Paper
17 May 2022 Investigating the co-relationship between housing price and inflation by assembling wavelet based time-frequency analysis
Jiamin Wang, Henglang Xie
Author Affiliations +
Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122591D (2022) https://doi.org/10.1117/12.2638824
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China
Abstract
The purpose of this paper is to use the wavelet transform context structures based on Granger causality analysis to investigate the correlation between housing prices and inflations in first-tier cities, namely Beijing, Shanghai, Guangzhou and Shenzhen. Wavelet analysis can be used for filtering, noise removal, multi-resolution time-frequency analysis, and other functions, especially for analyzing non-stationary signals. Results indicate that the relationship is generally positive but changes over time, displaying low to high-frequency cycles. Moreover, most of the situations that show a correlation between housing prices and inflations are that housing prices led inflations, which illustrate that housing prices are crucial to the economy.
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Jiamin Wang and Henglang Xie "Investigating the co-relationship between housing price and inflation by assembling wavelet based time-frequency analysis", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122591D (17 May 2022); https://doi.org/10.1117/12.2638824
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KEYWORDS
Wavelets

Time-frequency analysis

Wavelet transforms

Analytical research

Data processing

Electronic filtering

Signal analyzers

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