A Decision-Theoretic Analysis of the Unit-Root Hypothesis Using Mixtures of Elliptical Models
提出一种基于决策理论的单位根检验方法，通过预测方差定义损失函数，并扩展似然函数以允许非正态性、结构突变和移动平均误差，实证发现后验概率支持趋势平稳但决策分析常选单位根模型。
Abstract This article develops a formal decision-theoretic approach to testing for a unit root in economic time series. The approach is empirically implemented by specifying a loss function based on predictive variances; models are chosen so as to minimize expected loss. In addition, the article broadens the class of likelihood functions traditionally considered in the Bayesian unit-root literature by (a) allowing for departures from normality via the specification of a likelihood based on general elliptical densities, (b) allowing for structural breaks to occur, (c) allowing for moving average errors, and (d) using mixtures of various submodels to create a very flexible overall likelihood. Empirical results indicate that, although the posterior probability of trend stationarity is quite high for most of the series considered, the unit-root model is often selected in the decision-theoretic analysis. KEY WORDS: BayesianLoss functionMonte Carlo integrationPrediction