統計学輪講（第３０回）

日時    2006年11月14日(火)    15時〜16時40分
場所    経済学部新棟３階第３教室
講演者  John Geweke (University of Iowa)
演題    Smoothly Mixing Regressions

概要：
This paper extends the conventional Bayesian mixture of normals model by 
permitting state probabilities to depend on observed covariates. The
dependence is captured by a simple multinomial probit model. A
conventional and rapidly mixing MCMC algorithm provides access to the
posterior distribution at modest computational cost. This model is
competitive with existing econometric models, as documented in the
paper's illustrations. The first illustration studies quantiles of the
distribution of earnings of men conditional on age and education, and
shows that smoothly mixing regressions are an attractive alternative to
non-Baeysian quantile regression. The second illustration models serial
dependence in the S&P 500 return, and shows that the model compares
favorably with ARCH models using out of sample likelihood criteria.

論文は以下のサイトからダウンロード可能です．
URL:http://www.biz.uiowa.edu/faculty/jgeweke/papers/SMR/ms.pdf

統計学輪講のスケジュールに戻る．

Tokyo University