Denoised Least Squares Forecasting of GDP Changes Using Indexes of Consumer and Business Sentiment

Indexes of consumer and business sentiment are frequently characterized by measurement errors and short-term cyclical fluctuations that can distort their predictive accuracy for GDP changes. While measurement errors arise due to the survey sampling procedures that characterize these surveys, short-term cyclical fluctuations are generally linked with various exogenous and irregular factors that are not necessarily related to the economy. This paper shows, using data on the US economy, that applying wavelet denoising on indexes of consumer and business sentiment in the context of the linear regression model can overcome these limitations and can provide: (a) efficient coefficient estimates in models that explain consumer sentiment index variation; and (b) consistent coefficient estimates and predictions in models for GDP changes when using consumer and business sentiment indexes as predictors.Consumer sentiment index, denoised least squares, index of homebuilders’sentiment, index of manufacturing activity, measurement errors.

Case study:shipping trend estimation and prediction via multiscale variance stabilisation

<p>Shipping and shipping services are a key industry of great importance to the economy of Cyprus and the wider European Union. Assessment, management and future steering of the industry, and its associated economy, is carried out by a range of organisations and is of direct interest to a number of stakeholders. This article presents an analysis of shipping credit flow data: an important and archetypal series whose analysis is hampered by rapid changes of variance. Our analysis uses the recently developed data-driven Haar–Fisz transformation that enables accurate trend estimation and successful prediction in these kinds of situation. Our trend estimation is augmented by bootstrap confidence bands, new in this context. The good performance of the data-driven Haar–Fisz transform contrasts with the poor performance exhibited by popular and established variance stabilisation alternatives: the Box–Cox, logarithm and square root transformations.</p

Multiscale Partial Correlation Clustering of Stock Market Returns

This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&amp;P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation

Regression Analysis of Marketing Time Series: A Wavelet Approach with Some Frequency Domain Insights

Regression analysis with time series data is frequently used in marketing research. However, despite its popularity and ease of interpretation it cannot provide any information regarding the relations between marketing time series over different frequencies. This article proposes a new research tool, wavelet analysis, that when incorporated in regression analysis can provide some frequency domain insights about the effectiveness of marketing instruments over different cycles. In addition, by adopting appropriate regression-modeling techniques, wavelets can provide increased estimation and prediction accuracy of marketing causal effects.marketing time series, regression analysis, wavelets, prediction