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We provide new evidence on the comparison between the stock and housing wealth effects on consumption. Using a panel VAR approach applied to OECD data, we find evidence that the stock market wealth effect is generally the larger. However, with regard to the evolution of asset wealth effects over time, our findings show that the housing wealth effect has outweighed the share market wealth effect in the last decade. We further find that asset wealth has asymmetric effects on consumption, with stronger and more persistent effects from positive asset wealth shocks. Our results have important monetary policy implications for both stock and real estate markets, and offer timely insights into the desirability of current proposals to reduce house price volatility, such as through macro prudential regulations.

Purpose – This paper investigates whether mean reversion holds for a panel of 16 OECD stock price indices for the period 1970 to 2011. Design/methodology/approach – We employ seemingly unrelated regression (SUR)-based linear and non-linear unit root tests which are not only able to exploit the power of panel data analysis but also account for cross sectional dependencies as well as identify which panel members are stationary. Findings – In contrast to a literature that offers mixed findings on stationarity, it was found that most of our sample is characterized as mean- or trend-reverting with approximated half-lives in the region of three to five years. Originality/value – In contrast to other panel unit root tests of stock prices, the authors identify which individual panel members are stationary and non-stationary using a SURADF test. A further novelty of our approach is that we also develop a SUR-based panel KSS test that allows us to explore the possibility that stock prices exhibit non-linear stationarity.

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As the multi-daily electricity consumption behaviors have the strong characteristics of dynamicity, nonlinearity and locality caused by temporal manifold structure, the existing methods are difficult to fine-grained and accurately classify it. To solve this problem, this paper proposes a hierarchical classification method based on the temporal extension of the neighborhood preserving embedding algorithm (TNPE) and Bayes. The input data are multi daily-load curves of a single consumer, including power-hour-day three dimensions, which contains the full information of the user’s consumption behaviors not only in hours, but also in days. Firstly, electricity consumption behaviors are divided into routine and non-routine types by k-means clustering algorithm. Secondly, the load feature mapping matrix of different industries is extracted through the TNPE, and each TNPE model can regard as one binary classifier, so the multi-classifier is constructed through multiple TNPE models. Finally, by converting the feature similarity between samples into probabilities, a Bayesian model is established to realize which the power consumption type belongs to. The case results show that this method can effectively recognize the local dynamic features in the temporal load data, and obtain a higher classification accuracy through a smaller number of training samples.