A Bayesian Time Series Model of Multiple Structural Changes in Level, Trend, and Variance
提出一个确定性趋势动态时间序列模型，显式建模水平、趋势和误差方差的多重结构变化，使用吉布斯采样估计，并通过边际似然等准则选择最优模型，应用于美国实际利率和实际GDP数据。
We consider a deterministically trending dynamic time series model in which multiple structural changes in level, trend, and error variance are modeled explicitly and the number, but not the timing, of the changes is known. Estimation of the model is made possible by the use of the Gibbs sampler. The determination of the number of structural breaks and the form of structural change is considered as a problem of model selection, and we compare the use of marginal likelihoods, posterior odds ratios, and Schwarz's Bayesian model-selection criterion to select the most appropriate model from the data. We evaluate the efficacy of the Bayesian approach using a small Monte Carlo experiment. As empirical examples, we investigate structural changes in the U.S. ex post real interest rate and in a long time series of U.S. real gross domestic product.