Abstract
Wavelet coherence of time series provides valuable information about dynamic correlation and its impact on time scales. Here, the authors analyze the wavelet coherence of major real estate markets data, and take the USA, Hong Kong of China, Canada, Japan, and Developed Europe real estate market prices as time series. The wavelet coherence results show relationships among these markets, the correlations between the two and three markets (by multiple wavelet coherence) and how these relationships vary in the time-frequency space. These relationships allow the authors to build VARMA models of real estate data which produce forecasts with small errors.
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