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英文文献：Characterizing economic trends by Bayesian stochastic model specification search-利用贝叶斯随机模型规范搜索来表征经济趋势
英文文献作者：Stefano Grassi,Tommaso Proietti
英文文献摘要：
We extend a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. In particular, we focus on autoregressive models with possibly time-varying intercept and slope and decide on whether their parameters are fixed or evolutive. Stochastic model specification is carried out to discriminate two alternative hypotheses concerning the generation of trends: the trend-stationary hypothesis, on the one hand, for which the trend is a deterministic function of time and the short run dynamics are represented by a stationary autoregressive process; the difference-stationary hypothesis, on the other, according to which the trend results from the cumulation of the effects of random disturbances. We illustrate the methodology for a set of U.S. macroeconomic time series, which includes the traditional Nelson and Plosser dataset. The broad conclusion is that most series are better represented by autoregressive models with time-invariant intercept and slope and coefficients that are close to boundary of the stationarity region. The posterior distribution of the autoregressive parameters, estimated by a suitable Gibbs sampling scheme, provides useful insight on quasi-integrated nature of the specifications selected.
我们扩展了最近提出的贝叶斯模型选择技术，称为随机模型规范搜索，以表征宏观经济时间序列趋势的性质。特别是，我们关注可能随时间变化的截距和斜率的自回归模型，并确定其参数是固定的还是进化的。采用随机模型规范区分趋势产生的两个备选假设:趋势平稳假设，一方面趋势是时间的确定性函数，短期动态是平稳的自回归过程;另一种是差分平稳假设，根据这种假设，这种趋势是随机扰动作用的累积结果。我们说明了一组美国宏观经济时间序列的方法，其中包括传统的Nelson和Plosser数据集。一般的结论是，采用截距、斜率和系数接近平稳性区域边界的自回归模型可以更好地代表大多数序列。自回归参数的后验分布，由一个合适的吉布斯抽样方案估计，提供了有用的洞察力的准综合性质的规格选择。
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