Inference in dynamic stochastic general equilibrium models with possible weak identification
可能弱识别的动态随机一般均衡模型推断AI 解读 ↓
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可能弱识别的动态随机一般均衡模型推断
- 研究问题
- 研究对数线性化DSGE模型中弱识别乃至未识别参数的有效推断，并考察只使用部分频谱时能否获得信息性置信集。
- 方法与思路
- 作者基于频域高斯似然近似构造得分检验，以复数Gauss–Newton回归表达统计量，并反演检验和构造响应置信带。
- 材料陈述的贡献
- 摘要称反演检验可得渐近有效置信集，并提供冲击响应等对象的统一置信带；模拟提示小样本水平较适当且区间可有信息。
- 阅读关注点
- 应核对频域弱识别定义、频段选择与似然近似条件，以及卡方临界值、统一置信带和未识别参数下信息性的依据。
- 阅读边界
- 仅据所给OpenAlex版本摘要，不能判断全部正则条件或模拟表现的普遍性；具体数值未给出，不推断刊本变化。
原文摘要
This paper considers inference in log-linearized dynamic stochastic general equilibrium (DSGE) models with weakly (including un-) identified parameters. The framework allows for analysis using only part of the spectrum, say at the business cycle frequencies. First, we characterize weak identification from a frequency domain perspective and propose a score test for the structural parameter vector based on the frequency domain approximation to the Gaussian likelihood. The construction heavily exploits the structures of the DSGE solution, the score function, and the information matrix. In particular, we show that the test statistic can be represented as the explained sum of squares from a complex-valued Gauss–Newton regression, where weak identification surfaces as (imperfect) multicollinearity. Second, we prove that asymptotically valid confidence sets can be obtained by inverting this test statistic and using chi-squared critical values. Third, we provide procedures to construct uniform confidence bands for the impulse response function, the time path of the variance decomposition, the individual spectrum, and the absolute coherency. Finally, a simulation experiment suggests that the test has adequate size even with relatively small sample sizes. It also suggests it is possible to have informative confidence sets in DSGE models with unidentified parameters, particularly regarding the impulse response functions. Although the paper focuses on DSGE models, the methods are applicable to other dynamic models with well defined spectra, such as stationary (factor-augmented) vector autoregressions.