Forecasting Sales and Price for Existing Single-Family Homes: A VAR Model with Error Correction

In this paper we forecast demand for existing single-family housing in the United States. We first find that sales volume (sales) and median sales price (price) have unit roots. We then find that sales and price are cointegrated. We develop a vector autoregressive (VAR) model with error correction to further examine the causality between sales and price. We find that there exists a bidirectional causality relationship between sales and price. Price affects sales significantly and sales affects price weakly. With the VAR model we then forecast sales and price for existing single-family housing during the period 1991 to 1994 by using a recursive method. We find that our predictions for sales and price fit the actual data well.

Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation

We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by non-stationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in sleep EEG data is used to illustrate the usefulness of the proposed methodology. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional non-stationary time series without variance lower bounds are established, which could be of independent interest

Locating influential nodes via dynamics-sensitive centrality

With great theoretical and practical significance, locating influential nodes of complex networks is a promising issues. In this paper, we propose a dynamics-sensitive (DS) centrality that integrates topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is much more accurate than degree, $k$-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure