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

It is often believed that the consumer sentiment index has predictive power for future consumption levels. While Granger causality tests have already been used to test for this, no attempt has been made yet to quantify the predictive power of the consumer sentiment index over different time horizons. In this paper, we decompose the Granger causality at different time lags, by looking at a sequence of nested prediction models. Since the consumer sentiment index turns out to be cointegrated with real consumption, we resort to Error Correcting Models. Four consumption series are studied, namely total real consumption, real consumption of durables, nondurables and services. Among other findings, we show that the consumer sentiment index Granger causes future consumption with an average time lag of four to five months. Furthermore, it is found that the consumer sentiment index has more incremental predictive power for consumption of services than for consumption of durables or nondurables, and that the index is not only useful as a predictor at the very short term, but keeps predictive power at larger time horizons.Advantages; Calibration; Cointegration; Consumer sentiment; Consumption; Data; Estimator; Functions; Granger causality; Indexes; Least-squares; M-estimators; Methods; Model; Models; Optimal; Order; Outliers; Partial least squares; Precision; Prediction; Regression; Research; Robust estimation; Robust regression; Robustness; Squares; Tests; Time; Time series; Variables; VEC models; Yield;

Bagging and boosting classification trees to predict churn.

In this paper, bagging and boosting techniques are proposed as performing tools for churn prediction. These methods consist of sequentially applying a classification algorithm to resampled or reweigthed versions of the data set. We apply these algorithms on a customer database of an anonymous U.S. wireless telecom company. Bagging is easy to put in practice and, as well as boosting, leads to a significant increase of the classification performance when applied to the customer database. Furthermore, we compare bagged and boosted classifiers computed, respectively, from a balanced versus a proportional sample to predict a rare event (here, churn), and propose a simple correction method for classifiers constructed from balanced training samples.Algorithms; Bagging; Boosting; Churn; Classification; Classifiers; Companies; Data; Gini coefficient; Methods; Performance; Rare events; Sampling; Top decile; Training;

Bagging and boosting classification trees to predict churn.

Bagging; Boosting; Classification; Churn;

Advanced classification and time-series methods in marketing..

This collection of essays investigates diverse marketing issues ranging from customer retention to the elaboration of pan-European strategies, t hrough the use of advanced modeling techniques. This work follows two ma in streams of research, from the methodological viewpoint, but also with respect to the issues envisioned. As far as the methodology is concerne d, the first part concerns the development and the application of novel binary choice models that conceptually originated in the statistical mac hine learning literature. In turn, the second part is based on time-seri es analysis. In particular, we devote a special attention to the concept of Granger causality in the time and frequency domains. In addition to this unidirectional concept, we also use a measure of co-movement betwee n multiple time-series. Both parts also differ with respect to the issues envisioned. The main i nterest in the first part is customer retention and churn prediction. In the second part, we focus on the understanding of the similarities and divergences, as well as the mechanisms of interaction between the Europe an countries. In particular, we assess the predictive content of Busines s Tendency Surveys published by the European Commission, allowing for cr oss-country effects. In a second stage, we decompose this predictive con tent over the frequency band. Finally, we investigate to what extent the European countries exhibit homogeneous consumer confidence for various planning horizons and geographic regions. ------------------------------------------------------------------------ --------------------------

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

Consumer sentiment; Time;

Decomposing Granger causality over the spectrum.

We develop a bivariate spectral Granger-causality test that can be applied at each individual frequency of the spectrum. The spectral approach to Granger causality has the distinct advantage that it allows to disentangle (potentially) different Granger-causality relationships over different time horizons. We illustrate the usefulness of the proposed approach in the context of the predictive value of European production expectation surveys.Business surveys; Frequency; Granger causality; Production expectations; Spectral analysis; Surveys; Time; Value;

On the predictive content of production surveys: A pan-European study.

For over 40 years, Business Tendency Surveys have been collected in multiple member states of the European Union. Previous research has studied the predictive content of the expectation variables included in those surveys through bivariate, within-country, Granger-causality tests. These tests have resulted in mixed conclusions. We extend previous research in various ways, as we (i) explicitly allow for cross-country influences, and (ii) do so using both bivariate and multivariate Granger-causality tests. Specifically, the multivariate El Himdi-Roy (HR) test is adapted to jointly test the forecasting value of multiple production expectation series, to assess whether part of this joint effect is indeed due to cross-country influences, and to determine which countries' expectation series have the most "clout" in predicting the production levels in the other member countries, or have the highest receptivity, in that their production levels are Granger caused by the other countries' expectations. (c) 2004 Published by Elsevier B.V. on behalf of International Institute of Forecasters.business surveys; cross-correlations; production expectations; granger causality; business survey series; time-series; expectations; indicators; diffusion;

Trimmed bagging.

Bagging has been found to be successful in increasing the predictive performance of unstable classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then averages overal lobtained classification rules. The idea of trimmed bagging is to exclude the bootstrapped classification rules that yield the highest error rates, as estimated by the out-of-bag error rate, and to aggregate over the remaining ones. In this note we explore the potential benefits of trimmed bagging. On the basis of numerical experiments, we conclude that trimmed bagging performs comparably to standard bagging when applied to unstable classifiers as decision trees, but yields better results when applied to more stable base classifiers, like support vector machines.Bagging;

The European consumer: United in diversity?.

The ongoing unification which takes place on the European political scene, along with recent advances in consumer mobility and communication technology, raises the question whether the European Union can be treated as a single market to fully exploit the potential synergy effects from pan-European marketing strategies. Previous research, which mostly used domain-specific segmentation bases, has resulted in mixed conclusions. In this paper, a more general segmentation base is adopted, as we consider the homogeneity in the European countries' Consumer Confidence Indicators. Moreover, rather than analyzing more traditional static similarity measures, we adopt the concepts of dynamic correlation and cohesion between countries. The short-run fluctuations in consumer confidence are found to be largely country specific. However, a myopic focus on these fluctuations may inspire management to adopt multi-country strategies, foregoing the potential longer-run benefits from more standardized marketing strategies. Indeed, the Consumer Confidence Indicators become much more homogeneous as the planning horizon is extended. However, this homogeneity is found to remain inversely related to the cultural, economic and geographic distances among the various Member States. Hence, pan-regional rather pan-European strategies are called for.Communication; Consumer confidence; Country; Dynamic correlation; Effects; European unification; European Union; Indicators; Management; Market; Marketing; Planning; Research; Similarity; Strategy; Technology;

Trimmed bagging.

Bagging has been found to be successful in increasing the predictive performance of unstable classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then averages over all obtained classification rules. The idea of trimmed bagging is to exclude the bootstrapped classification rules that yield the highest error rates, as estimated by the out-of-bag error rate, and to aggregate over the remaining ones. In this note we explore the potential benefits of trimmed bagging. On the basis of numerical experiments, we conclude that trimmed bagging performs comparably to standard bagging when applied to unstable classifiers as decision trees, but yields better results when applied to more stable base classifiers, like support vector machines.