普通最小二乘法
Hedonic回归
计量经济学
贝叶斯概率
回归
贝叶斯推理
经济
估计
样品(材料)
回归分析
数学
计算机科学
统计
化学
管理
色谱法
作者
Urbi Garay,Gavino Puggioni,Germán Molina,Enrique ter Horst
标识
DOI:10.1016/j.jbusres.2022.06.055
摘要
Traditional ordinary least squares (OLS) regressions applied to hedonic pricing models assume that, when using time series, the estimated coefficients for each of the attributes remain constant. We propose a Bayesian dynamic estimation of the hedonic regression model in which the estimated coefficients can be time-varying, demonstrated with an application of art prices. Our dynamic linear regression model overcomes the problems associated with traditional rolling-window based OLS (which represent ad hoc approximations to dynamic estimation), such as under or over-estimation of parameter values and non-adaptive window sizes to account for time-variability. Using a sample of 27,124 paintings sold at auction from 63 Pop-artists (2001–2013), we demonstrate that the estimated coefficients associated with commonly used art attributes fluctuate noticeably through time, and that certain types of artworks and artists might be regarded as “safer” investments (as their art experiences smaller maximum drawdowns), based on price dynamics during the financial crisis (2008–09).
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