariate regression test is the most powerful among the considered possibilities. As a real data application, it is investigated whether the consumer confidence index Granger causes retail sales in Germany, France, the Netherlands and Belgium. (c) 2006 Elsevier B.V. All rights reserved.consumer sentiments; granger causality; multivariate time series; out-of-sample tests; hypothesis tests; forecast; consumption; accuracy;

Least angle regression for time series forecasting with many predictors.

Least Angle Regression(LARS)is a variable selection method with proven performance for cross-sectional data. In this paper, it is extended to time series forecasting with many predictors. The new method builds parsimonious forecast models,taking the time series dynamics into account. It is a exible method that allows for ranking the different predictors according to their predictive content. The time series LARS shows good forecast performance, as illustrated in a simulation study and two real data applications, where it is compared with the standard LARS algorithm and forecasting using diffusion indices.macro-econometrics; model selection; penalized regression; variable ranking;

Consumer sentiment and consumer spending: decomposing the granger causal relationship in the time domain.

Consumer sentiment; Time;

Robust exponential smoothing of multivariate time series.

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.Data cleaning; Exponential smoothing; Forecasting; Multivariate time series; Robustness;

Sparse least trimmed squares regression.

Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data. This paper combines robust regression and sparse model estimation. A robust and sparse estimator is introduced by adding an L1 penalty on the coefficient estimates to the well known least trimmed squares (LTS) estimator. The breakdown point of this sparse LTS estimator is derived, and a fast algorithm for its computation is proposed. Both the simulation study and the real data example show that the LTS has better prediction performance than its competitors in the presence of leverage points.Breakdown point; Outliers; Penalized regression; Robust regression; Trimming;

Robust online scale estimation in time series: A regression-free approach.

This paper presents variance extraction procedures for univariate time series. The volatility of a times series is monitored allowing for non-linearities, jumps and outliers in the level. The volatility is measured using the height of triangles formed by consecutive observations of the time series. This idea was proposed by Rousseeuw and Hubert (1996, Regression-free and robust estimation of scale for bivariate data, Computational Statistics and Data Analysis, 21, 67{85) in the bivariate setting. This paper extends their procedure to apply for online scale estimation in time series analysis. The statistical properties of the new methods are derived and nite sample properties are given. A nancial and a medical application illustrate the use of the procedures.Breakdown point; Inuence function; Online monitoring; Outliers; Robust scale estimation;

Robust online scale estimation in time series : regression-free approach

This paper presents variance extraction procedures for univariate time series. The volatility of a times series is monitored allowing for non-linearities, jumps and outliers in the level. The volatility is measured using the height of triangles formed by consecutive observations of the time series. This idea was proposed by Rousseeuw and Hubert (1996, Regression-free and robust estimation of scale for bivariate data, Computational Statistics and Data Analysis, 21, 67-85) in the bivariate setting. This paper extends their procedure to apply for online scale estimation in time series analysis. The statistical properties of the new methods are derived and finite sample properties are given. A financial and a medical application illustrate the use of the procedures. - breakdown point ; influence function ; online monitoring ; outliers ; robust scale estimation --

Robust online scale estimation in time series

This paper presents variance extraction procedures for univariate time series. The volatility of a times series is monitored allowing for non-linearities, jumps and outliers in the level. The volatility is measured using the height of triangles formed by consecutive observations of the time series. This idea was proposed by Rousseeuw and Hubert (1996, Regression-free and robust estimation of scale for bivariate data, Computational Statistics and Data Analysis, 21, 67{85) in the bivariate setting. This paper extends their procedure to apply for online scale estimation in time series analysis. The statistical properties of the new methods are derived and finite sample properties are given. A financial and a medical application illustrate the use of the procedures