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非平稳情形下的模型选择AI 解读

This paper studies model selection methods in the presence of nonstationarity. We focus on the Bayesian model selection rule and compare it with other criteria that are frequently used in econometric practice. First, we derive each of these criteria in the presence of nonstationarity. In particular, we study the Bayesian model selection rule in detail and derive three alternative forms of it in the presence of nonstationarity. One important feature of the Bayesian model…

短时段内协整关系失效的检验AI 解读

This article introduces tests for cointegration breakdown that may occur over a relatively short time period, such as at the end of the sample. The breakdown may be due to a shift in the cointegrating vector or due to a shift in the errors from being I(0) to being I(1). Tests are introduced based on the postbreakdown sum of squared residuals and sum of squared reverse partial sums of residuals. Critical values are provided using a subsampling method. Asymptotic results take…

分段协整的推断AI 解读

A cointegration relation is often interpreted as a long-run equilibrium relation whereas short-run deviations are allowed to a certain extent. In practice, however, real data often fail to confirm cointegration for a well understood economic relation. This paper proposes that in many cases failure of confirming cointegration is due to nonstationary deviations in a relatively small portion of the data period (“short-run”) whereas a cointegration relation prevails in the other…

非平稳时间序列模型中后验密度的大样本性质、贝叶斯信息准则与似然原则AI 解读

Asymptotic normality of posterior is a well understood result for dynamic as well as non-dynamic models based on sets of abstract conditions that are hard to verify especially for the case of nonstationarity. In this paper the authors provide a set of conditions by which they can relatively easily prove the asymptotic posterior normality under quite general situations of possible nonstationarity. This result reinforces and generalizes the validity of inference based on the…

可能含非平稳过程的时间序列模型中的贝叶斯渐近理论AI 解读

Asymptotic normality of the Bayesian posterior is a well-known result for stationary dynamic models or nondynamic models. This paper extends the analysis to a time series model with a possible nonstationary process. We spell out conditions under which asymptotic normality of the posterior is obtained even if the true data-generation process is a nonstationary process.