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Forecasting US Real House Price Returns over 1831-2013: Evidence from Copula Models

Author

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  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Anandamayee Majumdar

    (Center of Advanced Statistics and Econometrics, Soochow University, China)

Abstract

Given the existence of non-normality and nonlinearity in the data generating process of real house price returns over the period of 1831-2013, this paper compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1859-2013, based on an in-sample of 1831-1858. Overall, our results provide overwhelming evidence in favor of the copula models (Normal, Student’s t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student’s t copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for non-normality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data.

Suggested Citation

  • Rangan Gupta & Anandamayee Majumdar, 2014. "Forecasting US Real House Price Returns over 1831-2013: Evidence from Copula Models," Working Papers 201444, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201444
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    Cited by:

    1. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.
    2. Huthaifa Alqaralleh & Gazi Salah Uddin & Canepa, Alessandra, 2022. "Time-frequency connectedness across housing markets, stock market and uncertainty: A Wavelet-Time Varying Parameter Vector Autoregression," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202204, University of Turin.
    3. Alqaralleh, Huthaifa & Canepa, Alessandra & Salah Uddin, Gazi, 2023. "Dynamic relations between housing Markets, stock Markets, and uncertainty in global Cities: A Time-Frequency approach," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    4. Kang, Sang Hoon & Uddin, Gazi Salah & Ahmed, Ali & Yoon, Seong-Min, 2018. "Multi-scale causality and extreme tail inter-dependence among housing prices," Economic Modelling, Elsevier, vol. 70(C), pages 301-309.
    5. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 667-698, September.
    6. Luis A. Gil-Alana & Rangan Gupta & Fernando Perez de Gracia, 2016. "Persistence, mean reversion and non-linearities in the US housing prices over 1830--2013," Applied Economics, Taylor & Francis Journals, vol. 48(34), pages 3244-3252, July.
    7. Sinha, Ankur & Kedas, Satishwar & Kumar, Rishu & Malo, Pekka, 2019. "Buy, Sell or Hold: Entity-Aware Classification of Business News," IIMA Working Papers WP 2019-04-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    8. Sun, Tianyu & Chand, Satish & Sharpe, Keiran, 2018. "Effect of Aging on Urban Land Prices in China," MPRA Paper 89237, University Library of Munich, Germany.

    More about this item

    Keywords

    House Price; Copula Models; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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