Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago*

Süleyman Taşpınar*, Osman DoĞan, Jiyoung Chae, Anil K. Bera

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

5 Atıf (Scopus)

Özet

In this study, we propose a spatial stochastic volatility model in which the latent log-volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the log-volatility terms introduces spatial dependence in the outcome equation. To introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation algorithm, we transform the model so that the outcome equation takes the form of log-squared terms. We approximate the distribution of the log-squared error terms of the outcome equation with a finite mixture of normal distributions so that the transformed model turns into a linear Gaussian state-space model. Our simulation results indicate that the Bayesian estimator has satisfactory finite sample properties. We investigate the practical usefulness of our proposed model and estimation method by using the price returns of residential properties in the broader Chicago Metropolitan area.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1243-1272
Sayfa sayısı30
DergiOxford Bulletin of Economics and Statistics
Hacim83
Basın numarası5
DOI'lar
Yayın durumuYayınlandı - Eki 2021
Harici olarak yayınlandıEvet

Bibliyografik not

Publisher Copyright:
© 2021 The Department of Economics, University of Oxford and John Wiley & Sons Ltd

Parmak izi

Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago*' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

Alıntı Yap