Observed-data DIC for spatial panel data models

Dogan O., Yang Y., Taspinar S.

EMPIRICAL ECONOMICS, cilt.64, sa.3, ss.1281-1314, 2023 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 64 Sayı: 3
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s00181-022-02286-6
  • Dergi Adı: EMPIRICAL ECONOMICS
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, IBZ Online, International Bibliography of Social Sciences, ABI/INFORM, Business Source Elite, Business Source Premier, EconLit, Geobase, Public Affairs Index
  • Sayfa Sayıları: ss.1281-1314
  • Anahtar Kelimeler: Spatial panel data models, Bayesian inference, MCMC, Deviance information criterion, DIC, Bayesian model comparison, Model selection, DEVIANCE INFORMATION CRITERION, DYNAMIC-MODELS, TIME-SERIES, LM TESTS
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

In spatial panel data modeling, researchers often need to choose a spatial weights matrix from a pool of candidates, and decide between static and dynamic specifications. We propose observed-data deviance information criteria to resolve these specification problems in a Bayesian setting. The presence of high dimensional latent variables (i.e., the individual and time fixed effects) in spatial panel data models invalidates the use of a deviance information criterion (DIC) formulated with the conditional and the complete-data likelihood functions of spatial panel data models. We first show how to analytically integrate out these latent variables from the complete-data likelihood functions to obtain integrated likelihood functions. We then use the integrated likelihood functions to formulate observed-data DIC measures for both static and dynamic spatial panel data models. Our simulation analysis indicates that the observed-data DIC measures perform satisfactorily to resolve specification problems in spatial panel data modeling. We also illustrate the usefulness of the proposed observed-data DIC measures using an application from the literature on spatial modeling of the house price changes in the US.